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Статті в журналах з теми "Manufacturing processes Energy consumption Data processing"
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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.
Повний текст джерелаАнотація:
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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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.
Повний текст джерелаАнотація:
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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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.
Повний текст джерелаАнотація:
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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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.
Повний текст джерелаАнотація:
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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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.
Повний текст джерелаАнотація:
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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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.
Повний текст джерелаАнотація:
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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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.
Повний текст джерелаАнотація:
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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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.
Повний текст джерелаАнотація:
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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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.
Повний текст джерелаАнотація:
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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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.
Повний текст джерелаАнотація:
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.
Дисертації з теми "Manufacturing processes Energy consumption Data processing"
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Jiang, Sheng. "Processing rate and energy consumption analysis for additive manufacturing processes : material extrusion and powder bed fusion." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/111753.
Повний текст джерелаАнотація:
Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2017.Cataloged from PDF version of thesis.
Includes bibliographical references (pages 111-116).
Additive technologies have given birth to an expanding industry now worth 5.1 billion dollars. It has been adopted widely in design and prototyping as well as manufacturing fields. Compared to conventional technologies, additive manufacturing technologies provides opportunity to print unique complex-shaped geometries. However, it also suffers from slow production rate and high energy consumption. Improving the rate and energy becomes an important issue to make additive manufacturing competitive with conventional technologies. Among all the different limiting factors including printing strategy, heat transfer and mechanical movement limitations, heat transfer is the fundamental limiting barrier preventing further improvement the production rate. This thesis looks at the heat transfer mechanisms in material extrusion and powder bed fusion processes. In all the models developed for these two processes, processing rate is bounded by an adiabatic rate limit at which all the input energy is perfectly utilized to heat up the material to its molten/flowable state. In material extrusion, fused deposition technology suffers low throughput due to poor conductive heat transfer, big area additive manufacturing technology achieves high throughput by introducing viscous heating at the cost of resolution. In powder bed fusion, due to the intensive laser heating, the process window is limited to ensure high density material while avoid excessive evaporation. Further study quantifies the inefficiency from heat transfer mechanisms which leads to lower processing rates than the adiabatic rate limit. Energy consumption for material extrusion and powder bed fusion machines are reviewed to evaluate significance of energy consumed to heat up the material. For fused deposition technology, most of the energy is consumed by environment heating; while for powder bed fusion technology, laser unit takes the most energy. Life cycle energy consumption for products made with powder bed fusion process is compared with same/similar parts made from conventional manufacturing processes to explore scenarios in which manufacturing with additive technologies is less energy intensive.
by Sheng Jiang.
S.M.
Книги з теми "Manufacturing processes Energy consumption Data processing"
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1936-, Hamel Bernard B., and Hedman Bruce A. 1950-, eds. Energy analysis of 108 industrial processes. [Atlanta, Ga: Fairmont Press, 1985.
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Global Innovations Symposium (4th 2003 San Diego, Calif.). Energy efficient manufacturing processes: Proceedings of the technical sessions presented at the 132nd TMS Annual Meeting : San Diego, California, USA, March 2-6, 2003 : TMS Material Processing and Manufacturing Division Global Innovations Symposium. Warrendale, Pa: TMS, 2003.
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1950-, Cipriano Aldo, and Ordys A. W. 1956-, eds. Optimisation of industrial processes at supervisory level: Application to control of thermal power plants. London: Springer, 2002.
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Saez, Doris A., Aldo Cipriano, and Andrzej W. Ordys. Optimisation of Industrial Processes at Supervisory Level: Application to Control of Thermal Power Plants (Advances in Industrial Control). Springer, 2001.
Знайти повний текст джерелаЧастини книг з теми "Manufacturing processes Energy consumption Data processing"
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Mühlbauer, Matthias, Hubert Würschinger, Dominik Polzer, and Nico Hanenkamp. "Energy Profile Prediction of Milling Processes Using Machine Learning Techniques." In Machine Learning for Cyber Physical Systems, 1–11. Berlin, Heidelberg: Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-62746-4_1.
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AbstractThe prediction of the power consumption increases the transparency and the understanding of a cutting process, this delivers various potentials. Beside the planning and optimization of manufacturing processes, there are application areas in different kinds of deviation detection and condition monitoring. Due to the complicated stochastic processes during the cutting processes, analytical approaches quickly reach their limits. Since the 1980s, approaches for predicting the time or energy consumption use empirical models. Nevertheless, most of the existing models regard only static snapshots and are not able to picture the dynamic load fluctuations during the entire milling process. This paper describes a data-driven way for a more detailed prediction of the power consumption for a milling process using Machine Learning techniques. To increase the accuracy we used separate models and machine learning algorithms for different operations of the milling machine to predict the required time and energy. The merger of the individual models allows finally the accurate forecast of the load profile of the milling process for a specific machine tool. The following method introduces the whole pipeline from the data acquisition, over the preprocessing and the model building to the validation.
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Wicaksono, Hendro, Tina Boroukhian, and Atit Bashyal. "A Demand-Response System for Sustainable Manufacturing Using Linked Data and Machine Learning." In Dynamics in Logistics, 155–81. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88662-2_8.
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AbstractThe spread of demand-response (DR) programs in Europe is a slow but steady process to optimize the use of renewable energy in different sectors including manufacturing. A demand-response program promotes changes of electricity consumption patterns at the end consumer side to match the availability of renewable energy sources through price changes or incentives. This research develops a system that aims to engage manufacturing power consumers through price- and incentive-based DR programs. The system works on data from heterogeneous systems at both supply and demand sides, which are linked through a semantic middleware, instead of centralized data integration. An ontology is used as the integration information model of the semantic middleware. This chapter explains the concept of constructing the ontology by utilizing relational database to ontology mapping techniques, reusing existing ontologies such as OpenADR, SSN, SAREF, etc., and applying ontology alignment methods. Machine learning approaches are developed to forecast both the power generated from renewable energy sources and the power demanded by manufacturing consumers based on their processes. The forecasts are the groundworks to calculate the dynamic electricity price introduced for the DR program. This chapter presents different neural network architectures and compares the experiment results. We compare the results of Deep Neural Network (DNN), Long Short-Term Memory Network (LSTM), Convolutional Neural Network (CNN), and Hybrid architectures. This chapter focuses on the initial phase of the research where we focus on the ontology development method and machine learning experiments using power generation datasets.
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Schmitt, Thomas, Pavani Sakaray, Lars Hanson, Matías Urenda Moris, and Kaveh Amouzgar. "Frequent and Automatic Monitoring of Resource Data via the Internet of Things." In Advances in Transdisciplinary Engineering. IOS Press, 2022. http://dx.doi.org/10.3233/atde220127.
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The Internet of Things (IoT) offers potential for developing an intelligent and sustainable manufacturing system, allowing for better and more informed decisions that increase efficiency and cut down waste in production processes. The insights are generated from automatically collected data coming from machines and devices. While process data are already reported and support a close to real-time monitoring and evaluation of process efficiencies, data about resource consumption in manufacturing environments is more scarce but crucial for becoming more resource efficient. Through connected hardware and software applications, data from resource consumption of energy, water, and waste can be automatically collected. To achieve this, this study presents an IoT framework for monitoring resource efficiency in an automatic and frequent manner. Thus, the eco-efficiency and productivity of the process can be measured and integrated into the decision-making processes by sharing the data with shop floor and production management personnel via dashboards.
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M. Fadayini, Oluwafemi, Clement Madu, Taiwo T. Oshin, Adekunle A. Obisanya, Gloria O. Ajiboye, Tajudeen O. Ipaye, Taiwo O. Rabiu, Joseph T. Akintola, Shola J. Ajayi, and Nkechi A. Kingsley. "Energy and Economic Comparison of Different Fuels in Cement Production." In Cement Industry - Optimization, Characterization and Sustainable Application. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.96812.
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Cement clinkerisation is the major energy-consuming process in cement manufacturing due to the high-temperature requirement. In this paper, energy data including specific energy consumption, forms, and types of energy used at different units of cement manufacturing processes were analyzed and compared for effectiveness, availability, cost, environmental, and health impact. Data from three different cement industries in Nigeria labeled as A, B, and C were used for the analysis in this study. The results of this research work established that coal is the cheapest energy source but environmental issues exonerate it from being the choice energy source. LPFO and Natural gas give better production output while minimizing pollution and health issues. When benchmarked against each other, Factory B was found to be the most energy-efficient in terms of output and cost of production. Although coal is cheaper compared to fuel oil and supposed to contribute a share of fuel used in cement industries, the industries are moving towards the use of alternative and conventional fuels to reduce environmental pollution. It is therefore recommended that deliberate effort to achieve appreciable energy-efficient levels should be the priorities of the cement industries in Nigeria.
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Chang, Kuo-Chi, Kai-Chun Chu, Hsiao-Chuan Wang, Yuh-Chung Lin, Tsui-Lien Hsu, and Yu-Wen Zhou. "Study on IoT and Big Data Analysis of 12” 7 nm Advanced Furnace Process Exhaust Gas Leakage." In Linked Open Data - Applications, Trends and Future Developments. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.92849.
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Modern FAB uses a large number of high-energy processes, including plasma, CVD, and ion implantation. Furnaces are one of the important tools for semiconductor manufacturing. According to the requirements of conversion production management, FAB installed a set of IoT-based research based on 12″ 7 nm-level furnaces chip process. Two furnace processing tool measurement points were set up in a 12-inch 7 nm-level factory in Hsinchu Science Park, Taiwan, this is a 24-hour continuous monitoring system, the data obtained every second is sequentially send and stored in the cloud system. This study will be set in the cloud database for big data analysis and decision-making. The lower limit of TEOS, C2H4, CO is 0.4, 1.5, 1 ppm. Semiconductor process, so that IoT integration and big data operations can be performed in all processes, this is an important step to promote FAB intelligent production, and also an important contribution to this research.
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Kuppusamy, Elamvazhuthi, and Kailash Mariappan. "Integration of Operation Technology (OT) and Information Technology (IT) Through Intelligent Automation in Manufacturing Industries." In Advances in Transdisciplinary Engineering. IOS Press, 2021. http://dx.doi.org/10.3233/atde210050.
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The results of integrating OT and IT in Manufacturing Industries are Increase in Productivity, Reduction in Waste, Savings in Labor and Energy and Better Maintenance. The convergence of IT and OT in IoT has been going on for a while and there isn’t a strict division between them in the real world. Traditionally, IT is responsible for creating, storing and securing an organization’s data. At the same time, OT focuses primarily on processes that take place in the physical world-think managing productivity, people, and machinery. There are Pre Design Phases and Final Design Phases for Implementation of the Integration process. Under the Pre Design Phases, Identify the types of Assets in Industrial Zone and those that support Production and then Identify “Who” owns the hardware and software in the asset. In the final Phases of implementation we have: Requirements Phase: Interview all the system owners to gather requirements for operations, configuration and maintenance. Architectural Phase: Produce High level documentation and drawings to meet every requirement. Technical Design Phase: Produce detailed documentation such as drawings, switch configuration and VLAN, IP Address and Firewall ACLs. Implementation Phase: VERIFY “was the product built right?” and VALIDATE “was the right product built?” process. Maintain Phase: Modify configurations and assets to fix anomalies or required operational changes. The Intelligent Automation is Transforming Manufacturing Processes. The explosive growth of the cloud has made on – demand processing more accessible, more efficient and relatively lower cost. Robotic Process Automation (RPA) tools use Cognitive capabilities will replace those that don’t. There are several obvious benefits of automation that can be found in various automation projects as primary positive results. Among others, they include: #Cost Reduction #Higher Accuracy #Increased focus on core competencies #Improved productivity #Better compliance #Creating New jobs #Reducing Employee turnover. Three types of automation in production can be distinguished: 1. Fixed automation, 2. Programmable automation, and 3. Flexible automation. In many industries IT and OT convergence already happens since quite some time (Oil and Gas is just one of the many). Utilities are realizing that to reap the full benefits of advanced metering and smart grid systems, IT and OT must work together. The convergence of IT and OT is about systems, standards and a new way of thinking. We are in the start of Industry 4.0, the industrial internet, cyber – physical systems and evolutions in areas and markets such as Building Management systems, smart metering and critical power.
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Tabaa, Mohamed, Safa Saadaoui, Mouhamad Chehaitly, Aamre Khalil, Fabrice Monteiro, and Abbas Dandache. "Industrial IoT Using Wavelet Transform." In Wavelet Theory. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.93879.
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For many years now, communication in the industrial sector has been characterized by a new trend of integrating the wireless concept through cyber-physical systems (CPS). This emergence, known as the Smart Factory, is based on the convergence of industrial trades and digital applications to create an intelligent manufacturing system. This will ensure high adaptability of production and more efficient resource input. It should be noted that data is the key element in the development of the Internet of Things ecosystem. Thanks to the IoT, the user can act in real time and in a digital way on his industrial environment, to optimize several processes such as production improvement, machine control, or optimization of supply chains in real time. The choice of the connectivity strategy is made according to several criteria and is based on the choice of the sensor. This mainly depends on location (indoor, outdoor, …), mobility, energy consumption, remote control, amount of data, sending frequency and security. In this chapter, we present an Industrial IoT architecture with two operating modes: MtO (Many-to-One) and OtM (One-to-Many). An optimal choice of the wavelet in terms of bit error rate is made to perform simulations in an industrial channel. A model of this channel is developed in order to simulate the performance of the communication architecture in an environment very close to industry. The optimization of the communication systems is ensured by error correcting codes.
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Solona, Olena, and Ihor Kupchuk. "DEVELOPMENT OF A FUNCTIONAL MODEL OF A VIBRATING MILL WITH ADAPTIVE CONTROL SYSTEM OF MODE PARAMETERS." In Modernization of research area: national prospects and European practices. Publishing House “Baltija Publishing”, 2022. http://dx.doi.org/10.30525/978-9934-26-221-0-12.
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The research was supported and funded by the Ministry of Education and Science of Ukraine under grant No. 0121U108589 «Development of a complex of energy-efficient and resource-saving equipment and promising technologies for feeding farm animals of the AIC of Ukraine». The introduction of energy-efficient machines and technologies in the system of feed preparation and animal feeding is an important prerequisite for the development of agriculture. One of the advanced types of grinding technology are vibrating mills, which provide high specific productivity at relatively low energy consumption, adjustable tone of grinding products. Vibration impact on the product significantly increases the shock-absorbing effect with the possibility of wide and separate variation of shock and abrasion factors. Significant speed of mechanical and heat and mass transfer processes, a high degree of homogeneity of the product, the ability to effectively implement fine grinding and dispersion of the product at relatively low energy consumption lead to the widespread use of vibratory grinding.The constructive scheme of the mill is developed, in which the flat vertical vibrating field provides lifting of a part of loading and by means of the transport-reloading device carries out its continuously regulated movement from one grinding chamber to another, thereby circulating-spatial movement of the environment in which grinding shock interaction of grinding bodies and material that is crushed. One of the most important rules for the construction of vibrating mills is the need to maximize the degree of their automation in order to increase productivity, improve the quality of grinding and reduce the cost of the technological process.A constructive model of a controlled vibration mill with spatial-circulating motion was also developed, which constantly changes to the resonant mode of operation at the set technologically optimal parameters (productivity) and minimum energy consumption for vibration when changing the mass of the working body in the process of separation and unloading of crushed material from the grinding chamber.The aim of the study is to establish the dependence of the parameters of the crushed mass along the grinding chambers and in places of overload on the parameters of vibration of the vibration mill of continuous motion.Development of a structural model of adaptive vibration mill with spatial-circulating loading movement which when changing the mass of the working body in the process of separation and unloading of crushed material from the grinding chamber could constantly adapt to resonant mode at given technologically optimal parameters (productivity) and minimum energy consumptionResearch methods. Theoretical and experimental research methods were used in the work. Experiment planning and regression analysis methods were used in conducting experiments and processing experimental data. Verification of the adequacy of the obtained dependences with experimental data was carried out by methods of mathematical statistics.Scientific novelty: the theory and practice of vibration mechanics were further developed, in particular, the conditions of vertical lifting of the loading part in vibrating mills with a U-shaped chamber were determined and the influence of the main factors on the lifting height was studied; for the first time the scheme of the vibrating mill with spatial-circulating loading movement is developed, in which the effect of lifting of loading is used and by means of the transport-technological device reloading in the interconnected chambers is carried out.Practical significance. The conditions and parameters of the vibration field that regulate the intensity and duration of grinding are determined. The dependence of the productivity Q of a vibrating mill on the velocity of transporting the loaded mass along the grinding chamber has been established. The structure and two-circuit principle of control of work of adaptive vibration mill with spatial-circulating movement of loading are offered.
Тези доповідей конференцій з теми "Manufacturing processes Energy consumption Data processing"
1
Muroyama, Alexander, Mahesh Mani, Kevin Lyons, and Bjorn Johansson. "Simulation and Analysis for Sustainability in Manufacturing Processes." In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-47327.
Повний текст джерелаАнотація:
“Sustainability” has become a ubiquitous term in almost every field, especially in engineering design and manufacturing. Recently, an increased awareness of environmental problems and resource depletion has led to an emphasis on environmentally friendly practices. This is especially true in the manufacturing industry where energy consumption and the amount of waste generated can be high. This requires proactive tools to be developed to carefully analyze the cause-effect of current manufacturing practices and to investigate alternative practices. One such approach to sustainable manufacturing is the combined use of Discrete Event Simulation (DES) and Life Cycle Assessment (LCA) to analyze the utilization and processing of manufacturing resources in a factory setting. On an economic aspect such method can significantly reduce the financial and environmental costs by evaluating the system performance before its construction or use. This project considers what-if scenarios in a simplified golf ball factory, using as close to real-world data as possible, to demonstrate DES and LCA’s ability to facilitate decision-making and optimize the manufacturing process. Plastic injection molding, an energy-intensive step in the golf ball manufacturing process, is the focus of the DES model. AutoMod, a 3-D modeling software, was used to build the DES model and AutoStat was used to run the trials and analyze the data. By varying the input parameters such as type and number of injection molding machines and material used, the simulation model can output data indicating the most productive and energy efficient methods. On a more detailed level, the simulations can provide valuable information on bottlenecks or imbalances in the system. Correcting these can allow the factory to be both “greener” and more cost-effective.
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Wang, Xingtao, Robert E. Williams, Michael P. Sealy, Prahalada Rao, and Yuebin Guo. "Stochastic Modeling and Analysis of Spindle Energy Consumption During Hard Milling With a Focus on Tool Wear." In ASME 2018 13th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/msec2018-6511.
Повний текст джерелаАнотація:
The rapid development of modern science and technology brings with it a high demand for manufacturing quality. The surface integrity of a machined part is a critical factor which needs to be considered in the selection of the appropriate machining processes. By monitoring and predicting tool wear, it is possible to improve sustainability by reducing the scrap rate due to poor surface integrity. In this work, Data Dependent Systems (DDS), a stochastic modeling and analysis technique, was applied to study spindle motor energy consumption during a hard milling operation. The objective was to correlate the spindle power to tool wear conditions using DDS analysis. The spindle power was monitored and the time series trends were decomposed to study the frequency variation with different severities of tool wear conditions and processing parameters. Analysis of Variance (ANOVA) was also used to determine factors significant to the energy consumption by a spindle motor. Experiments indicate that low-level frequency of spindle power is correlated with the amount of tool wear, cutting speed, and feed per tooth. Results suggest that effective tool wear monitoring may be achieved by focusing on low-level frequencies (0.1 rad/sec) highlighted by DDS methodology.
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Bharambe, Ganesh, Prakash Dabeer, Kumar Digambar Sapate, and Suresh M. Sawant. "Energy Savings for Sustainability of Machining Process." In ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/imece2015-53295.
Повний текст джерелаАнотація:
Processing of metals in industries is lifeline of economy of country, which helps to shape the country. Energy saving in this process is attributed to both the parts ie process of machining and energy consumed in machine tools itself. The process of material removal had experienced lot of improvements in last few decades. This consists of developments in pre-machining processes, metal cutting methods and developments in cutting theories and cutting tools. Cutting fluid is one of challenging field to yield more favourable results. Manufacturing practices beyond its existing limits, process and machine automations, using the previous data for improving machinabilities, optimizing through relative benchmarks (a market driven schemes) shall lead the manufacturing speed to a new high. Adaptibility of manufacturing set up to absorb new requirement will also be a controlling factor for acceleration of manufacturing processes. This paper discusses the efforts to reduce the energy to produce a product. Various methods are discussed to minimize the energy consumed for driving the machine components such as spindle, feeding device, lubricating system, cutting fluid system, indexing and tooling management, speed and feed controlling devices etc. Different requirements such as friction energy in braking action, speed reducing or cushioning will also consume certain amount of energy during its operations. Therefore one has to understand the various types of energy flows and classification of energy forms used from place to place. Study of constructional features of machines brings a lot of opportunities for savings in energy. The concepts of material handling, fluid handling like hydraulic and pneumatic circuits, lubrication system, shall also provide the opportunities for savings in energy consumption. Energy used for working of accessories whether they are required at that particular moments needs to be considered from time to time. There are few more methods for locating the chances for arresting the energy wastages and reducing specific energy consumption referring a particular process or function. Previous data generated for similar functions can be referred for comparison and efforts can be added to reduce the requirement of energy. Efficient and effective utilization of equipment shall open a fresh path for finding the energy reductions. Sustainability of machining processes can be ensured for future using the lean energy utilizations for productions. Authors have explained the live cases to demonstrate reduction in energy consumption. Few potential guidelines are also narrated in this line. Further few cases are discussed from literature survey which support and will help to pursue the target.
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Nordlund, Alec, Rachel McAfee, Rebecca Ledsham, and Joshua Gess. "Cooling of High Powered GPUs Using Liquid Nitrogen Cold Plates Made With Additive Manufacturing." In ASME 2021 International Technical Conference and Exhibition on Packaging and Integration of Electronic and Photonic Microsystems. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/ipack2021-74108.
Повний текст джерелаАнотація:
Abstract Processor energy density is exceeding the capabilities of conventional air-cooling technology, but two-phase cooling has the potential to manage these resulting heat fluxes at reliable temperatures and higher electrical efficiency. When two-phase cooling is used in tandem with overclocking, data center footprints are reduced as individual chip processing power can be set at limits well beyond the manufacturer’s Thermal Design Power (TDP) or nominal operating condition. This study examines how Liquid Nitrogen (LN2) can be used with Additive Manufacturing (AM) and overclocking to increase the computational performance of a commercially available GPU. The power consumption and frequency relationship were established for both the cryogenically cooled solution and a comparative air-cooled solution. The cryogenic solution saw up to a 17.4% increase in compute efficiency and an 18.1% improvement in compute speed with comparable power efficiency at an equivalent performance level to the air-cooled solution. This study considers the computational performance and efficiency gains that can be acquired through cryogenic cooling on an individual graphics card, which can be replicated on a larger scale in data center applications.
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Fehrenbacher, Axel, Joshua R. Schmale, Michael R. Zinn, and Frank E. Pfefferkorn. "Tool-Workpiece Interface Temperature Measurement in Friction Stir Welding." In ASME 2012 International Manufacturing Science and Engineering Conference collocated with the 40th North American Manufacturing Research Conference and in participation with the International Conference on Tribology Materials and Processing. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/msec2012-7326.
Повний текст джерелаАнотація:
The objectives of this work are to develop an improved temperature measurement system for Friction Stir Welding (FSW). FSW is a novel joining technology enabling welds with excellent metallurgical and mechanical properties, as well as significant energy consumption and cost savings compared to traditional fusion welding processes. The measurement of temperatures during FSW is employed for process monitoring, heat transfer model verification and process control, but current methods have limitations due to their restricted spatial and temporal resolution and have found only few industrial applications so far. Thermocouples, which are most commonly used, are either placed too far away from the weld zone or are destructively embedded into the weld path, and therefore fail to provide suitable data about the dynamic thermal phenomena at the tool-workpiece interface. Previous work showed that temperatures at the tool shoulder-workpiece interface can be measured and utilized for closed-loop control of temperature. The method is improved by adding an additional thermocouple at the tool pin-workpiece interface to gain better insight into the temperature distribution in the weld zone. Both thermocouples were placed in through holes right at the interface of tool and workpiece so that the sheaths are in contact with the workpiece material. This measurement strategy reveals dynamic temperature variations at the shoulder and the pin within a single rotation of the tool in real-time. Due to the thermocouple’s limited response time and inherent delays due to physical heat conduction, the temperature response is experiencing attenuation in magnitude and a phase lag. Heat transfer models were constructed to correct for this issue. It was found that the highest temperatures are between the advancing side and the trailing edge of the tool. Further work is needed to increase the accuracy of the correction. Experimental results show that the weld quality is sensitive to the measured interface temperatures, but that temperature is not the only factor influencing the weld quality. The dynamic temperature measurements obtained with the current system are of unmatched resolution, fast and reliable and are likely to be of interest for both fundamental studies and process control of FSW.
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Kalla, Devi K., Samantha Corcoran, Janet Twomey, and Michael Overcash. "Energy Consumption in Discrete Part Production." In ASME 2011 International Manufacturing Science and Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/msec2011-50163.
Повний текст джерелаАнотація:
It is widely recognized that industrial production inevitably results in an environmental impact. Energy consumption during production is responsible for a part of this impact, but is often not provided in cradle-to-gate life cycles. Transparent description of the transformation of materials, parts, and chemicals into products is described herein as a means to improve the environmental profile of products and manufacturing machine. This paper focuses on manufacturing energy and chemicals/materials required at the machine level and provides a methodology to quantify the energy consumed and mass loss for simple products in a manufacturing setting. That energy data are then used to validate the new approach proposed by (Overcash et.al, 2009a, and 2009b) for drilling unit processes. The approach uses manufacturing unit processes as the basis for evaluating environmental impacts at the manufacturing phase of a product’s life cycle. Examining manufacturing processes at the machine level creates an important improvement in transparency which aids review and improvement analyses.
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Song, Ruoyu, Yanglong Lu, Cassandra Telenko, and Yan Wang. "Manufacturing Energy Consumption Estimation Using Machine Learning Approach." In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-67679.
Повний текст джерелаАнотація:
Environmental impacts of manufacturing are often significant and influenced by part and process parameters. Energy consumption is one of the most critical factors for the overall environmental impact of manufacturing. To achieve energy reduction, one must estimate the manufacturing energy consumption throughout the design stage. This paper presents an efficient data-driven approach to utilize machine learning to estimate energy consumption of a manufacturing process from a CAD model. The approach enables quick cost estimation with limited knowledge about the exact process parameters. A case study of fused deposition modeling is used to illustrate the feasibility of this framework and test potential regression methods. Lasso and elastic net regressions were compared in this study. The potential application of this framework to other manufacturing processes is also discussed.
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Sun, Zeyi, Donghai Wang, Lin Li, and Meng Zhang. "Relationship Investigation Between Energy Consumption and Parameters in Size Reduction and Pelleting Processes of Biofuel Manufacturing." In ASME 2014 International Manufacturing Science and Engineering Conference collocated with the JSME 2014 International Conference on Materials and Processing and the 42nd North American Manufacturing Research Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/msec2014-4010.
Повний текст джерелаАнотація:
Biofuel is considered a sustainable substitute for traditional liquid transportation fuels. The wide adoption of biofuel can effectively reduce the greenhouse gas emissions and secure the energy supply of the U.S. One major concern of the wide adoption of biofuel is the energy consumption during biofuel manufacturing processes, which plays a critical role in successful substitution. In this paper, we focused on the investigations of the relationships between the energy consumption and process parameters of the processes on size reduction and ultrasonic vibration-assisted pelleting. The methodology of design of experiments was used to analyze the experimental results of energy consumptions with different process parameter settings of the two processes. Critical parameters that significantly influence the energy consumption were identified. The optimal configurations of the process parameters were recommended.
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Muhammad, Moin, Saja Al Balushi, and Carrie Murtland. "Harvesting Geothermal Energy from Produced Reservoir Fluids Eliminates CO2 Emission from Production Facility Operations." In International Petroleum Technology Conference. IPTC, 2022. http://dx.doi.org/10.2523/iptc-22313-ea.
Повний текст джерелаАнотація:
Abstract Objective ICE thermal Harvesting has developed a patented technology to convert neglected thermal energy existing in producing oil and gas wells to 100% emissions free electrical power to fulfil in-field power needs and improve operators’ emissions profile. By leveraging advanced process design and automation, heat is harvested and converted to electricity which is then safely delivered to local equipment, the grid, or energy storage fields. During production of oil and gas from high-temperature, high-pressure formations, reservoir fluids are sent through a surface choke reducing the pressure prior to flowing to surface production equipment and pipelines. Flowing pressures before a choke can be as high as 10,000 psi and will most commonly be reduced to pressures below 1,400 psi. This pressure regulation is critical to both limit unmitigated flow from the well, optimizing the ultimate recovery from the reservoir, as well as to protect surface assets from potentially damaging flowing pressures. However, as the flowing pressure is reduced, the temperature as a result also drops significantly and the thermal energy is lost. Additionally, due to the depth of many of these producer wells, the fluid being produced from the subterranean reservoirs contain large amounts of thermal energy. Currently, this thermal energy is unutilized because there is no existing methodology or technology to effectively capture this thermal energy or convert it to electrical power. Based on the EIA estimates, there are roughly 900,000 producing wells across US lands and waters. From conservative initial ICE estimates, at least 7,500 of these well sites have the potential to be utilized for this application. With electric power rates of ICE packages varying from 125kW to 210kW, this would equate to 937,500 MW to 1,575,000 MW of emissions free power production for consumption within the United States. Contrary to previous past projects exploring similar technologies aiming to utilize oil and gas wells as geothermal reserviors, the requirement of continuously pumping large volumes of fresh water downhole is eliminated by utilizing producing wells instead of reconditioning de-commissioned wells. Because the wells are already producing, the ICE system relies on the reservoir pressure or others production lift mechnism to push the oil and gas stream back to surface, rather than pumping large volumes of fluid downhole to recover the geothermal energy. The benefit of this is reducing the parasitic loads imposed by pumping fluid downhole, ultimately improving net power output by over 50%. ICE's innovations to date have been primarily centered around the harvesting of one or more heat sources, aggregating those heat sources in an optimal manner through a patented process loop, and modulating heat transfer through automated control methods. This controlled thermal product is then transferred to the Organic Rankine Cycle generator portion of the system for conversion to electricity. Building upon decades of experience in the electrification of oilfield services, ICE engineers designed the system to be highly mobile, modular, and scalable to comply with the demands of remote oilfield operations. Contrary to other heat-to-power systems, the ICE system does not necessitate civil infrastructure work or the employment of EPC firms to install. ICE systems are planned to be installed in processes spanning several industrial spaces including cement manufacturing, power production, and industrial manufacturing; anywhere large industrial cooling is required, there exists opportunity to implement ICE technology. The initial strong interest from oil and gas operators has caused the initial deployments to focus on the energy sector. These applications are found across the oil and gas value chain, ranging from upstream, midstream, and downstream processes. For this overview, two ICE system applications will be described. For the first application, thermal energy will be harvested from aggregated oil production from 11 conventional wells. As liquid production is aggregated in-field and routed toward initial processing, the production stream will flow though ICE Thermal Harvesting's system, where heat will be extracted from the stream. The second application will harvest thermal energy from natural gas wells. In this application, hot, high- pressure gas from two wells will flow through the ICE system in the vicinity of the wellhead where flowing pressures are still high. Wellhead temperatures of these wells are greater than 230 degrees Fahrenheit. The ICE system is expected to have a cooling impact of over 40 degrees Fahrenheit on the gas stream during the power production process, which will greatly reduce the cooling duty required on location. Both projects will be executed in three phases: Phase 1: Assessing the feasibility of power production from subject assets by evaluating production dataPhase 2: Utilizing the measured heat within the subject assets, ICE will finalize engineering design on heat exchange equipment best suited to harvest the maximum amount of thermal energy from production streams.Phase 3: Critical parameters will be continuously monitored remotely. Optimization engineering to be performed to maximize power production from the system to achieve as close to 125kW nameplate output as possible.
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Ma, Junfeng, Wenmeng Tian, and Morteza Alizadeh. "Data Driven Modeling and Optimization for Energy Efficiency in Additive Manufacturing Process With Geometric Accuracy Consideration." In ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/detc2018-85642.
Повний текст джерелаАнотація:
Despite of its tremendous merits in producing parts with complex geometry and functionally graded materials, additive manufacturing (AM) is inherently an energy expensive process. Prior studies have shown that process parameters, such as printing resolution, printing speed, and printing temperature, are correlated to energy consumption per part. Moreover, part geometric accuracy is another major focus in AM research, and extensive studies have shown that the geometric accuracy of final parts is highly dependent on those process parameters as well. Though both energy consumption and part geometric accuracy heavily depend on the process parameters in AM processes, jointly considering the dual outputs in AM process is not fully investigated. The proposed study aims to obtain a quantitative understanding of the impact of these process parameters on AM energy consumption given part quality requirements, such as geometric accuracy. The study utilizes a MakerGear M2 fused deposition modeling (FDM) 3D printer to complete the designed experiments. By implementing experimental design and statistical regression analysis technologies, the study quantifies the correlation between AM process parameters and energy consumption as well as the final geometric accuracy measure. An optimization framework is proposed to minimize the energy consumption per part. The Kuhn-Tucker non-linear optimization algorithm is used to identify the optimal process parameters. This study is of significance to AM energy consumption in terms of jointly considering energy consumption and final part geometric accuracy in the optimization framework.
Звіти організацій з теми "Manufacturing processes Energy consumption Data processing"
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Wada, Yasutaka. Working Paper PUEAA No. 3. Parallel Processing and Parallelizing Compilation Techniques for "Green Computing". Universidad Nacional Autónoma de México, Programa Universitario de Estudios sobre Asia y África, 2022. http://dx.doi.org/10.22201/pueaa.001r.2022.
Повний текст джерелаАнотація:
The fourth technological revolution has brought great advances in manufacturing processes and human communications. Although processors have become increasingly efficient, both in speed, capacity and energy consumption, their functionality regarding this last point has yet to improve. The latest innovations represent an opportunity to create "green computing" and not only more environmentally friendly electronics and software, but also to use their new efficiency to improve our daily activities, as well as the designs of our cities themselves to make them more environmentally sustainable. These new computerized systems must also be applied in accordance with the socioeconomic factors that must be taken into account in order to be modified in favor of sustainability and efficiency.