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" Analysis of a Multi-Pick and Place Machine-Flexible Manufacturing Cell Using Stochastic Modeling and Simulation "
Batieha, Farhan Ahmad
Nagarur, Nagendra
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Latin Dissertation
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English
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| Record Number
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1111993
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TLpq2512265415
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| Main Entry
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Batieha, Farhan Ahmad
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Nagarur, Nagendra
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| Title & Author
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Analysis of a Multi-Pick and Place Machine-Flexible Manufacturing Cell Using Stochastic Modeling and Simulation\ Batieha, Farhan AhmadNagarur, Nagendra
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| College
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State University of New York at Binghamton
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| Date
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2020
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| student score
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2020
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| Degree
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Ph.D.
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| Page No
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96
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| Abstract
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Flexibility of manufacturing lines is critical for manufacturing firms; to respond to demand uncertainty and ambiguity and achieve product customization, and better utilization of resources. Uncertainty is a big challenge in flexible manufacturing cell improvements. Addition of more complexity to a Flexible Manufacturing Cell (FMC) and the need to deal with the reality nature of these systems; require more investigation and profound analysis, particularly with respect to better scheduling, higher utilization, and a smooth flow. FMC concept can be used in Surface Mount Technology (SMT) production processes. The growing demand for electronic devices has made the manufacturing of printed circuit boards (PCBs) a promising industry over the last decades. As the demand for printed circuit boards increases, the industry becomes more dependent on highly automated assembly processes. Markov process approach is a sound mathematical approach that can be used to analyze performance of production lines, such as machine(s) utilization, and throughput. In the current research this approach is used to study PCB production lines that use pick and place machines. This research focuses on mathematically modeling FMC consisting of a conveyor, robot and pick and place machine(s) using Markovian stochastic process to address some performance parameters in FMC analysis and understanding system with multi-machines, and buffers, for various failure rates and repair rates. This research is presenting three mathematical models. First, a general model of n-machines each having a buffer with one PCB capacity. Second, a general model of n-machines considering failure and repair rates. Finally, a general model of n-machines having a buffer with one PCB capacity and considering failure and repair rates. The results of these models were illustrated with numerical values to show the performances for one, two and then three machines. As Markov Process models tend to be complicated and are not mathematically tractable for complex models, Arena simulation software was used to address two of the mathematical models. As a Markov process tends to be more restrictive in terms of statistical distributions, simulation was used to get more flexibility than mathematical models, and it was used under different types of statistical distributions (such as normal distribution) for the processing time of the machine, robot and conveyor. Furthermore, simulation was used to solve the optimization problem of selecting the number of boards needed in the buffer to maximize the utilization of the machine.
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Industrial engineering
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Operations research
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