Matching Items (2)
Filtering by
- All Subjects: Production systems
- Creators: Ju, Feng
Description
Recent advances in manufacturing system, such as advanced embedded sensing, big data analytics and IoT and robotics, are promising a paradigm shift in the manufacturing industry towards smart manufacturing systems. Typically, real-time data is available in many industries, such as automotive, semiconductor, and food production, which can reflect the machine conditions and production system’s operation performance. However, a major research gap still exists in terms of how to utilize these real-time data information to evaluate and predict production system performance and to further facilitate timely decision making and production control on the factory floor. To tackle these challenges, this dissertation takes on an integrated analytical approach by hybridizing data analytics, stochastic modeling and decision making under uncertainty methodology to solve practical manufacturing problems.
Specifically, in this research, the machine degradation process is considered. It has been shown that machines working at different operating states may break down in different probabilistic manners. In addition, machines working in worse operating stage are more likely to fail, thus causing more frequent down period and reducing the system throughput. However, there is still a lack of analytical methods to quantify the potential impact of machine condition degradation on the overall system performance to facilitate operation decision making on the factory floor. To address these issues, this dissertation considers a serial production line with finite buffers and multiple machines following Markovian degradation process. An integrated model based on the aggregation method is built to quantify the overall system performance and its interactions with machine condition process. Moreover, system properties are investigated to analyze the influence of system parameters on system performance. In addition, three types of bottlenecks are defined and their corresponding indicators are derived to provide guidelines on improving system performance. These methods provide quantitative tools for modeling, analyzing, and improving manufacturing systems with the coupling between machine condition degradation and productivity given the real-time signals.
Specifically, in this research, the machine degradation process is considered. It has been shown that machines working at different operating states may break down in different probabilistic manners. In addition, machines working in worse operating stage are more likely to fail, thus causing more frequent down period and reducing the system throughput. However, there is still a lack of analytical methods to quantify the potential impact of machine condition degradation on the overall system performance to facilitate operation decision making on the factory floor. To address these issues, this dissertation considers a serial production line with finite buffers and multiple machines following Markovian degradation process. An integrated model based on the aggregation method is built to quantify the overall system performance and its interactions with machine condition process. Moreover, system properties are investigated to analyze the influence of system parameters on system performance. In addition, three types of bottlenecks are defined and their corresponding indicators are derived to provide guidelines on improving system performance. These methods provide quantitative tools for modeling, analyzing, and improving manufacturing systems with the coupling between machine condition degradation and productivity given the real-time signals.
ContributorsKang, Yunyi (Author) / Ju, Feng (Thesis advisor) / Pedrielli, Giulia (Committee member) / Wu, Teresa (Committee member) / Yan, Hao (Committee member) / Arizona State University (Publisher)
Created2020
Description
A production system is commonly restricted by time windows. For example, perishability is a major concern in food processing and requires products, such as yogurt, beer and meat, not to stay in buffer for long. Semiconductor manufacturing is faced with oxidation and moisture absorption issues, if a product in buffer is exposed to air for long. Machine reliability is a major source of uncertainty in production systems that causes residence time constraints to be unsatisfied, leading to potential product quality issues. Rapid advances in sensor technology and automation provide potentials to manage production in real time, but the system complexity, brought by residence time constraints, makes it difficult to optimize system performance while providing a guaranteed product quality. To contribute to this end, this dissertation is dedicated to modeling, analysis and control of production systems with constrained time windows. This study starts with a small-scale serial production line with two machines and one buffer. Even the simplest serial lines could have too large state space due to the consideration of residence time constraints. A Markov chain model is developed to approximately analyze its transient behavior with a high accuracy. An iterative learning algorithm is proposed to perform real-time control. The analysis of two-machine serial line contributes to the further analysis of more general and complex serial lines with multiple machines. Residence time constraints can be required in multiple stages. To deal with it, a two-machine-one-buffer subsystem isolated from a multi-stage serial production line is firstly analyzed and then acts as a building block to support the aggregation method for overall system performance. The proposed aggregation method substantially reduces the complexity of the problem while maintaining a high accuracy. A decomposition-based control approach is proposed to control a multi-stage serial production line. A production system is decomposed into small-scale subsystems, and an iterative aggregation procedure is then used to generate a production control policy. The decomposition-based control approach outperforms general-purpose reinforcement learning method by delivering significant system performance improvement and substantial reduction on computation overhead. Semiconductor assembly line is a typical production system, where products are restricted by time windows and production can be disrupted by machine failures. A production control problem of semiconductor assembly line is presented and studied, and thus total lot delay time and residence time constraint violation are minimized.
ContributorsWang, Feifan (Author) / Ju, Feng (Thesis advisor) / Askin, Ronald (Committee member) / Mirchandani, Pitu (Committee member) / Patel, Nital (Committee member) / Arizona State University (Publisher)
Created2021