Journal articles on the topic 'Manufacturing processes Energy conservation Data processing'
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1
Zhang, Chaoyang, Juchen Zhang, Weixi Ji, and Wei Peng. "Data Acquisition Network Configuration and Real-Time Energy Consumption Characteristic Analysis in Intelligent Workshops for Social Manufacturing." Machines 10, no. 10 (October 10, 2022): 923. http://dx.doi.org/10.3390/machines10100923.
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To achieve energy-saving production, one critical step is to calculate and analyze the energy consumption and energy efficiency of machining processes. However, considering the complexity and uncertainty of discrete manufacturing job shops, it is a significant challenge to conduct data acquisition and energy consumption data processing of manufacturing systems. Meanwhile, under the growing trend of personalization, social manufacturing is an emerging technical practice that allows prosumers to build individualized services with their partners, which produces new requirements for energy data processing. Thus, a real-time energy consumption characteristic analysis method in intelligent workshops for social manufacturing is established to realize data processing and energy efficiency evaluation automatically. First, an energy-conservation production architecture for intelligent manufacturing processes is introduced, and the configuration of a data acquisition network is described to create a ubiquitous manufacturing environment. Then, an energy consumption characteristic analysis method is proposed based on the process time window. Finally, a case study of coupling-part manufacturing verifies the feasibility and applicability of the proposed method. This method realizes a combination of social manufacturing and real-time energy characteristic analysis. Meanwhile, the energy consumption characteristics provide a decision basis for the energy-saving control of intelligent manufacturing workshops.
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Pătrașcu, Roxana, Eduard Minciuc, George Darie, Ștefan-Dominic Voronca, and Andreea-Ioana Bădicu. "Energy efficiency solutions for driers used in the glass manufacturing and processing industry." Proceedings of the International Conference on Business Excellence 11, no. 1 (July 1, 2017): 199–208. http://dx.doi.org/10.1515/picbe-2017-0021.
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Abstract Energy conservation is relevant to increasing efficiency in energy projects, by saving energy, by its’ rational use or by switching to other forms of energy. The goal is to secure energy supply on short and long term, while increasing efficiency. These are enforced by evaluating the companies’ energy status, by monitoring and adjusting energy consumption and organising a coherent energy management. The manufacturing process is described, starting from the state and properties of the raw material and ending with the glass drying technological processes involved. Raw materials are selected considering technological and economic criteria. Manufacturing is treated as a two-stage process, consisting of the logistic, preparation aspect of unloading, transporting, storing materials and the manufacturing process itself, by which the glass is sifted, shredded, deferrized and dried. The interest of analyzing the latter is justified by the fact that it has a big impact on the final energy consumption values, hence, in order to improve the general performance, the driers’ energy losses are to be reduced. Technological, energy and management solutions are stated to meet this problem. In the present paper, the emphasis is on the energy perspective of enhancing the overall efficiency. The case study stresses the effects of heat recovery over the efficiency of a glass drier. Audits are conducted, both before and after its’ implementation, to punctually observe the balance between the entering and exiting heat in the drying process. The reduction in fuel consumption and the increase in thermal performance and fuel usage performances reveal the importance of using all available exiting heat from processes. Technical faults, either in exploitation or in management, lead to additional expenses. Improving them is in congruence with the energy conservation concept and is in accordance with the Energy Efficiency Improvement Program for industrial facilities.
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Jones, Lewis C. R., Nicholas Goffin, Jinglei Ouyang, Nazanin Mirhossein, Jiaji Xiong, Yufeng Li, Lin Li, et al. "Laser specific energy consumption: How do laser systems compare to other manufacturing processes?" Journal of Laser Applications 34, no. 4 (November 2022): 042029. http://dx.doi.org/10.2351/7.0000790.
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Laser material interactions are routinely praised for their selective processing and high processing rates. However, this does not guarantee that the total manufacturing system has a low energy intensity compared to conventional manufacturing processes. This paper presents the results of a collaborative UK and China research project to improve the comprehension of the total energy consumption and carbon emissions for laser-based manufacturing. A range of individual laser cutting, welding, and cleaning processes were studied to assess their energy efficiency, including the laser and its ancillary subsystems (e.g., cooling and extraction). The project developed a systematic analysis method, adapted from BS ISO 14955-1:2017, which incorporated time and subsystem level studies to quantify all energy consumption components of a laser system. Previous research has identified that the laser system's most significant contributor to the total energy consumption are the auxiliary or supporting subsystems, not the laser emission. This identified that using only the absorbed radiation to evaluate manufacturing efficiency is misleading. All the processes evaluated followed a negative correlation between processing rate (kg/h) and specific energy consumption (J/kg). The new data conclude that laser processes have a relatively high energy intensity compared to conventional manufacturing alternatives. The results can be used to identify where the most significant improvements to individual laser systems can be made. The comprehensive comparison of processes allows manufacturers to select processes to improve environmental impact.
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Ralls, Alessandro M., Pankaj Kumar, and Pradeep L. Menezes. "Tribological Properties of Additive Manufactured Materials for Energy Applications: A Review." Processes 9, no. 1 (December 25, 2020): 31. http://dx.doi.org/10.3390/pr9010031.
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Recently, additive manufacturing (AM) has gained much traction due to its processing advantages over traditional manufacturing methods. However, there are limited studies which focus on process optimization for surface quality of AM materials, which can dictate mechanical, thermal, and tribological performance. For example, in heat-transfer applications, increased surface quality is advantageous for reducing wear rates of vibrating tubes as well as increasing the heat-transfer rates of contacting systems. Although many post-processing and in situ manufacturing techniques are used in conjunction with AM techniques to improve surface quality, these processes are costly and time-consuming compared to optimized processing techniques. With improved as-built surface quality, particles tend to be better fused, which allows for greater wear resistance from contacting tube surfaces. Additionally, improved surface quality can reduce the entropy and exergy generated from flowing fluids, in turn increasing the thermodynamic efficiency of heat-transferring devices. This review aims to summarize the process-optimizing methods used in AM for metal-based heat exchangers and the importance of as-built surface quality to its performance and long-term energy conservation. The future directions and current challenges of this field will also be covered, with suggestions on how research in this topic can be improved.
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Iten, Muriel, Miguel Oliveira, Diogo Costa, and Jochen Michels. "Water and Energy Efficiency Improvement of Steel Wire Manufacturing by Circuit Modelling and Optimisation." Energies 12, no. 2 (January 11, 2019): 223. http://dx.doi.org/10.3390/en12020223.
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Industrial water circuits (IWC) are frequently neglected as they are auxiliary circuits of industrial processes, leading to a missing awareness of their energy- and water-saving potential. Industrial sectors such as steel, chemicals, paper and food processing are notable in their water-related energy requirements. Improvement of energy efficiency in industrial processes saves resources and reduces manufacturing costs. The paper presents a cooling IWC of a steel wire processing plant in which steel billets are transformed into wire. The circuit was built in object-oriented language in OpenModelica and validated with real plant data. Several improvement measures have been identified and an optimisation methodology has been proposed. A techno-economic analysis has been carried out to estimate the energy savings and payback time for the proposed improvement measures. The suggested measures allow energy savings up to 29% in less than 3 years’ payback time and water consumption savings of approximately 7.5%.
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YANG, Heng, Dexin AN, Carmen GAIDAU, Jinwei ZHANG, and Jin ZHOU. "Life cycle assessment of processing for chrome tanned cowhide upper leather." Leather and Footwear Journal 21, no. 2 (June 30, 2021): 75–86. http://dx.doi.org/10.24264/lfj.21.2.1.
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Pollution has become a serious problem in leather industry, however, current method to evaluate its environmental effect usually used data from literature review, those data generated while leather manufacturing were rarely collected and analyzed. Thereby, the aim of this study was to evaluate the environmental effect of manufacturing process of chrome tanned cowhide upper leather by applying the Life Cycle Assessment protocols. Following the guidance of ISO 14010, we first combined data obtained from field study and empirical review; and then these data were input into eFootprint for calculation. Results, including four environmental indicators (global warming potential [GWP], primary energy demand [PED], water utility [WU] and acidification [AP]), show that producing 1 kg of cowhide upper leather releases 7.040 kg of CO2 eq, consumes 106.793 MJ of energy and 89.144 kg of water and emits 0.058 kg of SO2 eq. Sensitivity analysis of inventory data demonstrated that chrome tanning and retanning processes accounted for more than 40% of PED, AP and GWP, whereas the beamhouse was more than 78% of WU. Therefore, we could optimise the tanning process by using alternative materials or technologies in the critical sections to achieve cleaner production and sustainable leather manufacturing.
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Indzere, Zane, Kevin D. Manzano Martinez, Tereza Bezrucko, Zauresh Khabdullina, Ivars Veidenbergs, and Dagnija Blumberga. "Energy Efficiency Improvement in Thawing." Environmental and Climate Technologies 24, no. 2 (September 1, 2020): 221–30. http://dx.doi.org/10.2478/rtuect-2020-0068.
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AbstractThe thawing process within fish processing is one of the most essential steps in manufacturing. Various processes of thawing can be used where efficiency varies between companies depending on such characteristics as energy consumption, the price of resources, etc. The main aim of the research is to increase the efficiency of thawing processes. Firstly, to analyse various thawing methods and to find the most efficient one by using multi-criteria decision making analysis method. Secondly, analysing data of thawing of existing company to find opportunities for improvements, including the change of existing technology. Results showed that the most suitable method for thawing is the air blast method. Case study showed that current thawing technology is outdated, thus suggested improvement would be to replace the current boiler house with a cogeneration plant. A sensitivity analysis for the cogeneration plant has been performed.
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Pupeikis, Darius, Lina Morkūnaitė, Mindaugas Daukšys, Arūnas Aleksandras Navickas, and Svajūnas Abromas. "Possibilities of Using Building Information Model Data in Reinforcement Processing Plant." Journal of Sustainable Architecture and Civil Engineering 28, no. 1 (June 22, 2021): 80–93. http://dx.doi.org/10.5755/j01.sace.28.1.27593.
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While the AEC industry is moving towards digitalization off-site rebar prefabrication became a common practice. Now most companies use a long-established standard order processing method, where the customer submits 2D paper or PDF-based drawings. Subsequently, the manufacturers are obligated to make additional detailing, redrawing, calculations, and preparation of other required information for manufacturing. Thus, in this typical scenario, there is a great repetition of the same tasks, with the obvious loss of time and increased likelihood of human error. However, improvements can be made by the application of advanced digital production workflow and the use of open BIM standards (e.g., IFC, XML, BVBS). Therefore, this paper presents the typical data flow algorithm in contrast to the automated data flow for reinforcement manufacturing. Further, the two approaches are compared and analyzed based on Multi-Criteria Decision Making (MCDM) methods. The results have shown promising prospects for companies willing to automate their data flow processes by the use of 3D drawings and digital data from the BIM model in their plants.
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Cios, K. J., G. Y. Baaklini, and A. Vary. "Soft Computing in Design and Manufacturing of Advanced Materials." Journal of Engineering for Gas Turbines and Power 117, no. 1 (January 1, 1995): 161–65. http://dx.doi.org/10.1115/1.2812766.
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The goal of this paper is to show the potential of fuzzy sets and neural networks, often referred to as soft computing, for aiding in all aspects of manufacturing of advanced materials like ceramics. In design and manufacturing of advanced materials it is desirable to find which of the many processing variables contribute most to the desired properties of the material. There is also interest in real-time quality control of parameters that govern material properties during processing stages. This paper briefly introduces the concepts of fuzzy sets and neural networks and shows how they can be used in the design and manufacturing processes. These two computational methods are alternatives to other methods such as the Taguchi method. The two methods are demonstrated by using data collected at NASA Lewis Research Center. Future research directions are also discussed.
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Freitas, R. S. M., C. S. Stampa, D. C. Lobão, and G. B. Alvarez. "NUMERICAL STUDY CONCERNING THERMAL RESPONSES OF NANOFILMS UNDER THE THERMOMASS THEORY." Revista de Engenharia Térmica 15, no. 1 (June 30, 2016): 57. http://dx.doi.org/10.5380/reterm.v15i1.62148.
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he Thermomass theory is based on the relationship mass-energy of Einstein, i.e., the heat has mass-energy duality, behaving as energy in processes where its conversion occurs in another form of energy, and behaving as mass in heat transfer processes. The mathematical model stablished by the Thermomass model falls within the class of problems called models non-Fourier heat conduction. The present work aims to analyze the thermal responses provided by Thermomass theory of nanofilms submitted to a very fast heating process using two different heat sources (laser pulses). During the process of analysis, the equations are written in conservation law, put into dimensionless form and discretized in the way that a high-order TVD scheme is used on to provide accurate and reliable numerical simulations for obtaining the thermal responses predicted by the Thermomass model. The results show that the Thermomass theory predicted a heterogeneous temperature distribution with elevated temperature peaks. The thermal responses obtained from this model may prevent the thermal damage caused by technologies of the processing and manufacturing of elements based on high-power laser applications.
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Borovkov, Herman, Aitor Garcia de la Yedra, Xabier Zurutuza, Xabier Angulo, Pedro Alvarez, Juan Carlos Pereira, and Fernando Cortes. "In-Line Height Measurement Technique for Directed Energy Deposition Processes." Journal of Manufacturing and Materials Processing 5, no. 3 (August 5, 2021): 85. http://dx.doi.org/10.3390/jmmp5030085.
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Directed energy deposition (DED) is a family of additive manufacturing technologies. With these processes, metal parts are built layer by layer, introducing dynamics that propagate in time and layer-domains, which implies additional complexity and consequently, the resulting part quality is hard to predict. Control of the deposit layer thickness and height is a critical issue since it impacts on geometrical accuracy, process stability, and the overall quality of the product. Therefore, online feedback height control for DED processes with proper sensor strategies is required. This work presents a novel vision-based triangulation technique through an off-axis located CCD camera synchronized with a 640 nm wavelength pulsed illumination laser. Image processing and machine vision techniques allow in-line height measurement right after metal solidification. The linearity and the precision of the proposed setup are validated through off-and in-process trials in the laser metal deposition (LMD) process. Besides, the performance of the developed in-line inspection system has also been tested for the Arc based DED process and compared against experimental weld bead characterization data. In this last case, the system additionally allowed for the measurement of weld bead width and contact angles, which are critical in first runs of multilayer buildups.
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Grewal, Randeep S., and Pamela Banks-Lee. "Development of Thermal Insulation for Textile Wet Processing Machinery Using Needlepunched Nonwoven Fabrics." International Nonwovens Journal os-8, no. 2 (June 1999): 1558925099OS—80. http://dx.doi.org/10.1177/1558925099os-800221.
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In textile manufacturing, many fiber manufacturing, dyeing and finishing processes require temperatures in the range of 100oC to 200oC. A substantial amount of energy is needed to produce the desired temperature, and part of this energy is wasted when heat from the process escapes to the environment. Many of the processes are batch processes requiring frequent reheating and restarting. Most process equipment is constructed from stainless steel, which is a good conductor of heat. In addition to this, because of the cost involved in installation and regular maintenance of insulation, many manufacturers do not insulate their process equipment. The heat and moisture loss to the environment makes the manufacturing facilities environmentally uncomfortable for employees. This reduces their productivity and is a health risk. Due to the energy wasted in the textile wet processing industry, there is a need to develop suitable insulating materials specifically for these applications. For commercial applications, both the cost of the insulating material as well as its effectiveness, ease of installation and durability are important. Needlepunched fabrics have the potential to meet these demands [1]. Since low density needled felts with good heat blocking capacity can be made from durable fibers, they are ideal for heat insulation applications [1,2]. This research focuses on identifying suitable fibers and the manufacturing technology which will yield the desired results. After testing of prepared samples, the data was analyzed to determine the fabric and fiber parameters which influence heat transfer. An economic analysis was also conducted to optimize both cost and effectiveness. The important factors contributing to the transfer of heat through needlepunched nonwoven fabrics were found to be the bulk density of the batt and the surface area of the fibers. Incorporation of low denier fibers (meltblown web) in the needlepunched structure led to a significant decrease in the apparent thermal conductivity of the batt. A cost analysis of this insulation (incorporating the meltblown web) determined the optimum thickness of such an insulation to be 10.1 mm.
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Willenbacher, Martina, Jonas Scholten, and Volker Wohlgemuth. "Machine Learning for Optimization of Energy and Plastic Consumption in the Production of Thermoplastic Parts in SME." Sustainability 13, no. 12 (June 16, 2021): 6800. http://dx.doi.org/10.3390/su13126800.
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In manufacturing companies, especially in SMEs, the optimization of processes in terms of resource consumption, waste minimization, and pollutant emissions is becoming increasingly important. Another important driver is digitalization and the associated increase in the volume of data. These data, from a multitude of devices and systems, offer enormous potential, which increases the need for intelligent, dynamic analysis models even in smaller companies. This article presents the results of an investigation into whether and to what extent machine learning processes can contribute to optimizing energy consumption and reducing incorrectly produced plastic parts in plastic processing SMEs. For this purpose, the machine data were recorded in a plastics-producing company for the automotive industry and analyzed with regard to the material and energy flows. Machine learning methods were used to train these data in order to uncover optimization potential. Another problem that was addressed in the project was the analysis of manufacturing processes characterized by strong non-linearities and time-invariant behavior with Big Data methods and self-learning controls. Machine learning is suitable for this if sufficient training data are available. Due to the high material throughput in the production of the SMEs’ plastic parts, these requirements for the development of suitable learning methods were met. In response to the increasing importance of current information technologies in industrial production processes, the project aimed to use these technologies for sustainable digitalization in order to reduce the industry’s environmental impact and increase efficiency.
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Tan, Jun, Hao Chen, Xuerong Ye, and Yigang Lin. "A Novel Fault Diagnosis Approach for the Manufacturing Processes of Permanent Magnet Actuators for Renewable Energy Systems." Energies 15, no. 13 (July 1, 2022): 4826. http://dx.doi.org/10.3390/en15134826.
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A permanent magnet actuator (PMA) is a critical device for transforming, transmitting, and protecting electrical energy in renewable energy systems. The reliability of a PMA exerts a direct effect on the operational safety, stability, and reliability of renewable energy systems. An effective fault diagnosis and adjustments for manufacturing processes (MPs) are vital for improving the reliability of a PMA. However, the state-of-the-art fault diagnosis methods are mainly used for single process parameters, extensive sample data, and automated manufacturing systems under real-time monitoring and are not applicable to a PMA with low levels of automation and high human factor-induced uncertainties. This study proposes a novel fault diagnosis approach based on a surrogate model and machine learning for multiple manufacturing processes of a PMA with insufficient training data due to human factor uncertainties. First, a surrogate model that correlated the MP parameters with the output characteristics (OCs) was constructed by a finite element simulation. Second, the quality performance of the OCs under different fault combinations with the mean or variance of the shift of the MP parameters as typical patterns was calculated by the Monte Carlo method. Finally, using the above computations as the training data, a fault diagnosis model capable of identifying the fault pattern of the manufacturing process parameters according to the OCs was constructed based on machine learning. This approach compensated for the inadequacies of traditional fault diagnosis methods with complex analytical models or numerous processing data. The effectiveness and potential applications of the proposed approach were verified through a case study of a rotary PMA in smart grids.
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McAfee, Marion, Mandana Kariminejad, Albert Weinert, Saif Huq, Johannes D. Stigter, and David Tormey. "State Estimators in Soft Sensing and Sensor Fusion for Sustainable Manufacturing." Sustainability 14, no. 6 (March 19, 2022): 3635. http://dx.doi.org/10.3390/su14063635.
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State estimators, including observers and Bayesian filters, are a class of model-based algorithms for estimating variables in a dynamical system given the sensor measurements of related system states. They can be used to derive fast and accurate estimates of system variables that cannot be measured directly (‘soft sensing’) or for which only noisy, intermittent, delayed, indirect, or unreliable measurements are available, perhaps from multiple sources (‘sensor fusion’). In this paper, we introduce the concepts and main methods of state estimation and review recent applications in improving the sustainability of manufacturing processes across sectors including industrial robotics, material synthesis and processing, semiconductor, and additive manufacturing. It is shown that state estimation algorithms can play a key role in manufacturing systems for accurately monitoring and controlling processes to improve efficiencies, lower environmental impact, enhance product quality, improve the feasibility of processing more sustainable raw materials, and ensure safer working environments for humans. We discuss current and emerging trends in using state estimation as a framework for combining physical knowledge with other sources of data for monitoring and controlling distributed manufacturing systems.
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Ingarao, Giuseppe, Paolo C. Priarone, Francesco Gagliardi, Rosa di Lorenzo, and Luca Settineri. "Environmental Comparison between a Hot Extrusion Process and Conventional Machining Processes through a Life Cycle Assessment Approach." Key Engineering Materials 622-623 (September 2014): 103–10. http://dx.doi.org/10.4028/www.scientific.net/kem.622-623.103.
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Nowadays manufacturing technologies have to be evaluated not only for the technical features they can provide to products, but also considering the environmental perspective as well. As long as the technological feasibility of a given process is guaranteed, processes minimizing resources and energy consumption have to be selected for manufacturing. With respect to this topic, the research studies in the domain of metal processing technologies predominantly focus on conventional material removal processes as milling and turning. Despite some exceptions, many other non-machining technologies, such as metal forming processes, are still not well documented in terms of their energy and resource efficiency, and related environmental impact. In this paper, an environmental challenge between two traditional technologies is developed: the environmental performances of a partial hot extrusion process and of a turning processes are quantified and compared. A Life Cycle Assessment (LCA) approach is implemented to properly analyze the considered processes. The material production step and the manufacturing phase to obtain a simple axy-symmetric aluminum component is considered for the Life Cycle Inventory (LCI) data collection step. Besides, the material and consumables usage and the consumed electrical power are measured in order to quantify the energy consumption of the manufacturing phase. Further, the environmental impacts related to the manufacturing of the extrusion dies and of the turning process are included in the analysis. The paper presents an early step of a wider research project aiming at identifying the greenest technologies as functions of given product features.
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Tyupin, V. N. "Finding velocity of roller-bit and rotary-percussive drilling using the energy conservation law." Mining informational and analytical bulletin, no. 6 (May 20, 2020): 76–84. http://dx.doi.org/10.25018/0236-1493-2020-6-0-76-84.
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Velocity of roller-bit and rotary-percussive drilling depends on many factors distributed in 4 groups in technical literature: rock properties, bit parameters, bit-rock interaction conditions and drilling modes. Literature sources present some very complex formulas which need finding empirical coefficients before determining drilling velocity, i.e. the formulas are difficult to use. Moreover, the formulas neglect jointing of rock masses. At the same time, mathematical relations connecting drilling velocity, drilling mode and drillability of jointed rocks will make it possible to rate drilling processes and adjust blasting parameters. These studies aim to determine velocity of roller-bit and rotary-percussive drilling using the energy conservation law. The used method of mathematical modeling allowed obtaining formulas for rock drilling velocity with regard to drilling modes, bit parameters, factor of rock hardness (strength) and rock mass jointing. The validity of the relations of the roller-bit and rotary-percussive drilling velocity is proved. The reliability of the drilling velocity formulas can be determined by means of investigations performed in open pit mines, with recording of all parameters and with mathematical processing of the data. The mathematical relations connecting drilling velocity, drilling modes, drill bit parameters and drillability of jointed rocks will enable rating of drilling and adjustment of blasting patterns.
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Girdu, Constantin Cristinel, and Catalin Gheorghe. "Energy Efficiency in CO2 Laser Processing of Hardox 400 Material." Materials 15, no. 13 (June 26, 2022): 4505. http://dx.doi.org/10.3390/ma15134505.
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The use of laser technology for materials processing has a wide applicability in various industrial fields, due to its proven advantages, such as processing time, economic efficiency and reduced impact on the natural environment. The expansion of laser technology has been possible due to the dynamics of research in the field. One of the directions of research is to establish the appropriate cutting parameters. The evolution of research in this direction can be deepened by determining the efficiency of laser cutting. Starting from such a hypothesis, the study contains an analysis of laser cutting parameters (speed, power and pressure) to determine the linear energy and cutting efficiency. For this purpose, the linear energy and the cutting efficiency were determined analytically, and the results obtained were tested with the Lagrange interpolation method, the statistical mathematical method and the graphical method. The material chosen was Hardox 400 steel with a thickness of 8 mm, due to its numerous industrial applications and the fact that it is an insufficiently studied material. Statistical data processing shows that the maximum cutting efficiency is mainly influenced by speed, followed by laser power. The results obtained reduce energy costs in manufacturing processes that use the CO2 laser. The combinations identified between laser speed and power lead to a reduction in energy consumption and thus to an increase in processing efficiency. Through the calculation relationships established for linear energy and cutting efficiency, the study contributes to the extension of the theoretical and practical basis.
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Datsenko, Olha, and Tatiana Klotchko. "APPLICATION OF LOTKA-VOLTERRA MODEL FOR OPTIMIZATION OF AUTOMATED PARTS PROCESSING SYSTEM." Bulletin of Kyiv Polytechnic Institute. Series Instrument Making, no. 64(2) (December 24, 2022): 77–86. http://dx.doi.org/10.20535/1970.64(2).2022.270033.
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The article shows peculiarities of the organization of technological processes of processing parts when applying automation of the enterprise and at same time defines main problems associated with production features, technological processes, problems of conservation and rational use of resources. It is shown that these problems are solved by optimizing production processes, in particular, e technological process of manufacturing precise parts of devices.
Optimization of production has always been a very relevant issue for many enterprises in various branches of production. This is not surprising, because both the state and private business are always looking for new technologies and possible ways to reduce expenses, increase profits, and solve the problem of general improvement of production efficiency. However, after beginning of the full-scale invasion of Russia on the territory of the sovereign state of Ukraine and the massive destruction of industrial and critical infrastructure, civilian buildings and housing, the issue of effective use of available and surviving resources became extremely urgent. According to official data, as of April 2022, losses of industrial assets amount to $6.7 billion. So, this is why there is a need to optimize production under condition of its automation.
On the basis of research on the current state of optimization of automated systems for mechanical processing of parts and basic scientific research on this topic, approaches to the justification of a formalized optimization model aimed at increasing the efficiency of instrument-making production are considered. An optimization solution is proposed, based on use of Lotka-Volterra mathematical model, which will allow optimizing the operation of precision parts manufacturing systems under the condition of production automation. The results of conducted proved the expediency of such bionic approaches to the automation of technological structures of the production enterprise, since the proposed model is mostly used in research of biological processes.
So, well-founded analogies made it possible to determine full range of production problems, including logistical problems and personnel problems based on competing models.
The created software, namely a web application that performs all the necessary calculations, provides the ability to build graphs that visualize the results of the optimization of automated parts processing unit of enterprise, or the overall system, or its individual structural unit.
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Luo, Xin. "Analysis of Vibrational Energy on the Pendulum Based on Finite Element." Key Engineering Materials 693 (May 2016): 453–57. http://dx.doi.org/10.4028/www.scientific.net/kem.693.453.
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Charpy impact test is very sensitive to mechanical processing technologies and product defects. Charpy impact test can give quantitative test data and improve the product quality of advanced manufacturing industry and the safety of the application of new materials. The Charpy impact test machine has elastic deformation. The center of percussion is different from the designed center of strike and this difference can affect the vibration energy on the pendulum. In this article, by using the finite element analysis method, we simulate experimental processes having different distance to the center of percussion and obtain the numerical quantity related effects. In the end, we verify the accuracy of the finite element analysis by using different energy level impact test.
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Yu, Hui Jun, Zhi Wei Zhou, Cai Biao Chen, and Ju Hui Gu. "Design of Locomotive Intelligent Watt Hour Meter Based on STM32." Applied Mechanics and Materials 672-674 (October 2014): 1205–9. http://dx.doi.org/10.4028/www.scientific.net/amm.672-674.1205.
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The paper focuses on the locomotive intelligent watt hour meter based on STM32. By designing Intelligent watt hour meter main function, hardware architecture, and software processes to achieve energy data display, storage, and duplex communication. The hardware structure consists of main control module, electric energy metering module, communication module and other components, electric energy metering module is responsible for data acquisition, the main control module is responsible for data processing, communications module is responsible for two-way communication. Using MATLAB software to simulate the newly developed Intelligent watt hour meter,the simulation results show that, the intelligent watt hour meter has the advantages of high precision, low power consumption, fast performance, strong anti-interference ability, low manufacturing cost etc.
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Wang, Nan, Quan Yang, and Cuixia Zhang. "Data-Driven Low-Carbon Control Method of Machining Process—Taking Axle as an Example." Sustainability 14, no. 21 (October 29, 2022): 14133. http://dx.doi.org/10.3390/su142114133.
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It is an inevitable trend of enterprise development to optimize the low-carbon machining process and reduce the carbon emissions generated by this system. The traditional quality-based manufacturing method is no longer suitable for today’s concept of sustainable development. Therefore, a data-driven method based on uncertainty evaluation for low-carbon control in machining processes is proposed. Firstly, the framework for the data-driven method was established, then the data collection for the input and output in the machining process was carried out. Secondly, by establishing the carbon emission data model and analyzing data with carbon emission uncertainty evaluation indicators during processing, the carbon emission optimization strategy was proposed. Finally, axle processing technology was applied to the experimental verification, exploring the uncertainty of emissions finishing machining steps and other work sequences, while carrying out targeted strategy optimization, which verifies the feasibility and effectiveness of the method. The results show that the uncertainty of each process is reduced after optimization. This study provides theoretical and methodological support for promoting low-carbon emissions for manufacturing enterprises.
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Kim, Hoejin, Yirong Lin, and Tzu-Liang Bill Tseng. "A review on quality control in additive manufacturing." Rapid Prototyping Journal 24, no. 3 (April 9, 2018): 645–69. http://dx.doi.org/10.1108/rpj-03-2017-0048.
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Purpose The usage of additive manufacturing (AM) technology in industries has reached up to 50 per cent as prototype or end-product. However, for AM products to be directly used as final products, AM product should be produced through advanced quality control process, which has a capability to be able to prove and reach their desire repeatability, reproducibility, reliability and preciseness. Therefore, there is a need to review quality-related research in terms of AM technology and guide AM industry in the future direction of AM development. Design/methodology/approach This paper overviews research progress regarding the QC in AM technology. The focus of the study is on manufacturing quality issues and needs that are to be developed and optimized, and further suggests ideas and directions toward the quality improvement for future AM technology. This paper is organized as follows. Section 2 starts by conducting a comprehensive review of the literature studies on progress of quality control, issues and challenges regarding quality improvement in seven different AM techniques. Next, Section 3 provides classification of the research findings, and lastly, Section 4 discusses the challenges and future trends. Findings This paper presents a review on quality control in seven different techniques in AM technology and provides detailed discussions in each quality process stage. Most of the AM techniques have a trend using in-situ sensors and cameras to acquire process data for real-time monitoring and quality analysis. Procedures such as extrusion-based processes (EBP) have further advanced in data analytics and predictive algorithms-based research regarding mechanical properties and optimal printing parameters. Moreover, compared to others, the material jetting progresses technique has advanced in a system integrated with closed-feedback loop, machine vision and image processing to minimize quality issues during printing process. Research limitations/implications This paper is limited to reviewing of only seven techniques of AM technology, which includes photopolymer vat processes, material jetting processes, binder jetting processes, extrusion-based processes, powder bed fusion processes, directed energy deposition processes and sheet lamination processes. This paper would impact on the improvement of quality control in AM industries such as industrial, automotive, medical, aerospace and military production. Originality/value Additive manufacturing technology, in terms of quality control has yet to be reviewed.
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Brooks, Christopher, Mark Swainson, Ian Beauchamp, Isabel Campelos, Ruzaina Ishak, and Wayne Martindale. "Transformational Steam Infusion Processing for Resilient and Sustainable Food Manufacturing Businesses." Foods 10, no. 8 (July 30, 2021): 1763. http://dx.doi.org/10.3390/foods10081763.
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Here we show how food and beverage manufacturers report more incisive sustainability and product fulfilment outcomes for their business enterprises when innovative processing technologies are used. The reported steam infusion technology heats food materials within a Vaction Pump device so that steam is directed into the food material within a much reduced volume, reducing the use of steam and processing time. This study reports how such technological interventions will enable supply chain stakeholders to demonstrate responsible consumption by connecting assessments for the reduction of greenhouse gas emissions with consumer-focused outcomes such as product quality. The technology reported in this research not only improves operational agility by improving processing speed, but also improves the responsiveness of factory production to changes in demand. Heating procedures are systemic processes in the food industry that can be used to pasteurize, achieve commercially viable shelf-life, and provide cleaning in place. The reported research defines how these technologies can reduce the carbon footprint of products, improve quality attributes, and lower operating costs across supply chains. They provide an important step in developing distributed manufacturing in the food system because the technologies reported here are modular and can be installed into existing operations. The specific technology can reduce energy consumption by 17.3% compared to basic direct steam heating, with a reduction of 277.8 processing hours and 8.7 tonnes GHG emissions per kettle production line each year. Food and beverage manufacturers are increasingly required to report across the sustainability, nutrition, and product quality outcomes of their business enterprises more incisively so that supply chain stakeholders can demonstrate responsible production and consumption. The steam infusion technologies assessed in this research enable alignment to the UN Sustainable Development Goals, specifically SDG12, Responsible Production and Consumption, using in situ data logging in factory trials for novel heating procedures used to process foods.
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Jasiulewicz-Kaczmarek, Małgorzata, Katarzyna Antosz, Ryszard Wyczółkowski, Dariusz Mazurkiewicz, Bo Sun, Cheng Qian, and Yi Ren. "Application of MICMAC, Fuzzy AHP, and Fuzzy TOPSIS for Evaluation of the Maintenance Factors Affecting Sustainable Manufacturing." Energies 14, no. 5 (March 5, 2021): 1436. http://dx.doi.org/10.3390/en14051436.
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This paper presents an empirical study on the impact of maintenance function on more sustainable manufacturing processes. The work methodology comprises four stages. At first, ten factors of maintenance activities from a sustainable manufacturing point of view were identified. Then, in the second stage, the matrix of crossed impact multiplications applied to a classification (MICMAC) was carried out to categorize these factors based on their influence and dependence values. In the third stage, the criteria for evaluation of maintenance factors were defined, then the fuzzy analytic hierarchy process (F-AHP) was applied to determine their relative weights. In the last stage, the results of MICMAC and F-AHP analyses were used as inputs for the fuzzy technique for order preference by similarity to ideal solution (F-TOPIS) to generate aggregate scores and selection of the most important maintenance factors that have an impact on sustainable manufacturing processes. A numerical example is provided to demonstrate the applicability of the approach. It was observed that factors “Implementation of preventive and prognostic service strategies”, “The usage of M&O data collection and processing systems”, and “Modernization of machines and devices” are the major factors that support the realization of sustainable manufacturing process challenges.
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Delgado-Alvarado, Enrique, Jaime Martínez-Castillo, Luis Zamora-Peredo, Jose Amir Gonzalez-Calderon, Ricardo López-Esparza, Muhammad Waseem Ashraf, Shahzadi Tayyaba, and Agustín L. Herrera-May. "Triboelectric and Piezoelectric Nanogenerators for Self-Powered Healthcare Monitoring Devices: Operating Principles, Challenges, and Perspectives." Nanomaterials 12, no. 24 (December 9, 2022): 4403. http://dx.doi.org/10.3390/nano12244403.
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The internet of medical things (IoMT) is used for the acquisition, processing, transmission, and storage of medical data of patients. The medical information of each patient can be monitored by hospitals, family members, or medical centers, providing real-time data on the health condition of patients. However, the IoMT requires monitoring healthcare devices with features such as being lightweight, having a long lifetime, wearability, flexibility, safe behavior, and a stable electrical performance. For the continuous monitoring of the medical signals of patients, these devices need energy sources with a long lifetime and stable response. For this challenge, conventional batteries have disadvantages due to their limited-service time, considerable weight, and toxic materials. A replacement alternative to conventional batteries can be achieved for piezoelectric and triboelectric nanogenerators. These nanogenerators can convert green energy from various environmental sources (e.g., biomechanical energy, wind, and mechanical vibrations) into electrical energy. Generally, these nanogenerators have simple transduction mechanisms, uncomplicated manufacturing processes, are lightweight, have a long lifetime, and provide high output electrical performance. Thus, the piezoelectric and triboelectric nanogenerators could power future medical devices that monitor and process vital signs of patients. Herein, we review the working principle, materials, fabrication processes, and signal processing components of piezoelectric and triboelectric nanogenerators with potential medical applications. In addition, we discuss the main components and output electrical performance of various nanogenerators applied to the medical sector. Finally, the challenges and perspectives of the design, materials and fabrication process, signal processing, and reliability of nanogenerators are included.
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Abdullah Hanif, Muhammad, and Muhammad Shafique. "SalvageDNN: salvaging deep neural network accelerators with permanent faults through saliency-driven fault-aware mapping." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 378, no. 2164 (December 23, 2019): 20190164. http://dx.doi.org/10.1098/rsta.2019.0164.
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Deep neural networks (DNNs) have proliferated in most of the application domains that involve data processing, predictive analysis and knowledge inference. Alongside the need for developing highly performance-efficient DNN accelerators, there is an utmost need to improve the yield of the manufacturing process in order to reduce the per unit cost of the DNN accelerators. To this end, we present ‘SalvageDNN’, a methodology to enable reliable execution of DNNs on the hardware accelerators with permanent faults (typically due to imperfect manufacturing processes). It employs a fault-aware mapping of different parts of a given DNN on the hardware accelerator (subjected to faults) by leveraging the saliency of the DNN parameters and the fault map of the underlying processing hardware. We also present novel modifications in a systolic array design to further improve the yield of the accelerators while ensuring reliable DNN execution using ‘SalvageDNN’ and negligible overheads in terms of area, power/energy and performance. This article is part of the theme issue ‘Harmonizing energy-autonomous computing and intelligence’.
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Wang, Feng, Zhijie Yang, Xiangzhou Hu, Yu Pan, Yuan Lu, and Man Jiang. "Coaxial 3D printed anisotropic thermal conductive composite aerogel with aligned hierarchical porous carbon nanotubes and cellulose nanofibers." Smart Materials and Structures 31, no. 4 (February 18, 2022): 045002. http://dx.doi.org/10.1088/1361-665x/ac4e4e.
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Abstract Thermal management materials are obtaining increasing research interest, due to the requirement on energy conservation and environment protection. However, the complex designs and energy-consuming manufacturing processes prohibit their wide spread practical account. 3D printing is an intriguing revolutionary technology in fabricating anisotropic thermal conductive materials because of its inherent virtues on directional additive manufacturing a complicated subject with designed microstructure. We demonstrate the coaxial 3D printing along with directional freezing processes to obtain anisotropic thermal conductive composite aerogel consisting of carbon nanotubes (CNTs) and cellulose nanofibers (CNFs). The as prepared composite aerogel, with the thermal conductive CNTs as inner layer, and the insulate CNFs as outer layer, presented remarkable anisotropic thermal conductivity with 0.025 W (m K)−1 in the axial direction and 0.302 W (m K)−1 in the radial direction. The Young’s modulus of the CNTs/CNFs composite aerogel was tested to be 10.91 MPa in the axial direction, and 2.62 MPa in the radial direction, respectively. The coaxial 3D printed CNTs/CNFs composite aerogel has great potential application in electronics, especially for those custom-tailored products and the related field.
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Bykov, Alexander N., Marina N. Vishnyakova, Yuriy N. Deryugin, Andrey B. Emelyanov, Alexey A. Lazarev, Sergey N. Polishchuk, and Christina V. Cherenkova. "Numerical simulation of selective laser melting by the SPH method." Zhurnal Srednevolzhskogo Matematicheskogo Obshchestva 24, no. 4 (December 31, 2022): 419–35. http://dx.doi.org/10.15507/2079-6900.24.202204.419-435.
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Abstract. Currently, additive manufacturing technologies develop actively. This requires creation of computational methods to describe physical processes occurring at the time of manufacturing. One of the methods used for the production of metal powder parts is the method of selective laser melting. This paper presents an SPH-based numerical technique for modeling the process of powder sintering under the influence of a laser beam. The flow of liquid formed as a result of melting is described by the Navier-Stokes equations. Pressure forces, viscous effects and surface forces at the interface are included in the force balance. The thermal state is determined from the energy conservation law, which takes into account thermal processes, volumetric absorption of laser radiation energy, convective heat exchange with the external environment and radiation. Phase transitions between solid and liquid phases are described in the framework of the generalized formulation of the Stefan problem. The calculation method is verified on tests specific to the class of problems under consideration. A comparison is made with the analytical solution, as well as with solutions obtained by other modifications of the SPH method, and with experimental data.
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Kelling, Steve. "Technology Developments for Biodiversity Monitoring and Conservation." Biodiversity Information Science and Standards 2 (May 22, 2018): e25833. http://dx.doi.org/10.3897/biss.2.25833.
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Over the next 5 years major advances in the development and application of numerous technologies related to computing, mobile phones, artificial intelligence (AI), and augmented reality (AR) will have a dramatic impact in biodiversity monitoring and conservation. Over a 2-week period several of us had the opportunity to meet with multiple technology experts in the Silicon Valley, California, USA to discuss trends in technology innovation, and how they could be applied to conservation science and ecology research. Here we briefly highlight some of the key points of these meetings with respect to AI and Deep Learning. Computing: Investment and rapid growth in AI and Deep Learning technologies are transforming how machines can perceive the environment. Much of this change is due to increased processing speeds of Graphics Processing Units (GPUs), which is now a billion-dollar industry. Machine learning applications, such as convolutional neural networks (CNNs) run more efficiently on GPUs and are being applied to analyze visual imagery and sounds in real time. Rapid advances in CNNs that use both supervised and unsupervised learning to train the models is improving accuracy. By taking a Deep Learning approach where the base layers of the model are built upon datasets of known images and sounds (supervised learning) and later layers relying on unclassified images or sounds (unsupervised learning), dramatically improve the flexibility of CNNs in perceiving novel stimuli. The potential to have autonomous sensors gathering biodiversity data in the same way personal weather stations gather atmospheric information is close at hand. Mobile Phones: The phone is the most widely used information appliance in the world. No device is on the near horizon to challenge this platform, for several key reasons. First, network access is ubiquitous in many parts of the world. Second, batteries are improving by about 20% annually, allowing for more functionality. Third, app development is a growing industry with significant investment in specializing apps for machine-learning. While GPUs are already running on phones for video streaming, there is much optimism that reduced or approximate Deep Learning models will operate on phones. These models are already working in the lab, with the biggest hurdle being power consumption and developing energy efficient applications and algorithms to run complicated AI processes will be important. It is just a matter of time before industry will have AI functionality on phones. These rapid improvements in computing and mobile phone technologies have huge implications for biodiversity monitoring, conservation science, and understanding ecological systems. Computing: AI processing of video imagery or acoustic streams create the potential to deploy autonomous sensors in the environment that will be able to detect and classify organisms to species. Further, AI processing of Earth spectral imagery has the potential to provide finer grade classification of habitats, which is essential in developing fine scale models of species distributions over broad spatial and temporal extents. Mobile Phones: increased computing functionality and more efficient batteries will allow applications to be developed that will improve an individual’s perception of the world. Already AI functionality of Merlin improves a birder’s ability to accurately identify a bird. Linking this functionality to sensor devices like specialized glasses, binoculars, or listening devises will help an individual detect and classify objects in the environment. In conclusion, computing technology is advancing at a rapid rate and soon autonomous sensors placed strategically in the environment will augment the species occurrence data gathered by humans. The mobile phone in everyone’s pocket should be thought of strategically, in how to connect people to the environment and improve their ability to gather meaningful biodiversity information.
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Soulis, Spyridon, George Konstantopoulos, Elias P. Koumoulos, and Costas A. Charitidis. "Impact of Alternative Stabilization Strategies for the Production of PAN-Based Carbon Fibers with High Performance." Fibers 8, no. 6 (May 26, 2020): 33. http://dx.doi.org/10.3390/fib8060033.
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The aim of this work is to review a possible correlation of composition, thermal processing, and recent alternative stabilization technologies to the mechanical properties. The chemical microstructure of polyacrylonitrile (PAN) is discussed in detail to understand the influence in thermomechanical properties during stabilization by observing transformation from thermoplastic to ladder polymer. In addition, relevant literature data are used to understand the comonomer composition effect on mechanical properties. Technologies of direct fiber heating by irradiation have been recently involved and hold promise to enhance performance, reduce processing time and energy consumption. Carbon fiber manufacturing can provide benefits by using higher comonomer ratios, similar to textile grade or melt-spun PAN, in order to cut costs derived from an acrylonitrile precursor, without suffering in regard to mechanical properties. Energy intensive processes of stabilization and carbonization remain a challenging field of research in order to reduce both environmental impact and cost of the wide commercialization of carbon fibers (CFs) to enable their broad application.
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Luo, Fei, Bo Feng, and Huazhong Wang. "Automatic first-arrival picking method via intelligent Markov optimal decision processes." Journal of Geophysics and Engineering 18, no. 3 (June 2021): 406–17. http://dx.doi.org/10.1093/jge/gxab026.
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Abstract Picking the first arrival is an important step in seismic processing. The large volume of the seismic data calls for automatic and objective picking. In this paper, we formulate first-arrival picking as an intelligent Markov decision process in the multi-dimensional feature attribute space. By designing a reasonable model, the global optimization is carried out in the reward function space to obtain the path with the largest cumulative reward value, to achieve the purpose of automatically picking up the first arrival. The state-value function contains a distance-related discount factor γ, which enables the Markov decision process to pick up the first-arrival continuity to consider the lateral continuity of the seismic data and avoid the bad trace information in the seismic data. On this basis, the method of this paper further introduces the optimized model that is a fuzzy clustering-based multi-dimensional attribute reward function and structure-based Gaussian stochastic policy, thereby reducing the difficulty of model design, and making the seismic data pick up more accurately and automatically. Testing this approach in the field seismic data reveals its properties and shows it can automatically pick up more reasonable first arrivals and has a certain quality control ability, especially the first-arrival energy is weak (the signal-to-noise ratio is low) or there are adjacent complex waveforms in the shallow layer.
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Sand, Christian, Dominik Manke, and Jörg Franke. "Virtual Process Data Linkage of Assembly Stations in High Variance Workshop Production." Applied Mechanics and Materials 871 (October 2017): 60–68. http://dx.doi.org/10.4028/www.scientific.net/amm.871.60.
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The advance of digitalization changes the requirements of processes in industrial production and assembly. For this reason, production and assembly must now be able to execute complex process steps. This is about quality and productivity expectations, as well as flexibility and reliability of production, lines and plants [1]. Today, data is generated by almost every system, machine and sensor, yet it is hardly used for process optimization. Manufacturing processes are usually organized as workshop production or chained production systems, in addition to standalone machines [2,3]. Most analytic projects focus on chained systems and serial production, unlike individual machines and specific workshop production. Depending on manufacturing IT, process data from serial production is stored in data bases, which are usually optimized for traceability. Standalone machines and machines within workshop production are scarcely connected to a common data base. The required process data is stored either on the module itself or inside a local data base [4]. The identification of dependencies between individual assembly processes, energy data and the quality of the finished product is necessary for an extended optimization. These optimizations can be process-specific, as well as environmental and resource related. Due to decentralized process data storages, an overall view of a dynamic order-oriented value chain is denied. Therefore, the potential of the machines is largely unused. Based on Data Mining, this advanced development can be counteracted by process monitoring and optimization. Therefore, this paper provides a solution for a virtual process data linkage of assembly stations. This enables the acquisition, processing, transformation and storage of unstructured raw data by special software and methods, which is also able to cope with chained production systems and standalone machines. For further analysis of interdependencies, a visualization is developed for advanced monitoring and optimization [5,6].
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Wang, Weile, Ramakrishna Nemani, Hirofumi Hashimoto, Sangram Ganguly, Dong Huang, Yuri Knyazikhin, Ranga Myneni, and Govindasamy Bala. "An Interplay between Photons, Canopy Structure, and Recollision Probability: A Review of the Spectral Invariants Theory of 3D Canopy Radiative Transfer Processes." Remote Sensing 10, no. 11 (November 14, 2018): 1805. http://dx.doi.org/10.3390/rs10111805.
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Earth observations collected by remote sensors provide unique information to our ever-growing knowledge of the terrestrial biosphere. Yet, retrieving information from remote sensing data requires sophisticated processing and demands a better understanding of the underlying physics. This paper reviews research efforts that lead to the developments of the stochastic radiative transfer equation (RTE) and the spectral invariants theory. The former simplifies the characteristics of canopy structures with a pair-correlation function so that the 3D information can be succinctly packed into a 1D equation. The latter indicates that the interactions between photons and canopy elements converge to certain invariant patterns quantifiable by a few wavelength independent parameters, which satisfy the law of energy conservation. By revealing the connections between plant structural characteristics and photon recollision probability, these developments significantly advance our understanding of the transportation of radiation within vegetation canopies. They enable a novel physically-based algorithm to simulate the “hot-spot” phenomenon of canopy bidirectional reflectance while conserving energy, a challenge known to the classic radiative transfer models. Therefore, these theoretical developments have a far-reaching influence in optical remote sensing of the biosphere.
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Pei, Li, Zeya Xi, Bing Bai, Jianshuai Wang, Xiaoyan Zuo, Tigang Ning, Jingjing Zheng, and Jing Li. "Key Technologies of Photonic Artificial Intelligence Chip Structure and Algorithm." Applied Sciences 11, no. 12 (June 20, 2021): 5719. http://dx.doi.org/10.3390/app11125719.
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Artificial intelligence chips (AICs) are the intersection of integrated circuits and artificial intelligence (AI), involving structure design, algorithm analysis, chip fabrication and application scenarios. Due to their excellent ability in data processing, AICs show a long-term industrial prospect in big data services, cloud centers, etc. However, with the conceivable exhaustion of Moore’s Law, the size of traditional electronic AICs (EAICs) is gradually approaching the limit, and an architectural update is highly required. Photonic artificial intelligence chips (PAIC) utilize light beam propagation in the silicon waveguide, contributing to a high parallelism configuration, fast calculation speed and low latency. Due to light manipulation, PAICs perform well in anti-electromagnetic interference and energy conservation. This invited paper summarized the recent research on PAICs. The characteristics of different hardware structures are discussed. The current widely used training algorithm is given and the Photonic Design Automatic (PDA) simulation platform is introduced. In addition, the authors’ related work on PAICs is presented and we believe that PAICs may play a critical role in the deployment of data processing technology.
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Ali, Bagh, Rizwan Ali Naqvi, Dildar Hussain, Omar M. Aldossary, and Sajjad Hussain. "Magnetic Rotating Flow of a Hybrid Nano-Materials Ag-MoS2 and Go-MoS2 in C2H6O2-H2O Hybrid Base Fluid over an Extending Surface Involving Activation Energy: FE Simulation." Mathematics 8, no. 10 (October 9, 2020): 1730. http://dx.doi.org/10.3390/math8101730.
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Numeric simulations are performed for a comparative study of magnetohydrodynamic (MHD) rotational flow of hybrid nanofluids (MoS2-Ag/ethyleneglycol-water (50–50%) and MoS2-Go/ethyleneglycol-water (50–50%)) over a horizontally elongated plane sheet. The principal objective is concerned with the enhancement of thermal transportation. The three-dimensional formulation governing the conservation of mass, momentum, energy, and concentration is transmuted into two-dimensional partial differentiation by employing similarity transforms. The resulting set of equations (PDEs) is then solved by variational finite element procedure coded in Matlab script. An intensive computational run is carried out for suitable ranges of the particular quantities of influence. The primary velocity component decreases monotonically and the magnitude of secondary velocity component diminishes significantly when magnetic parameter, rotational parameter, and unsteadiness parameter are incremented. Both the primary and secondary velocities are smaller in values for the hybrid phase Ag-MoS2 than that of hybrid phase Go-MoS2 but the nanoparticle concentration and temperature are higher for hybrid phase Ag-MoS2. The increased values of parameters for thermophoresis, Brownian motion, shape factor, and volume fraction of ϕ2 made significant improvement in the temperature of the two phases of nano liquids. Results are also computed for the coefficients of skin friction(x, y-directions), Nusselt number, and Sherwood number. The present findings manifest reasonable comparison to their existing counterparts. Some of the practical engineering applications of the present analysis may be found in high-temperature nanomaterial processing technology, crystal growing, extrusion processes, manufacturing and rolling of polymer sheets, academic research, lubrication processes, and polymer industry.
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Li, Zhi, Fei Fei, and Guanglie Zhang. "Edge-to-Cloud IIoT for Condition Monitoring in Manufacturing Systems with Ubiquitous Smart Sensors." Sensors 22, no. 15 (August 7, 2022): 5901. http://dx.doi.org/10.3390/s22155901.
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The Industrial Internet of Things (IIoT) connects industrial assets to ubiquitous smart sensors and actuators to enhance manufacturing and industrial processes. Data-driven condition monitoring is an essential technology for intelligent manufacturing systems to identify anomalies from malfunctioning equipment, prevent unplanned downtime, and reduce the operation costs by predictive maintenance without interrupting normal machine operations. However, data-driven condition monitoring requires massive data collected from smart sensors to be transmitted to the cloud for further processing, thereby contributing to network congestion and affecting the network performance. Furthermore, unbalanced training data with very few labelled anomalies limit supervised learning models because of the lack of sufficient fault data for the training process in anomaly detection algorithms. To address these issues, we proposed an IIoT-based condition monitoring system with an edge-to-cloud architecture and computed the relative wavelet energy as feature vectors on the edge layer to reduce the network traffic overhead. We also proposed an unsupervised deep long short-term memory (LSTM) network module for anomaly detection. We implemented the proposed IIoT condition monitoring system for a manufacturing machine in a real shop site to evaluate our proposed solution. Our experimental results verify the effectiveness of our approach which can not only reduce the network traffic overhead for the IIoT but also detect anomalies accurately.
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Ur-Rahman, Nadeem. "Textual Data Mining For Knowledge Discovery and Data Classification: A Comparative Study." European Scientific Journal, ESJ 13, no. 21 (July 31, 2017): 429. http://dx.doi.org/10.19044/esj.2017.v13n21p429.
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Business Intelligence solutions are key to enable industrial organisations (either manufacturing or construction) to remain competitive in the market. These solutions are achieved through analysis of data which is collected, retrieved and re-used for prediction and classification purposes. However many sources of industrial data are not being fully utilised to improve the business processes of the associated industry. It is generally left to the decision makers or managers within a company to take effective decisions based on the information available throughout product design and manufacture or from the operation of business or production processes. Substantial efforts and energy are required in terms of time and money to identify and exploit the appropriate information that is available from the data. Data Mining techniques have long been applied mainly to numerical forms of data available from various data sources but their applications to analyse semi-structured or unstructured databases are still limited to a few specific domains. The applications of these techniques in combination with Text Mining methods based on statistical, natural language processing and visualisation techniques could give beneficial results. Text Mining methods mainly deal with document clustering, text summarisation and classification and mainly rely on methods and techniques available in the area of Information Retrieval (IR). These help to uncover the hidden information in text documents at an initial level. This paper investigates applications of Text Mining in terms of Textual Data Mining (TDM) methods which share techniques from IR and data mining. These techniques may be implemented to analyse textual databases in general but they are demonstrated here using examples of Post Project Reviews (PPR) from the construction industry as a case study. The research is focused on finding key single or multiple term phrases for classifying the documents into two classes i.e. good information and bad information documents to help decision makers or project managers to identify key issues discussed in PPRs which can be used as a guide for future project management process.
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Režek Jambrak, Anet, Marinela Nutrizio, Ilija Djekić, Sanda Pleslić, and Farid Chemat. "Internet of Nonthermal Food Processing Technologies (IoNTP): Food Industry 4.0 and Sustainability." Applied Sciences 11, no. 2 (January 12, 2021): 686. http://dx.doi.org/10.3390/app11020686.
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With the introduction of Industry 4.0, and smart factories accordingly, there are new opportunities to implement elements of industry 4.0 in nonthermal processing. Moreover, with application of Internet of things (IoT), smart control of the process, big data optimization, as well as sustainable production and monitoring, there is a new era of Internet of nonthermal food processing technologies (IoNTP). Nonthermal technologies include high power ultrasound, pulsed electric fields, high voltage electrical discharge, high pressure processing, UV-LED, pulsed light, e-beam, and advanced thermal food processing techniques include microwave processing, ohmic heating and high-pressure homogenization. The aim of this review was to bring in front necessity to evaluate possibilities of implementing smart sensors, artificial intelligence (AI), big data, additive technologies with nonthermal technologies, with the possibility to create smart factories together with strong emphasis on sustainability. This paper brings an overview on digitalization, IoT, additive technologies (3D printing), cloud data storage and smart sensors including two SWOT analysis associated with IoNTPs and sustainability. It is of high importance to perform life cycle assessment (LCA), to quantify (En)—environmental dimension; (So)—social dimension and (Ec)—economic dimension. SWOT analysis showed: potential for energy saving during food processing; optimized overall environmental performance; lower manufacturing cost; development of eco-friendly products; higher level of health and safety during food processing and better work condition for workers. Nonthermal and advanced thermal technologies can be applied also as sustainable techniques working in line with the sustainable development goals (SDGs) and Agenda 2030 issued by United Nations (UN).
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Antoninova, N. Yu, L. S. Rybnikova, Yu O. Slavikovskaya, and L. A. Shubina. "Environmental and Economic Aspects of Selecting Reclamation Directions for Industrial Mining and Metallurgical Waste Disposal Sites." Mining Industry Journal (Gornay Promishlennost), no. 1S/2022 (March 16, 2022): 71–77. http://dx.doi.org/10.30686/1609-9192-2022-1s-71-77.
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Anthropogenic geoecology was developed at the end of the 20th century, but continues to face challenges in an integrated methodological approach to assessing the pollution of territories with long-term consequences of natural resource extraction. The consequences of extraction and primary processing of natural raw materials are the lack of effective control over the territories once the ore mining is completed. In order to develop effective methods to control the development of hazardous natural and man-made processes in the areas of inactive waste disposal facilities, it is necessary to analyze a sufficiently large set of data, including the condition of ground and surface waters, soils, flora, the efficiency of waste disposal facilities protection from direct or indirect impact on the natural environment. Research on modeling the processes of transfer and accumulation of pollutants includes a general assessment of the direction of man-made flows and selection of vegetation for phytoremediation of territories along the boundaries of the facilities as well as the direction of pollutant migration. The impact of mining facilities (dumps, tailings reservoirs) is directly correlated with the time of their existence, the toxicity and the rate of transformation of the components contained, the economic efficiency of their further utilization or conservation. Thus, the methodological approach to the rehabilitation of environmentally disadvantageous areas in places where mining and primary processing of resources is completed requires integration of several techniques and methods to assess the existing environmental situation. It also includes the speed and direction of its evolvement, and the economic assessment of damage to the natural environment. The introduction of tested recovery techniques will prevent the expansion of territories with irreversible destruction of geosystems, which led to a complete loss of productivity of the reproducing resources.
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Mousavi, Seyed Mahdi, Saeid Sadeghnejad, and Mehdi Ostadhassan. "Evaluation of 3D printed microfluidic networks to study fluid flow in rocks." Oil & Gas Science and Technology – Revue d’IFP Energies nouvelles 76 (2021): 50. http://dx.doi.org/10.2516/ogst/2021029.
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Visualizing fluid flow in porous media can provide a better understanding of transport phenomena at the pore scale. In this regard, transparent micromodels are suitable tools to investigate fluid flow in porous media. However, using glass as the primary material makes them inappropriate for predicting the natural behavior of rocks. Moreover, constructing these micromodels is time-consuming via conventional methods. Thus, an alternative approach can be to employ 3D printing technology to fabricate representative porous media. This study investigates fluid flow processes through a transparent microfluidic device based on a complex porous geometry (natural rock) using digital-light processing printing technology. Unlike previous studies, this one has focused on manufacturing repeatability. This micromodel, like a custom-built transparent cell, is capable of modeling single and multiphase transport phenomena. First, the tomographic data of a carbonate rock sample is segmented and 3D printed by a digital-light processing printer. Two miscible and immiscible tracer injection experiments are performed on the printed microfluidic media, while the experiments are verified with the same boundary conditions using a CFD simulator. The comparison of the results is based on Structural Similarity Index Measure (SSIM), where in both miscible and immiscible experiments, more than 80% SSIM is achieved. This confirms the reliability of printing methodology for manufacturing reusable microfluidic models as a promising and reliable tool for visual investigation of fluid flow in porous media. Ultimately, this study presents a novel comprehensive framework for manufacturing 2.5D realistic microfluidic devices (micromodels) from pore-scale rock images that are validated through CFD simulations.
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Dugar, Jaka, Awais Ikram, Damjan Klobčar, and Franci Pušavec. "Sustainable Hybrid Manufacturing of AlSi5 Alloy Turbine Blade Prototype by Robotic Direct Energy Layered Deposition and Subsequent Milling: An Alternative to Selective Laser Melting?" Materials 15, no. 23 (December 3, 2022): 8631. http://dx.doi.org/10.3390/ma15238631.
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Additive technologies enable the flexible production through scalable layer-by-layer fabrication of simple to intricate geometries. The existing 3D-printing technologies that use powders are often slow with controlling parameters that are difficult to optimize, restricted product sizes, and are relatively expensive (in terms of feedstock and processing). This paper presents the development of an alternative approach consisting of a CAD/CAM + combined wire arc additive-manufacturing (WAAM) hybrid process utilizing the robotic MIG-based weld surfacing and milling of the AlSi5 aluminum alloy, which achieves sustainably high productivity via structural alloys. The feasibility of this hybrid approach was analyzed on a representative turbine blade piece. SprutCAM suite was utilized to identify the hybrid-manufacturing parameters and virtually simulate the processes. This research provides comprehensive experimental data on the optimization of cold metal transfer (CMT)–WAAM parameters such as the welding speed, current/voltage, wire feed rate, wall thickness, torch inclination angle (shift/tilt comparison), and deposit height. The multi-axes tool orientation and robotic milling strategies, i.e., (a) the side surface from rotational one-way bottom-up and (b) the top surface in a rectangular orientation, were tested in virtual CAM environments and then adopted during the prototype fabrication to minimize the total fabrication time. The effect of several machining parameters and robotic stiffness (during WAAM + milling) were also investigated. The mean deviation for the test piece’s tolerance between the virtual processing and experimental fabrication was −0.76 mm (approx.) at a standard deviation of 0.22 mm assessed by 3D scanning. The surface roughness definition Sa in the final WAAM pass corresponds to 36 µm, which was lowered to 14.3 µm after milling, thus demonstrating a 55% improvement through the robotic comminution. The tensile testing at 0° and 90° orientations reported fracture strengths of 159 and 161.3 MPa, respectively, while the yield stress and reduced longitudinal (0°) elongations implied marginally better toughness along the WAAM deposition axes. The process sustainability factors of hybrid production were compared with Selective Laser Melting (SLM) in terms of the part size freedom, processing costs, and fabrication time with respect to tight design tolerances. The results deduced that this alternative hybrid-processing approach enables an economically viable, resource/energy feasible, and time-efficient method for the production of complex parts in contrast to the conventional additive technologies, i.e., SLM.
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Sankar, Sennan, Somula Ramasubbareddy, Ashish Kr Luhach, Anand Nayyar, and Basit Qureshi. "CT-RPL: Cluster Tree Based Routing Protocol to Maximize the Lifetime of Internet of Things." Sensors 20, no. 20 (October 16, 2020): 5858. http://dx.doi.org/10.3390/s20205858.
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Energy conservation is one of the most critical challenges in the Internet of Things (IoT). IoT devices are incredibly resource-constrained and possess miniature power sources, small memory, and limited processing ability. Clustering is a popular method to avoid duplicate data transfer from the participant node to the destination. The selection of the cluster head (CH) plays a crucial role in gathering and aggregating the data from the cluster members and forwarding the data to the sink node. The inefficient CH selection causes packet failures during the data transfer and early battery depletion nearer to the sink. This paper proposes a cluster tree-based routing protocol (CT-RPL) to increase the life span of the network and avoid the data traffic among the network nodes. The CT-RPL involves three processes, namely cluster formation, cluster head selection, and route establishment. The cluster is formed based on the Euclidean distance. The CH selection is accomplished using a game theoretic approach. Finally, the route is established using the metrics residual energy ratio (RER), queue utilization (QU), and expected transmission count (ETX). The simulation is carried out by using a COOJA simulator. The efficiency of a CT-RPL is compared with the Routing Protocol for Low Power and Lossy Networks (RPL) and energy-efficient heterogeneous ring clustering routing (E2HRC-RPL), which reduces the traffic load and decreases the packet loss ratio. Thus, the CT-RPL enhances the lifetime of the network by 30–40% and the packet delivery ratio by 5–10%.
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Fu, Jianghua, Chunrong Zhu, and Jintao Su. "Research on the Design Method of Pure Electric Vehicle Acceleration Motion Sense Sound Simulation System." Applied Sciences 13, no. 1 (December 22, 2022): 147. http://dx.doi.org/10.3390/app13010147.
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Due to the emerging trend of energy conservation and emission reduction around the world, the new energy vehicle industry has achieved rapid development. However, there are new challenges regarding vehicle NVH (noise, vibration, and harshness). When a pure electric vehicle is being driven, the inside of the vehicle is relatively quiet, which cannot bring the driver sound feedback reflecting the change of the vehicle state. In order to solve this problem, this paper studies the design method of a sound simulation system for acceleration motion in pure electric vehicles. In this paper, the motion characteristics of automobile engine acceleration sound, sound signal analysis and processing, subjective evaluation of sound quality, and sound synthesis strategy are studied. By reading the vehicle running state data on the CAN bus of pure electric vehicles, the goal of engine motion sense sound simulation during vehicle acceleration is achieved. This helps to enhance driving feedback and improve user driving experience.
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Chiumenti, Michele, Xin Lin, Miguel Cervera, Wei Lei, Yuxiang Zheng, and Weidong Huang. "Numerical simulation and experimental calibration of additive manufacturing by blown powder technology. Part I: thermal analysis." Rapid Prototyping Journal 23, no. 2 (March 20, 2017): 448–63. http://dx.doi.org/10.1108/rpj-10-2015-0136.
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Purpose This paper aims to address the numerical simulation of additive manufacturing (AM) processes. The numerical results are compared with the experimental campaign carried out at State Key Laboratory of Solidification Processing laboratories, where a laser solid forming machine, also referred to as laser engineered net shaping, is used to fabricate metal parts directly from computer-aided design models. Ti-6Al-4V metal powder is injected into the molten pool created by a focused, high-energy laser beam and a layer of added material is sinterized according to the laser scanning pattern specified by the user. Design/methodology/approach The numerical model adopts an apropos finite element (FE) activation technology, which reproduces the same scanning pattern set for the numerical control system of the AM machine. This consists of a complex sequence of polylines, used to define the contour of the component, and hatches patterns to fill the inner section. The full sequence is given through the common layer interface format, a standard format for different manufacturing processes such as rapid prototyping, shape metal deposition or machining processes, among others. The result is a layer-by-layer metal deposition which can be used to build-up complex structures for components such as turbine blades, aircraft stiffeners, cooling systems or medical implants, among others. Findings Ad hoc FE framework for the numerical simulation of the AM process by metal deposition is introduced. Description of the calibration procedure adopted is presented. Originality/value The objectives of this paper are twofold: firstly, this work is intended to calibrate the software for the numerical simulation of the AM process, to achieve high accuracy. Secondly, the sensitivity of the numerical model to the process parameters and modeling data is analyzed.
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Zhulai, Yu O., and D. D. Zahovailova. "Energy efficient technologies for the mining industry." Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, no. 6 (December 25, 2022): 11–17. http://dx.doi.org/10.33271/nvngu/2022-6/011.
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The cavitation generator of fluid pressure oscillations is a promising device for productivity and efficiency improvement in the mining industry (hereinafter referred to as the generator). Due to the periodic growth, separation and collapse of cavitation cavities into generator volume, shock pressure oscillations are realized with a frequency range from 1 to 20 kHz. Oscillatory pressure peak values are up by 4 times higher than the steady-state pressure at the generator inlet. The destroyed rock takes on a fatigue character under repeated alternating effects of force impulses. Due to the development of a network of microcracks in the rock, the discontinuity of the rock mass occurs at stresses lower than the rock ultimate strength. This leads to an increase in the rate of penetration, high-quality disintegration of well productive zones and an increase in their production rate, as well as to effective loosening and degassing of outburst-prone coal seams. Purpose. To conduct a systematic analysis of the use of a cavitation generator in the mining industry and evaluate its effectiveness. To develop a simplified method for calculating the maximum values of the range of fluid pressure oscillations by the generator. Methodology. The techniques are based on the study of recent research and publications on the use of the generator as a means of impulse action, and on the processing of on its dynamic parameters experimental data. Findings. The results are given in the form of the main parameters that determine the efficiency of technological processes with hydro pulse exposure. The calculation dependences of values are presented of the cavitation parameter for which of the maximum levels of the fluid oscillation are implemented on the injection pressure and those of the maximum values of the range of fluctuations on the cavitation parameter. Originality. It has been established that the use of the generator as a means of impulse action intensifies the mining industrys technological processes and leads to a significant reduction in specific energy consumption. A new simplified method for calculating the maximum level of the oscillation range has been developed, which makes it possible to determine the rational operation modes of the generator. Practical value. At the stage of designing new equipment or upgrading existing equipment, this simplified method allows determining the effective mode of operation of the generator by engineering methods to reduce the specific energy consumption of the technological process.
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Strong, Danielle, Michael Kay, Thomas Wakefield, Issariya Sirichakwal, Brett Conner, and Guha Manogharan. "Rethinking reverse logistics: role of additive manufacturing technology in metal remanufacturing." Journal of Manufacturing Technology Management 31, no. 1 (August 7, 2019): 124–44. http://dx.doi.org/10.1108/jmtm-04-2018-0119.
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Purpose Although the adoption of metal additive manufacturing (AM) for production has continuously grown, in-house access to production grade metal AM systems for small and medium enterprises (SMEs) is a major challenge due to costs of acquiring metal AM systems, specifically powder bed fusion AM. On the other hand, AM technology in directed energy deposition (DED) has been evolving in both: processing capabilities and adaptable configuration for integration within existing traditional machines that are available in most SME manufacturing facilities, e.g. computer numerical control (CNC) machining centers. Integrating DED with conventional processes such as machining and grinding into Hybrid AM is well suited for remanufacturing of metal parts. The paper aims to discuss these issues. Design/methodology/approach Classical facility location models are employed to understand the effects of SMEs adopting DED systems to offer remanufacturing services. This study identifies strategically located counties in the USA to advance hybrid AM for reverse logistics using North American Industry Classification System (NAICS) data on geographical data, demand, fixed and transportation costs. A case study is also implemented to explore its implications on remanufacturing of high-value parts on the reverse logistics supply chain using an aerospace part and NAICS data on aircraft maintenance, repair and overhaul facilities. Findings The results identify the candidate counties, their allocations, allocated demand and total costs. Offering AM remanufacturing services to traditional manufacturers decreases costs for SMEs in the supply chain by minimizing expensive new part replacement. The hubs also benefit from hybrid AM to repair their own parts and tools. Originality/value This research provides a unique analysis on reverse logistics through hybrid AM focused on remanufacturing rather than manufacturing. Facility location using real data is used to obtain results and offers insights into integrating AM for often overlooked aspect of remanufacturing. The study shows that SMEs can participate in the evolving AM economy through remanufacturing services using significantly lower investment costs.
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Menne, Julia, Astrid Holzheid, and Christopher Heilmann. "Multi-Scale Measurements of Neolithic Ceramics—A Methodological Comparison of Portable Energy-Dispersive XRF, Wavelength-Dispersive XRF, and Microcomputer Tomography." Minerals 10, no. 10 (October 21, 2020): 931. http://dx.doi.org/10.3390/min10100931.
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Archaeometric investigation of ancient pottery with regard to their material composites allows insights into the material structures, production techniques and manufacturing processes. The applied methods depend on the classification of the pottery: some finds should remain unchanged for conservation reasons, other finds are less valuable or more common. While the first group cannot be destroyed for material analyses and the choice of analytical methods is limited, the latter can be investigated using destructive methods and thus can widen the spectrum of possible devices. Multi-element analyses of portable energy-dispersive X-ray fluorescence spectrometry (portable XRF) have become important for archaeological research, as portable XRF provides a quick overview about the chemical composition of potteries and can be used in non-destructive as well as destructive ways in addition to conventional microscopic examination and petrographic thin sections. While most portable XRF analyses of solely fracture surfaces do not provide satisfying results, portable XRF analyses on pulverized samples are a cost-efficient and fast alternative to wavelength-dispersive XRF (WD-XRF). In comparison to WD-XRF, portable XRF on pulverized samples provides reliable concentration data (K, Fe, Rb, Ti, V, Y, Zn, Zr), but other elements need to be corrected. X-ray microtomography (µCT) has proven to be a non-destructive technique to derive not only the porosity of ancient pottery but also to characterize temper components and non-plastic inclusions. Hence, the µCT technique has the potential to extract valuable information needed by archaeologists, for example, to deduce details about manufacturing.
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Kanoun, Olfa, Sabrine Khriji, Slim Naifar, Sonia Bradai, Ghada Bouattour, Ayda Bouhamed, Dhouha El Houssaini, and Christian Viehweger. "Prospects of Wireless Energy-Aware Sensors for Smart Factories in the Industry 4.0 Era." Electronics 10, no. 23 (November 26, 2021): 2929. http://dx.doi.org/10.3390/electronics10232929.
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Advanced sensors are becoming essential for modern factories, as they contribute by gathering comprehensive data about machines, processes, and human-machine interaction. They play an important role in improving manufacturing performance, in-factory logistics, predictive maintenance, supply chains, and digitalization in general. Wireless sensors and wireless sensor networks (WSNs) provide, in this context, significant advantages as they are flexible and easily deployable. They have reduced installation and maintenance costs and contributed by reducing cables and preinstalled infrastructure, leading to improved reliability. WSNs can be retrofitted in machines to provide direct information from inside the processes. Recent developments have revealed exciting possibilities to enhance energy harvesting (EH) and wireless energy transmission, enabling a reliable use of wireless sensors in smart factories. This review provides an overview of the potential of energy aware WSNs for industrial applications and shows relevant techniques for realizing a sustainable energy supply based on energy harvesting and energy transfer. The focus is on high-performance converter solutions and improvement of frequency, bandwidth, hybridization of the converters, and the newest trends towards flexible converters. We report on possibilities to reduce the energy consumption in wireless communication on the node level and on the network level, enabling boosting network efficiency and operability. Based on the existing technologies, energy aware WSNs can nowadays be realized for many applications in smart factories. It can be expected that they will play a great role in the future as an enabler for digitalization in this decisive economic sector.
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Fernandez-Tudela, Elisa, Luis C. Zambrano, Lázaro G. Lagóstena, and Manuel Bethencourt. "Documentación y análisis de un cepo de ancla romano y sus elementos iconográficos y epigráficos sellados." Virtual Archaeology Review 13, no. 26 (January 21, 2022): 147–62. http://dx.doi.org/10.4995/var.2022.15349.
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This paper aims to present the documentation and analysis methodology carried out on a lead trap from the ancient period, which belongs to the collection of traps in the Museum of Cádiz (Andalusia, Spain). The anchor stock had some interesting characteristics for this research. On the one hand, from the point of view of conservation and restoration, due to the alterations it presented. On the other hand, from a historical and archaeological point of view, it showed signs of reliefs on its surface hidden under the alteration products. The removal of the different layers of alteration that covered the surface during conservation and restoration treatments revealed an unpublished iconographic and epigraphic programme, as well as possible marks of use and manufacture. The poor state of conservation of the original surface made it impossible to visualise the details as a whole, so we applied photogrammetric methods, and subsequently processed models using various GIS analysis and point cloud processing softwares.Two photogrammetric models (in Agisoft PhotoScan) were made to document the trap in general: one prior to the conservation and restoration process; and a second three-dimensional (3D) model once the surface had been cleaned. The purpose of the second model was to visualise the reliefs programme in general, as well as the different surface details. The first complete 3D model of the object was used to perform a virtual reconstruction of the anchor including the elements that did not preserve, using a 3D modelling program (Blender).Nine areas of the stock surface were selected for the analyses of the various iconographic and epigraphic features, which were documented and processed in Agisoft PhotoScan. The Digital Elevation Model (DEM) and point cloud models were then processed with different analyses tools in Geographic Information System (GIS) (such as QGIS) and point cloud processing software (CloudCompare). Our results document a piece of highly interesting information from its surface consisting of reliefs of four dolphins; at least four rectangular stamps: two of them with possible inscriptions, and an anthropomorphic figure. Thanks to the comparative data, we conclude that the four dolphins were made with the same stamp during the stock manufacturing process. Further, we were able to reconstruct the dolphin stamp, partially preserved in each of the reliefs, by unifying the 3D models, thus revealing the original set. This system of stamping by means of reusable dies is well known in other elements such as amphorae but has not been studied in the specific case of lead traps.In the case of the epigraphic elements, the 3D documentation methodology revealed numerous micro-surface details, not visible under conventional documentation techniques, which could help specialists to interpret these inscriptions. Although they have not been analysed in this research, its documentation has promoted the appreciation of surface details that could refer to the manufacturing processes (moulds and tools) or the traces of use, providing historical information on this object. At the same time, the virtual reconstruction of the anchor has aided the formation of hypotheses on the dimensions and original appearance of the anchor. The different tools used, such as raster analysis using shadow mapping and point cloud alignment, proved to be very effective. They have fulfilled the established objectives and have helped to establish a possible analysis methodology for future lead traps with decorative elements. These types of artefacts recovered from underwater sites are very common in museum collections. In many cases, their state of conservation and the difficulty in handling them due to their size and weight make it difficult to document surface details. In this case, the multidisciplinary work of conservation and 3D documentation allows for high-quality documentation that is easy to access and exchange between researchers. The combined use of photogrammetric techniques with virtual RTI provides a non-invasive method for the object, low cost and easy processing compared to other conventional methods.