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Статті в журналах з теми "091099 Manufacturing Engineering not elsewhere classified"
Farid, A. M., and D. C. McFarlane. "Production degrees of freedom as manufacturing system reconfiguration potential measures." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 222, no. 10 (October 1, 2008): 1301–14. http://dx.doi.org/10.1243/09544054jem1056.
Повний текст джерелаДисертації з теми "091099 Manufacturing Engineering not elsewhere classified"
Zhang, Yumin. "Virtual manufacturing - a study of some important issues relating to the transformation of traditional manufacturing organisations." Thesis, Aston University, 2006. http://publications.aston.ac.uk/12237/.
Повний текст джерела(6661946), Jeremy Wayne Byrd. "PREDICTIVE MAINTENANCE PRACTICES & STANDARDS." 2019.
Знайти повний текст джерелаManufacturing today is increasingly competitive and every organization around the world is looking to decrease costs. Maintenance costs generated an average of 28 percent of total manufacturing cost at the Fiat Chrysler Indiana Transmission Plant One in 2018, states Rex White, Head Maintenance Planner at Fiat Chrysler (2018). Maintenance is a supportive expense that does not generate a profit, which makes maintenance an attractive expense to decrease. The cost for components and skilled labor are expensive; however, the downtime is exponentially a larger threat to production cost. One most feared scenarios within a manufacturing facility is that one machine takes down several as it backs up the entire production process.
The three major types of maintenance are reactive, preventive, and
predictive. The research project focused on applying the principles of
predictive maintenance to the Fiat Chrysler facilities in Indiana. The report explains
the techniques and principles of applying the technology currently available to
reduce downtime and maintenance cost. The predictive maintenance procedures and
saving are compared with reactive and preventive methods to determine a value
of return. The report will examine the benefits of using the Internet of Things
technology to create autonomous self-diagnosing smart machines. The predictive
maintenance plan in this research illustration will introduce health check
equipment used to implement longer lasting machine components. In conclusion,
the project developed out an entire predictive maintenance plan to reduce
downtime and maintenance costs.
(8740677), Jeremy Sickmiller. "REAL TIME CONTROL OF MANUFACTURING UTILIZING A MANUFACTURING EXECUTION SYSTEM (MES)." Thesis, 2020.
Знайти повний текст джерела(8065976), Kanjakha Pal. "Process Intensification Enabling Direct Compression for Pharmaceutical Manufacturing: From Spherical Agglomeration to Precise Control of Co-Agglomeration." Thesis, 2019.
Знайти повний текст джерелаHsiao, Yu-Chan Helen. "A framework of university incubator to maintain financial sustainability." 2008. http://arrow.unisa.edu.au:8081/1959.8/42985.
Повний текст джерела(5930069), Mariana Moreno. "Robust Process Monitoring for Continuous Pharmaceutical Manufacturing." Thesis, 2019.
Знайти повний текст джерелаRobust process monitoring in real-time is a challenge for Continuous Pharmaceutical Manufacturing. Sensors and models have been developed to help to make process monitoring more robust, but they still need to be integrated in real-time to produce reliable estimates of the true state of the process. Dealing with random and gross errors in the process measurements in a systematic way is a potential solution. In this work, we present such a systematic framework, which for a given sensor network and measurement uncertainties will predict the most likely state of the process. As a result, real-time process decisions, whether for process control, exceptional events management or process optimization can be based on the most reliable estimate of the process state.
Data reconciliation (DR) and gross error detection (GED) have been developed to accomplish robust process monitoring. DR and GED mitigate the effects of random measurement errors and non-random sensor malfunctions. This methodology has been used for decades in other industries (i.e., Oil and Gas), but it has yet to be applied to the Pharmaceutical Industry. Steady-state data reconciliation (SSDR) is the simplest forms of DR but offers the benefits of short computational times. However, it requires the sensor network to be redundant (i.e., the number of measurements has to be greater than the degrees of freedom).
In this dissertation, the SSDR framework is defined and implemented it in two different continuous tableting lines: direct compression and dry granulation. The results for two pilot plant scales via continuous direct compression tableting line are reported in this work. The two pilot plants had different equipment and sensor configurations. The results for the dry granulation continuous tableting line studies were also reported on a pilot-plant scale in an end-to-end operation. New measurements for the dry granulation continuous tableting line are also proposed in this work.
A comparison is made for the model-based DR approach (SSDR-M) and the purely data-driven approach (SSDR-D) based on the use of principal component constructions. If the process is linear or mildly nonlinear, SSDR-M and SSDR-D give comparable results for the variables estimation and GED. The reconciled measurement values generate using SSDR-M satisfy the model equations and can be used together with the model to estimate unmeasured variables. However, in the presence of nonlinearities, the SSDR-M and SSDR-D will differ. SSDR successfully estimates the real state of the process in the presence of gross errors, as long as steady-state is maintained and the redundancy requirement is met. Gross errors are also detected whether using SSDR-M or SSDR-D.
(9136835), Sungbum Jun. "SCHEDULING AND CONTROL WITH MACHINE LEARNING IN MANUFACTURING SYSTEMS." Thesis, 2020.
Знайти повний текст джерела(9726050), Onkar V. Sonur. "The Sustainable Manufacturing System Design Decomposition." Thesis, 2020.
Знайти повний текст джерела(8922227), Mohamadreza Moini. "BUILDABILITY AND MECHANICAL PERFORMANCE OF ARCHITECTURED CEMENT-BASED MATERIALS FABRICATED USING A DIRECT-INK-WRITING PROCESS." Thesis, 2020.
Знайти повний текст джерелаAdditive Manufacturing (AM) allows for the creation of elements with novel forms and functions. Utilizing AM in development of components of civil infrastructure allows for achieving more advanced, innovative, and unique performance characteristics. The research presented in this dissertation is focused on development of a better understanding of the fabrication challenges and opportunities in AM of cement-based materials. Specifically, challenges related to printability and opportunities offered by 3D-printing technology, including ability to fabricate intricate structures and generate unique and enhanced mechanical responses have been explored. Three aspects related to 3D-printing of cement-based materials were investigated. These aspects include: fresh stability of 3D-printed elements in relation to materials rheological properties, microstructural characteristics of the interfaces induced during the 3D-printing process, and the mechanical response of 3D-printed elements with bio-inspired design of the materials’ architecture. This research aims to contribute to development of new pathways to obtain stability in freshly 3D-printed elements by determining the rheological properties of material that control the ability to fabricate elements in a layer-by-layer manner, followed by the understanding of the microstructural features of the 3D-printed hardened cement paste elements including the interfaces and the pore network. This research also introduces a new approach to enhance the mechanical response of the 3D-printed elements by controlling the spatial arrangement of individual filaments (i.e., materials’ architecture) and by harnessing the weak interfaces that are induced by the 3D-printing process.
(10695907), Wo Jae Lee. "AI-DRIVEN PREDICTIVE WELLNESS OF MECHANICAL SYSTEMS: ASSESSMENT OF TECHNICAL, ENVIRONMENTAL, AND ECONOMIC PERFORMANCE." Thesis, 2021.
Знайти повний текст джерелаOne way to reduce the lifecycle cost and environmental impact of a product in a circular economy is to extend its lifespan by either creating longer-lasting products or managing the product properly during its use stage. Life extension of a product is envisioned to help better utilize raw materials efficiently and slow the rate of resource depletion. In the case of manufacturing equipment (e.g., an electric motor on a machine tool), securing reliable service life as well as the life extension are important for consistent production and operational excellence in a factory. However, manufacturing equipment is often utilized without a planned maintenance approach. Such a strategy frequently results in unplanned downtime, owing to unexpected failures. Scheduled maintenance replaces components frequently to avoid unexpected equipment stoppages, but increases the time associated with machine non-operation and maintenance cost.
Recently, the emergence of Industry 4.0 and smart systems is leading to increasing attention to predictive maintenance (PdM) strategies that can decrease the cost of downtime and increase the availability (utilization rate) of manufacturing equipment. PdM also has the potential to foster sustainable practices in manufacturing by maximizing the useful lives of components. In addition, advances in sensor technology (e.g., lower fabrication cost) enable greater use of sensors in a factory, which in turn is producing greater and more diverse sets of data. Widespread use of wireless sensor networks (WSNs) and plug-and-play interfaces for the data collection on product/equipment states are allowing predictive maintenance on a much greater scale. Through advances in computing, big data analysis is faster/improved and has allowed maintenance to transition from run-to-failure to statistical inference-based or machine learning prediction methods.
Moreover, maintenance practice in a factory is evolving from equipment “health management” to equipment “wellness” by establishing an integrated and collaborative manufacturing system that responds in real-time to changing conditions in a factory. The equipment wellness is an active process of becoming aware of the health condition and of making choices that achieve the full potential of the equipment. In order to enable this, a large amount of machine condition data obtained from sensors needs to be analyzed to diagnose the current health condition and predict future behavior (e.g., remaining useful life). If a fault is detected during this diagnosis, a root cause of a fault must be identified to extend equipment life and prevent problem reoccurrence.
However, it is challenging to build a model capturing a relationship between multi-sensor signals and mechanical failures, considering the dynamic manufacturing environment and the complex mechanical system in equipment. Another key challenge is to obtain usable machine condition data to validate a method.
A goal of the proposed work is to develop a systematic tool for maintenance in manufacturing plants using emerging technologies (e.g., AI, Smart Sensor, and IoT). The proposed method will facilitate decision-making that supports equipment maintenance by rapidly detecting a worn component and estimating remaining useful life. In order to diagnose and prognose a health condition of equipment, several data-driven models that describe the relationships between proxy measures (i.e., sensor signals) and machine health conditions are developed and validated through the experiment for several different manufacturing-oriented cases (e.g., cutting tool, gear, and bearing). To enhance the robustness and the prediction capability of the data-driven models, signal processing is conducted to preprocess the raw signals using domain knowledge. Through this process, useful features from the large dataset are extracted and selected, thus increasing computational efficiency in model training. To make a decision using the processed signals, a customized deep learning architecture for each case is designed to effectively and efficiently learn the relationship between the processed signals and the model’s outputs (e.g., health indicators). Ultimately, the method developed through this research helps to avoid catastrophic mechanical failures, products with unacceptable quality, defective products in the manufacturing process as well as to extend equipment service life.
To summarize, in this dissertation, the assessment of technical, environmental and economic performance of the AI-driven method for the wellness of mechanical systems is conducted. The proposed methods are applied to (1) quantify the level of tool wear in a machining process, (2) detect different faults from a power transmission mini-motor testbed (CNN), (3) detect a fault in a motor operated under various rotation speeds, and (4) to predict the time to failure of rotating machinery. Also, the effectiveness of maintenance in the use stage is examined from an environmental and economic perspective using a power efficiency loss as a metric for decision making between repair and replacement.