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

A modular hybrid simulation framework for complex manufacturing system design

For complex manufacturing systems, the current hybrid Agent-Based Modelling and Discrete Event Simulation (ABM–DES) frameworks are limited to component and system levels of representation and present a degree of static complexity to study optimal resource planning. To address these limitations, a modular hybrid simulation framework for complex manufacturing system design is presented. A manufacturing system with highly regulated and manual handling processes, composed of multiple repeating modules, is considered. In this framework, the concept of modular hybrid ABM–DES technique is introduced to demonstrate a novel simulation method using a dynamic system of parallel multi-agent discrete events. In this context, to create a modular model, the stochastic finite dynamical system is extended to allow the description of discrete event states inside the agent for manufacturing repeating modules (meso level). Moreover, dynamic complexity regarding uncertain processing time and resources is considered. This framework guides the user step-by-step through the system design and modular hybrid model. A real case study in the cell and gene therapy industry is conducted to test the validity of the framework. The simulation results are compared against the data from the studied case; excellent agreement with 1.038% error margin is found in terms of the company performance. The optimal resource planning and the uncertainty of the processing time for manufacturing phases (exo level), in the presence of dynamic complexity is calculated

A survey of simulation techniques in commerce and defence

Despite the developments in Modelling and Simulation (M&S) tools and techniques over the past years, there has been a gap in the M&S research and practice in healthcare on developing a toolkit to assist the modellers and simulation practitioners with selecting an appropriate set of techniques. This study is a preliminary step towards this goal. This paper presents some results from a systematic literature survey on applications of M&S in the commerce and defence domains that could inspire some improvements in the healthcare. Interim results show that in the commercial sector Discrete-Event Simulation (DES) has been the most widely used technique with System Dynamics (SD) in second place. However in the defence sector, SD has gained relatively more attention. SD has been found quite useful for qualitative and soft factors analysis. From both the surveys it becomes clear that there is a growing trend towards using hybrid M&S approaches

Energy efficiency in discrete-manufacturing systems: insights, trends, and control strategies

Since the depletion of fossil energy sources, rising energy prices, and governmental regulation restrictions, the current manufacturing industry is shifting towards more efficient and sustainable systems. This transformation has promoted the identification of energy saving opportunities and the development of new technologies and strategies oriented to improve the energy efficiency of such systems. This paper outlines and discusses most of the research reported during the last decade regarding energy efficiency in manufacturing systems, the current technologies and strategies to improve that efficiency, identifying and remarking those related to the design of management/control strategies. Based on this fact, this paper aims to provide a review of strategies for reducing energy consumption and optimizing the use of resources within a plant into the context of discrete manufacturing. The review performed concerning the current context of manufacturing systems, control systems implemented, and their transformation towards Industry 4.0 might be useful in both the academic and industrial dimension to identify trends and critical points and suggest further research lines.Peer ReviewedPreprin

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Design of an instrumented smart cutting tool and its implementation and application perspectives

This paper presents an innovative design of a smart cutting tool, using two surface acoustic wave (SAW) strain sensors mounted onto the top and the side surface of the tool shank respectively, and its implementation and application perspectives. This surface acoustic wave-based smart cutting tool is capable of measuring the cutting force and the feed force in a real machining environment, after a calibration process under known cutting conditions. A hybrid dissimilar workpiece is then machined using the SAW-based smart cutting tool. The hybrid dissimilar material is made of two different materials, NiCu alloy (Monel) and steel, welded together to form a single bar; this can be used to simulate an abrupt change in material properties. The property transition zone is successfully detected by the tool; the sensor feedback can then be used to initiate a change in the machining parameters to compensate for the altered material properties.The UK Technology Strategy Board (TSB) for supporting this research (SEEM Project, contract No. BD266E

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

Special Session on Industry 4.0

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