We present a modeling methodology for conceptual design and simulation of automated material handling systems (AMHS) in semiconductor manufacturing. This methodology integrates the conceptual design and modeling proce...
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We present a modeling methodology for conceptual design and simulation of automated material handling systems (AMHS) in semiconductor manufacturing. This methodology integrates the conceptual design and modelingprocesses. This results in a reduction in modeling cycle time. The modelingprocess utilizes a simulator, selected from a suite, which is coupled with a design tool. The design tool, an in-house expert system helps in preliminary analysis of and selection among several alternatives. The design tool then generates appropriate data files to be used by a simulator. A simulator, built using Automod ii, represents in AMHS in terms of layout, vehicle control logic, and material flow. We discuss the architecture of a simulator in detail and present a case study. We have developed a set of simulators to handle different vehicle control algorithms.
This paper discusses the results obtained from the SEDS-1 flight and compares them with preflight and post flight simulations of the mission. The successful first flight of the small, expendable deployer system (SEDS-...
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Artificial neural networks were evaluated for monitoring and control of the variable polarity plasma arc welding (VPPAW) process. Three areas of welding application were investigated: weld processmodeling, weld proce...
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Artificial neural networks were evaluated for monitoring and control of the variable polarity plasma arc welding (VPPAW) process. Three areas of welding application were investigated: weld processmodeling, weld processcontrol, and weld bead profile analysis for quality control. Experiments and analysis confirm that artificial neural networks are powerful tools for analysis, modeling, and control applications. They are particularly attractive in view of their capabilities to process nonlinear and noisy data, learn from actual welding data, and execute at relatively high speed. It is shown that neural networks are capable of modeling parameters of the VPPAW process to on the order of 10% accuracy or better. The same was observed when neural networks were used to select welding equipment parameters and the resulting bead geometries were estimated. These performance figures suggest that a VPPA welding control system can be implemented based on neural network models and control mechanisms.< >
A systematic algorithm is proposed to design a fuzzy inference system through statistical data pre-processing. This approach is appropriate in modeling the qualitative aspects of a semiconductor manufacturing process,...
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A systematic algorithm is proposed to design a fuzzy inference system through statistical data pre-processing. This approach is appropriate in modeling the qualitative aspects of a semiconductor manufacturing process, when extensive training data are often limited or difficult to collect due to the high cost of conducting experiments. With the limited number of data sets from a designed experiment, our system employs a proper statistical analysis to extract simple fuzzy inference rules of input-output relationships and initialize the corresponding membership functions. The output process variable can be continuous or categorical, and the fuzzy system can be further tuned to accommodate newly acquired experimental data.< >
Modelling a protocol is difficult because it involves describing a two-dimensional relationship between the flow of control of many processes and the synchronized flow of data between those processes. This paper prese...
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Modelling a protocol is difficult because it involves describing a two-dimensional relationship between the flow of control of many processes and the synchronized flow of data between those processes. This paper presents the use of a new technique, Deductive Systems, for the modeling of communication protocols. The strengths of using such approach for protocol modeling are the ease with which modeling can be modified, the rigorous analysis which they enable of the constructed models, and the incremental way in which modeling and verification of a system can be performed. Starting from the fundamental definitions of Deductive Systems, it is shown how an extended Alternating Bit (AB) protocol can be modeled, with real life conditions taken into consideration.
The authors discuss how traditional information system analysis and design techniques might be enhanced by coupling them with simulation modeling and analysis techniques. They propose a simple enhancement to data flow...
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The authors discuss how traditional information system analysis and design techniques might be enhanced by coupling them with simulation modeling and analysis techniques. They propose a simple enhancement to data flow diagrams (DFDs) to capture dynamic and probabilistic system information in the system model and suggest simulating and evaluating alternate system designs based on the enhanced DFD. To further enhance this process they propose an experiment design strategy that permits robust information system designs to be determined. They illustrate the proposed experiment design strategy to determine the resource capacity appropriate for an order entry information system. Another more complex objective for which the proposed strategy should prove beneficial is process design or redesign.< >
One municipal facility is beginning to consider the benefits of using model predictive control as a means of improving product quality and reducing energy costs. To date, the initial steps of this project have been co...
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One municipal facility is beginning to consider the benefits of using model predictive control as a means of improving product quality and reducing energy costs. To date, the initial steps of this project have been completed. The first step was to upgrade the basic control and data acquisition systems. The second step was to collect experimental data in order to build a process model. The third step was to build this model; a dynamic nonlinear finite impulse response model was constructed using the neural network partial least squares algorithm. This model has been used to analyze the steady state behavior of the plant and this analysis has helped identify an improved strategy which lowers annual operating costs. The implementation of these ideas awaits the completion of a process retrofit. After this expansion, the modified process will be remodeled, and the suggested control strategy will be experimentally verified.
We performed a domain analysis for the data-intensive business systems. A large amount of commonly used database retrievals and computational functions were found to he repeatedly specified in the process of specifyin...
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ISBN:
(纸本)0818642122
We performed a domain analysis for the data-intensive business systems. A large amount of commonly used database retrievals and computational functions were found to he repeatedly specified in the process of specifying programs in this domain. In this paper, a program specification technique called the data derivation (DD) specification technique is proposed to improve the reuse of program specifications in this domain. In this technique, we use the object-oriented approach augmented with attribute propagation, and a set of predefined generic components for forming an object model. A program is specified through modeling its effects. The commonly used database retrievals are incorporated into the predefined generic components. The commonly used computational functions are stored in a user definable library and can be incorporated into the generic components as and when needed.< >
The authors report on the nature of the glass tinting process and provide an overview of the data engineering process that has been implemented to provide data from the Pilkington AIRCO furnace in an appropriate form ...
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The authors report on the nature of the glass tinting process and provide an overview of the data engineering process that has been implemented to provide data from the Pilkington AIRCO furnace in an appropriate form for ANN modeling. A brief discussion of the general regression neural network architecture and its use as an adaptive model is also presented.< >
Industrial processes usually involve a large number of variables, many of which vary in a correlated manner. To identify a process model which has correlated variables, an ordinary least squares approach demonstrates ...
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Industrial processes usually involve a large number of variables, many of which vary in a correlated manner. To identify a process model which has correlated variables, an ordinary least squares approach demonstrates ill-conditioned problem and the resulting model is sensitive to changes in sampled data. In this paper, a recursive partial least squares (PLS) regression is used for online system identification and circumventing the ill-conditioned problem. The partial least squares method is used to remove the correlation by projecting the original variable space to an orthogonal latent space. Application of the proposed algorithm to a chemical processing modeling problem is discussed.< >
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