A Modeling and Analysis Framework To Support Monitoring, Assessment, and Control of Manufacturing Systems Using Hybrid Models

The manufacturing industry has constantly been challenged to improve productivity, adapt to continuous changes in demand, and reduce cost. The need for a competitive advantage has motivated research for new modeling and control strategies able to support reconfiguration considering the coupling between different aspects of plant floor operations. However, models of manufacturing systems usually capture the process flow and machine capabilities while neglecting the machine dynamics. The disjoint analysis of system-level interactions and machine-level dynamics limits the effectiveness of performance assessment and control strategies. This dissertation addresses the enhancement of productivity and adaptability of manufacturing systems by monitoring and controlling both the behavior of independent machines and their interactions. A novel control framework is introduced to support performance monitoring and decision making using real-time simulation, anomaly detection, and multi-objective optimization. The intellectual merit of this dissertation lies in (1) the development a mathematical framework to create hybrid models of both machines and systems capable of running in real-time, (2) the algorithms to improve anomaly detection and diagnosis using context-sensitive adaptive threshold limits combined with context-specific classification models, and (3) the construction of a simulation-based optimization strategy to support decision making considering the inherent trade-offs between productivity, quality, reliability, and energy usage. The result is a framework that transforms the state-of-the-art of manufacturing by enabling real-time performance monitoring, assessment, and control of plant floor operations. The control strategy aims to improve the productivity and sustainability of manufacturing systems using multi-objective optimization. The outcomes of this dissertation were implemented in an experimental testbed. Results demonstrate the potential to support maintenance actions, productivity analysis, and decision making in manufacturing systems. Furthermore, the proposed framework lays the foundation for a seamless integration of real systems and virtual models. The broader impact of this dissertation is the advancement of manufacturing science that is crucial to support economic growth. The implementation of the framework proposed in this dissertation can result in higher productivity, lower downtime, and energy savings. Although the project focuses on discrete manufacturing with a flow shop configuration, the control framework, modeling strategy, and optimization approach can be translated to job shop configurations or batch processes. Moreover, the algorithms and infrastructure implemented in the testbed at the University of Michigan can be integrated into automation and control products for wide availability.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147657/1/migsae_1.pd

Analysing approaches to specify automated manufacturing systems

Automating manufacturing systems can achieve competitive advantage leading to growth in profits through efficiency gains and other advantages including safety of workers and quality of produced products. However, without accurate specification there is no guarantee of realising return on investment. Automated systems are becoming more complex as the need for customisability and variability of products increases and can only be satisfied through flexibility of production processes. To aid companies in specifying automation and mitigate the risks of project failure an approach is needed that guides users choices. The aim of the research was to investigate approaches to specify automated manufacturing systems to provide a basis for a methodology that would aid practitioners in this difficult task. The objectives were in two phases. Firstly to categorise and criticise conclusions of other researchers resulting in identification of themes and criteria for an approach. Secondly to experiment empirically with promising approaches in a company producing of automated manufacturing systems (AMS) and compare the results of the experiments with those found in literature and provide a ranking of themes and criteria to aid future researchers in designing new approaches to specify AMS. The methodology used was literature review followed by mini case studies in a host company to test theory. The results from literature and the experiments were classified into four themes quantitative modelling and simulation (QM&S), database decision aids (DDA), flowcharts and consultancy. These were compared using analytical hierarchy process (AHP) against the identified criteria; rapid application, usability by managers, considering costs and benefits other than financial ones, reducing required resources, being applicable to engineer to order products and usable at the early stage of planning. The results were the strengths and weaknesses of each theme defined by the identified criteria and showed that none of the themes fulfilled all of the criteria for an approach to specify AMS. For this reason a hybrid approach was proposed beginning with a flowchart group session to make an outline plan, followed by a database decision aid to provide options and guidance in creating a detailed plan. Finally, an optional simulation stage could test the planned system for suitability. It is hoped that the comparison of approaches will aid future researchers in the creation of new approaches to assist engineers in specifying automated manufacturing systems in a rapidly changing world