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209 System reliability prediction using global optimization
408
Abstracts
206 Surface Temperature Control in Plasma Deposition of Metal Matrix Composites S. Koskle, S.C. Shah, E.S. Russell, pp 199-203
210 Machine Tool-Wear Sensing Aided by a Stochastic Global Optimizer M.A. Zohdy, B. Adamczyk, pp 216-219
This paper describes the control of target surface temperature in inductively coupled plasma deposition (ICPD) in the manufacture of metal matrix composites. A workstation-based rapid prototyping system was used to acquire data, identify models, and perform analysis and design. Real-time implementation of the algorithms was facilitated by a C language source code generator and interactive real-time displays on the workstation monitor. The resulting controller communicated with the facility's conventional programmable controller and included an interactive operator interface. The complete development cycle, including system identification, control design, and robustness analysis and re-implementation, takes of the order of several hours to repeat in the event of changes in process equipment or configuration.
This paper presents an approach to the problem of on-line machine tool-wear sensing and monitoring. Ultrasonic energy acoustic emission signals are used in the diagnostic system. The real-time situation is simulated by using a linear combination of periodic waves superimposed on a random noise. The peaking pattern of the acoustic waves is keyed in as an indicator of tool-wear and is continuously monitored to predict tool failure or fracture. A new stochastic global optimization is used to generate the data set that best represents archetypes of machine-wear characteristics.
207 Global Optimization of Manufacturing System Simulations G.W. Evans, M. Mollaghaseml, B. Stuckman, S. Koli, pp 204.206 This paper discusses the importance of models in manufacturing system design, and the difficulties associated with the optimization of these models. An example problem, involving the global optimization of a simulation model of a discrete-parts manufacturing system, is presented.
208 The Adaptive Complex Method of Global Optimization with Application to Problems with Discrete-Continuous Objective Functions T.J. Manetsch, pp 207-210 The adaptive complex method of global optimization is presented, along with an extension which makes it applicable to objective functions with both discrete and continuous decision variables. So-called "discretecontinuous" objective functions are discussed and classified aocording to key subtypes. It is noted that such objective functions are inherently multimodal and that a number of optimization problems in manufacturing systems have so-called "discrete-continuous" objective functions. The extension of the adaptive complex method is described and it is seen to be effective in locating global optima with discrete-continuous objective functions. Results from applying this extended adaptive complex method to a class of discrete-continuous objective functions are presented.
209 System Reliability Prediction Using Global Optimization J. Usher, B. Stuckman, pp 211-215 In realistic scenarios, the prediction of system reliability involves the maximization of a likelihood function over a large multidimensional space. Where the exact cause of failure is known, maximization can be performed analytically. Often, however, failure data is masked (the cause can only be narrowed to some subset of possible components), and a global optimization algorithm is needed. This paper presents the mathematical framework for the formation of the likelihood function for Weibull distributed component failures in the presence of masked data. Simulated annealing is used to find the maximum likelihood estimates of the component parameters in a case study involving ten components. Comparative results are given.
211 An Intelligent Model Generator for FMS Simulation Liu Fuyan, Lu Shaoyi, pp 220-224 This paper presents an implementation of automatic programming techniques in an intelligent model generator, used to create simulation models of flexible manufactming systems automatically. It uses the information acquired via an interactive dialogue interface to generate a model framework and experimental framework, and then converts it into a target language structure. The model generator is contained in a simulation software environment, which is used for studying the behaviour of the FMS under various control and scheduling policies, and for performance evaluation with different system performance measures. This paper describes the modelling specification acquisition and the model construction process. The simulation software is also presented briefly.
212 Data Transfer, Processing and Path Planning for AGVs in a Flexible Manufacturing Environment Z. Katz, G. Bright, pp 225-228 The paper presents some details on the mechanical design of the AGV, its guidance and drive systems and the interaction between its subsystems. Specific problems related to data transfer and data processing for ongoing communication within the AGV and within the operating environment are described. Concepts of path planning are highlighted and potential optimal path selection incorporating collision avoidance within task requirements is discussed. Algorithms for specific path selection within a flexible manufacturing environment are incorporated and the logical approach used is explained.
213 The Reliability Model on Multi-Stage CIMS Production Line with Unreliable Buffer Tan Min, pp 229-234 This paper presents the reliability model of a multi-stage CIMS production line with an unreliable buffer, analyzes the operating situation of the production line under the failure of the buffer, gets the solutions of the steady state availability production rate, and discusses a numerical example.
214 Simulation Modeling in Factory Layout Optimization M. Chierotti, J.W. Rozenblit, pp 235-239 This paper presents a new factory layout methodology dud its integration in a simulation-based environment for FMS design. Once machines and material-handling devices have been selected, a layout synthesis tool generates a workscene model for planning robot trajectories and investigating material-handling control strategies by means of discrete-event simulation. Layouts are represented as
Abstracts
206 Surface Temperature Control in Plasma Deposition of Metal Matrix Composites S. Koskle, S.C. Shah, E.S. Russell, pp 199-203
210 Machine Tool-Wear Sensing Aided by a Stochastic Global Optimizer M.A. Zohdy, B. Adamczyk, pp 216-219
This paper describes the control of target surface temperature in inductively coupled plasma deposition (ICPD) in the manufacture of metal matrix composites. A workstation-based rapid prototyping system was used to acquire data, identify models, and perform analysis and design. Real-time implementation of the algorithms was facilitated by a C language source code generator and interactive real-time displays on the workstation monitor. The resulting controller communicated with the facility's conventional programmable controller and included an interactive operator interface. The complete development cycle, including system identification, control design, and robustness analysis and re-implementation, takes of the order of several hours to repeat in the event of changes in process equipment or configuration.
This paper presents an approach to the problem of on-line machine tool-wear sensing and monitoring. Ultrasonic energy acoustic emission signals are used in the diagnostic system. The real-time situation is simulated by using a linear combination of periodic waves superimposed on a random noise. The peaking pattern of the acoustic waves is keyed in as an indicator of tool-wear and is continuously monitored to predict tool failure or fracture. A new stochastic global optimization is used to generate the data set that best represents archetypes of machine-wear characteristics.
207 Global Optimization of Manufacturing System Simulations G.W. Evans, M. Mollaghaseml, B. Stuckman, S. Koli, pp 204.206 This paper discusses the importance of models in manufacturing system design, and the difficulties associated with the optimization of these models. An example problem, involving the global optimization of a simulation model of a discrete-parts manufacturing system, is presented.
208 The Adaptive Complex Method of Global Optimization with Application to Problems with Discrete-Continuous Objective Functions T.J. Manetsch, pp 207-210 The adaptive complex method of global optimization is presented, along with an extension which makes it applicable to objective functions with both discrete and continuous decision variables. So-called "discretecontinuous" objective functions are discussed and classified aocording to key subtypes. It is noted that such objective functions are inherently multimodal and that a number of optimization problems in manufacturing systems have so-called "discrete-continuous" objective functions. The extension of the adaptive complex method is described and it is seen to be effective in locating global optima with discrete-continuous objective functions. Results from applying this extended adaptive complex method to a class of discrete-continuous objective functions are presented.
209 System Reliability Prediction Using Global Optimization J. Usher, B. Stuckman, pp 211-215 In realistic scenarios, the prediction of system reliability involves the maximization of a likelihood function over a large multidimensional space. Where the exact cause of failure is known, maximization can be performed analytically. Often, however, failure data is masked (the cause can only be narrowed to some subset of possible components), and a global optimization algorithm is needed. This paper presents the mathematical framework for the formation of the likelihood function for Weibull distributed component failures in the presence of masked data. Simulated annealing is used to find the maximum likelihood estimates of the component parameters in a case study involving ten components. Comparative results are given.
211 An Intelligent Model Generator for FMS Simulation Liu Fuyan, Lu Shaoyi, pp 220-224 This paper presents an implementation of automatic programming techniques in an intelligent model generator, used to create simulation models of flexible manufactming systems automatically. It uses the information acquired via an interactive dialogue interface to generate a model framework and experimental framework, and then converts it into a target language structure. The model generator is contained in a simulation software environment, which is used for studying the behaviour of the FMS under various control and scheduling policies, and for performance evaluation with different system performance measures. This paper describes the modelling specification acquisition and the model construction process. The simulation software is also presented briefly.
212 Data Transfer, Processing and Path Planning for AGVs in a Flexible Manufacturing Environment Z. Katz, G. Bright, pp 225-228 The paper presents some details on the mechanical design of the AGV, its guidance and drive systems and the interaction between its subsystems. Specific problems related to data transfer and data processing for ongoing communication within the AGV and within the operating environment are described. Concepts of path planning are highlighted and potential optimal path selection incorporating collision avoidance within task requirements is discussed. Algorithms for specific path selection within a flexible manufacturing environment are incorporated and the logical approach used is explained.
213 The Reliability Model on Multi-Stage CIMS Production Line with Unreliable Buffer Tan Min, pp 229-234 This paper presents the reliability model of a multi-stage CIMS production line with an unreliable buffer, analyzes the operating situation of the production line under the failure of the buffer, gets the solutions of the steady state availability production rate, and discusses a numerical example.
214 Simulation Modeling in Factory Layout Optimization M. Chierotti, J.W. Rozenblit, pp 235-239 This paper presents a new factory layout methodology dud its integration in a simulation-based environment for FMS design. Once machines and material-handling devices have been selected, a layout synthesis tool generates a workscene model for planning robot trajectories and investigating material-handling control strategies by means of discrete-event simulation. Layouts are represented as