Cover of the Journal of Intelligent Manufacturing Soft Computing in Manufacturing, Part I

A Special Issue of the Journal
Intelligent Manufacturing
Vol. 14, No. 2, April of 2003


T. Warren Liao and Evangelos Triantaphyllou
Guest Editors




TABLE OF CONTENTS

  1. Interactive Evolutionary Solution Synthesis in Fuzzy Set-based Preliminary Engineering Design
    by Jiachuan Wang and Janis Terpenny (See Abstract)


  2. Development of a Fuzzy Decision Model for Manufacturability Evaluation
    by Bernard C. Jiang and Chi-Hsing Hsu  (See Abstract)


  3. Fuzzy Neural Networks For Intelligent Design Retrieval Using Associative Manufacturing Features
    by C.-Y. Tsai and C. A. Chang (See Abstract)


  4. A Tabu-enhanced Genetic Algorithm Approach for Assembly Process Planning
    by Li J R, Khoo L P and Tor S B (See Abstract)


  5. A genetic algorithm approach for the cutting stock problem
    by Godfrey C. Onwubolu And Michael Mutingi (See Abstract)


  6. Estimation Of Shrinkage For Near Net-Shape Using A Neural Network Approach
    by Abdullah Konak,Sadan Kulturel-Konak,Alice E. Smith and Ian Nettleship (See Abstract)


  7. Optimizing The IC Wire Bonding Process Using A  Neural Networks/Genetic Algorithms Approach
    by Chao-Ton Su and Tai-Lin Chiang (See Abstract)


  8. Intelligent Remote Monitoring and Diagnosis of Manufacturing Processes Using An Integrated Approach of Neural Networks and Rough Sets
    by Tung-Hsu (Tony) Hou, Wang-Lin Liu and Li Lin (See Abstract)


  9. Intelligent Adaptive Control of Bioreactors
    by R. Babŭska, M. R. Damen, C. Hellinga and  H. Maarleveld (See Abstract)






ABSTRACTS:

     
  1. Interactive Evolutionary Solution Synthesis in Fuzzy Set-based Preliminary Engineering Design

    Journal of Intelligent Manufacturing, Vol. xx, No. x, pp. xxx-xxx, 2002.

    by Jiachuan Wang and Janis Terpenny

    Department of Mechanical and Industrial Engineering, University of Massachusetts,
    Amherst, MA 01003, USA
    Telephone: 413-545-0707*, FAX: 413-545-1027
    E-mail: terpenny@ecs.umass.edu


    ABSTRACT:
    This paper describes an interactive evolutionary approach to synthesize component-based preliminary engineering design problems. This approach is intended to address preliminary engineering design as an evolutionary synthesis process, with the needs for human-computer interaction in a changing environment caused by uncertainty and imprecision inherent in the early design stages. It combines an agent-based hierarchical design representation, set-based design generation, fuzzy design trade-off strategy and interactive design adaptation into evolutionary synthesis to gradually refine and reduce the search space while maintaining solution diversity to accommodate future changes.  The fitness function of solutions employed is not fixed but adapted according to elicited human value judgment and constraint change. It incorporates multi-criteria evaluation as well as constraint satisfaction.  This new approach takes advantage of the different roles of computers and humans play in design and optimization.  The methodology will be applicable to general multi-domain applications, with emphasis on physical modeling of dynamic systems. An automotive speedometer design case study is included to demonstrate the methodology.

    KEY WORDS: Agent-based hierarchical design representation, set-based design generation, fuzzy design trade-off strategy, interactive design adaptation, evolutionary solution synthesis

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  2. Development of a Fuzzy Decision Model for Manufacturability Evaluation

    Journal of Intelligent Manufacturing, Vol. xx, No. x, pp. xxx-xxx, 2002.

    by Bernard C. Jiang1 and Chi-Hsing Hsu2  

    1Department of Industrial Engineering and Management, Yuan Ze University, Chung-Li, 32026, Taiwan, R.O.C. E-Mail: iebjiang@saturn.yzu.edu.tw

    2 Department of Industrial Engineering and Management, Ching Yun Institute of Technology, Taiwan, R.O.C.

     
    ABSTRACT:
    A manufacturability evaluation decision model is formulated and analyzed based on fuzzy logic and multiple attribute decision-making under the concurrent engineering environment. The study emphasizes on the treatment of the linguistic and vagueness at the early product development stage.  The study also considers the function integration of the total life cycle of a product. Hence, the integrated decision model covers the multi-level, multi-goal requirements of the products.  Multiple criteria such as the goal space, the decision space, the function space, the development (i.e., product & process design) space, and the activity space, are then applied under different analysis of decision-making methods. For instances, the fuzzy multiple attribute decision-making (FMADM) combined with activity-based costing (ABC) can be used in the activity decision space. The fuzzy logic decision model can be applied in the goal decision space. The results of this study point out the importance of early decision making capability. An example of a high-pressure vessel is provided to demonstrate the proposed model for evaluating the manufacturability.

    KEY WORDS: product development, concurrent engineering, fuzzy logic, multiple attribute decision making.

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  3. Fuzzy Neural Networks For Intelligent Design Retrieval Using Associative Manufacturing Features

    Journal of Intelligent Manufacturing, Vol. xx, No. x, pp. xxx-xxx, 2002.

    by C.-Y. Tsai and C. A. Chang

    Department of Industrial Engineering and Management, Yuan Ze University, Chung-Li, 32026, Taiwan, R.O.C. E-Mail:cytsai@saturn.yzu.edu.tw

    ABSTRACT:
    In the conceptual design stage, designers usually initiate a design concept through an association activity.  The activity helps designers collect and retrieve reference information regarding a current design subject instead of starting from scratch.  By modifying previous designs, designers can create a new design in a much shorter time.  To computerize this process, this paper proposes an intelligent design retrieval system involving soft computing techniques for both feature and object association functions.  A feature association method that utilizes fuzzy relation and fuzzy composition is developed to increase the searching spectrum.  In the mean time, object association functions composed by a fuzzy neural network allow designers to control the similarity of retrieved designs.  Our implementation result shows that the intelligent design retrieval system with two soft computing based association functions can retrieve target reference designs as expected.

    KEY WORDS: soft computing, fuzzy set theory, neural networks, manufacturing features, and intelligent design retrieval.

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  4. A Tabu-enhanced Genetic Algorithm Approach for Assembly Process Planning

    Journal of Intelligent Manufacturing, Vol. xx, No. x, pp. xxx-xxx, 2002.

    by Li J R, Khoo L P* and Tor S B

    School of Mechanical and Production Engineering
    Nanyang Technological University
    50 Nanyang Avenue, Singapore 639798
    *mlpkhoo@ntu.edu.sg

    ABSTRACT:
    Over the past decade, much work has been done to optimise assembly process plans to improve productivity. Among them, Genetic Algorithms (GAs) are one of the most widely used techniques. Basically, GAs are optimisation methodologies based on a direct analogy to Darwinian natural selection and genetics in biological systems. They can deal with complex product assembly planning. However, during the process, the neighbourhood may converge too fast and limit the search to a local optimum prematurely. In a similar domain, Tabu Search (TS) constitutes a meta-procedure that organises and directs the operation of a search process. It is able to systematically impose and release constraints so as to permit the exploration of otherwise forbidden regions in a search space. This study attempts to combine the strengths of GAs and TS to realize a hybrid approach for optimal assembly process planning. More robust search behaviour can possibly be obtained by incorporating the Tabuís intensification and diversification strategies into GAs. The hybrid approach also takes into account assembly guidelines and assembly constraints in the derivation of near optimal assembly process plans. A case study on a cordless telephone assembly is used to demonstrate the approach. Results show that the assembly process plans obtained are superior to those derived by GA alone. The details of the hybrid approach and the case study are presented.
    KEY WORDS: Genetic Algorithms, Tabu Search, Assembly guidelines, Assembly constraints.

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  5. A genetic algorithm approach for the cutting stock problem

    Journal of Intelligent Manufacturing, Vol. xx, No. x, pp. xxx-xxx, 2002.

    by Godfrey C. Onwubolu+ And Michael Mutingi*

    +Department of Engineering,
    The University of the South Pacific,
    P.O. Box 1168, Suva, Fiji; *Olivine Industries, Zimbabwe.
    Email+: onwubolu_g@usp.acl.fj


    ABSTRACT:
    In this paper, a genetic algorithm approach is developed for solving the rectangular cutting stock problem.  The performance measure is the minimization of the waste.  Simulation results obtained from the genetic algorithm-based approach are compared with one heuristic based on partial enumeration of all feasible patterns, and another heuristic based on a genetic neuro-nesting approach.  Some test problems taken from the literature were used for the experimentation.  Finally the genetic algorithm approach was applied to test problems generated randomly. The simulation results of the proposed approach in terms of solution quality are encouraging when compared to the partial enumeration-based heuristic and the genetic neuro-nesting approach.

    KEY WORDS: cutting stock; optimization; genetic algorithms.

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  6. Estimation Of Shrinkage For Near Net-Shape Using A Neural Network Approach

    Journal of Intelligent Manufacturing, Vol. xx, No. x, pp. xxx-xxx, 2002.

    by Abdullah Konak1,Sadan Kulturel-Konak1,Alice E. Smith1and Ian Nettleship2

    1Department of Industrial and Systems Engineering
    Auburn University, Auburn, AL 36849 USA ;e-mail: Email:akonak@eng.auburn.edu,aesmith@eng.auburn.edu
    2Department of Materials Science and Engineering
     University of Pittsburgh, Pittsburgh, PA 15261 USA

    ABSTRACT:
    A neural network approach is presented for the estimation of shrinkage during a Hot Isostatic Pressing (HIP) process of nickel-based superalloys for near net-shape manufacture.  For the HIP process, the change in shape must be estimated accurately; otherwise, the finished piece will need excessive machining and expensive nickel-based alloy powder will be wasted (if shrinkage is overestimated) or the part will be scrapped (if shrinkage is underestimated).  Estimating shape change has been a very difficult task in the powder metallurgy industry and approaches range from rules of thumb to sophisticated finite element models.  However, the industry still lacks a reliable and general way to accurately estimate final shape.  This paper demonstrates that the neural network approach is promising to estimate post-HIP dimensions from a combination of pre-HIP dimensions, powder characteristics and processing information.  Compared to nonlinear regression models to estimate shrinkage, the neural network models perform very well.  Furthermore, the models described in this paper can be used to find good HIP process settings, such as temperature and pressure, which can reduce operating costs.

     KEY WORDS: Hot Isostatic Pressing (HIP), Artificial Neural Networks, Powder Metallurgy, Near Net-Shape.

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  7. Optimizing The IC Wire Bonding Process Using A  Neural Networks/Genetic Algorithms Approach

    Journal of Intelligent Manufacturing, Vol. xx, No. x, pp. xxx-xxx, 2002.

    by Chao-Ton Su1 and Tai-Lin Chiang2

    1Department of Industrial Engineering and Management
    National Chiao Tung University, Hsinchu, Taiwan, R.O.C.


    2Department of Business Administration
    Minghsin Institute of Technology, Hsinchu, Taiwan, R.O.C.


    ABSTRACT:
    A critical aspect of wire bonding is the quality of the bonding strength that contributes the major part of yield loss to the integrated circuit assembly process. This paper applies an integrated approach using a neural networks and genetic algorithms to optimize IC wire bonding process. We first use a back-propagation network to provide the nonlinear relationship between factors and the response based on the experimental data from a semiconductor manufacturing company in Taiwan. Then, a genetic algorithms is applied to obtain the optimal factor settings. A comparison between the proposed approach and the Taguchi method was also conducted. The results demonstrate the superiority of the proposed approach in terms of process capability.

    KEY WORDS: Integrated circuit (IC), Wire bonding, Neural networks, Back-propagation network, Genetic algorithms.

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  8. Intelligent Remote Monitoring and Diagnosis of Manufacturing Processes Using 

    Journal of Intelligent Manufacturing, Vol. xx, No. x, pp. xxx-xxx, 2002.

    by Tung-Hsu (Tony) Hou*, Wang-Lin Liu* and Li Lin**

    * Institute of Industrial Engineering and Management
    National Yunlin University of Science & Technology, Taiwan, R.O.C.
    ** Department of Industrial Engineering
    University at Buffalo, State University of New York, U.S.A.

    ABSTRACT:
    This research develops a methodology for the intelligent remote monitoring and diagnosis of manufacturing processes. A back propagation neural network monitors a manufacturing process and identifies faulty quality categories of the products being produced. For diagnosis of the process, rough set is used to extract the causal relationship between manufacturing parameters and product quality measures. Therefore, an integration of neural networks and a rough set approach not only provides information about what is expected to happen, but also reveals why this has occurred and how to recover from the abnormal condition with specific guidelines on process parameter settings. The methodology is successfully implemented in an Ethernet network environment with sensors and PLC connected to the manufacturing processes and control computers. In an application to a manufacturing system that makes conveyor belts, the back propagation neural network accurately classified quality faults, such as wrinkles and uneven thickness. The rough set also determined the causal relationships between manufacturing parameters, e.g., process temperature, and output quality measures. In addition, rough set provided operating guidelines on specific settings of process parameters to the operators to correct the detected

    KEY WORDS: Remote monitoring, Computer networks, Neural networks, Data mining, Rough set.
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  9. Intelligent Adaptive Control of Bioreactors

    Journal of Intelligent Manufacturing, Vol. xx, No. x, pp. xxx-xxx, 2002.

    by R. Babŭska, M. R. Damen, C. Hellinga, H. Maarleveld



    ABSTRACT:

    KEY WORDS:  
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