Optimum Selection of a Composite Product System Using MADM Approach

The paper describes a methodology for evaluation, coding, ranking, and optimum selection of subsystems for composite product used directly by its manufacturers. This method is important from the point view of development of a reliable database, virtual design, customization, developing cutting-edge technology, and meeting the challenges of global competition in composite industry. The 77-attribute electronic coding scheme and the evaluation techniques presented in this paper are useful to the designer during all the phases of design process, and manufacturer for the selection of optimum subsystems, which meet global market requirements. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is a Multiple-Attribute Decision Making (MADM) approach, used for selection of subsystems for a composite product development in order of preference for given application. Two graphical methods of MADM approach for evaluation and comparison are also introduced. The proposed 3-stage selection methodology is explained with an illustrative example.

[2]  John Summerscales,et al.  Resin Infusion under Flexible Tooling (RIFT): a review , 1996 .

[3]  Kevin D Potter Suggestions for good practice in the design and development of RTM components , 1997 .

[4]  Subir Kumar Saha,et al.  Attribute based specification, comparison and selection of a robot , 2004 .

[5]  Edith Mäder,et al.  Characterization of fiber/matrix interface strength: applicability of different tests, approaches and parameters , 2005 .

[6]  K. W. Ip,et al.  Optimization of process conditions for the transfer molding of electronic packages , 2003 .

[7]  Bill Bessant Rapid tooling system benefits boat builders , 2002 .

[8]  Jin-chein Lin,et al.  Morphological structure, processing and properties of propylene polymer matrix nanocomposites , 2005 .

[9]  David L. Olson,et al.  Comparison of weights in TOPSIS models , 2004, Math. Comput. Model..

[10]  C. Hwang Multiple Objective Decision Making - Methods and Applications: A State-of-the-Art Survey , 1979 .

[11]  Dong-Hyun Jee,et al.  A method for optimal material selection aided with decision making theory , 2000 .

[12]  M. K. Bannister,et al.  Quantitative structure–property relationships for composites: prediction of glass transition temperatures for epoxy resins , 2004 .

[13]  S. H. Choi,et al.  A virtual prototyping system for rapid product development , 2004, Comput. Aided Des..

[14]  W. D Brouwer,et al.  Vacuum injection moulding for large structural applications , 2003 .

[15]  C. Kang,et al.  Microstructural characteristics and mechanical properties of hypo-eutectic and hyper-eutectic Al–Si alloys in the semi-solid forming process , 2001 .

[16]  Bhabani K. Satapathy,et al.  Wear data analysis of friction materials to investigate the simultaneous influence of operating parameters and compositions , 2004 .

[17]  Tai-Yue Wang,et al.  Machine selection in flexible manufacturing cell: A fuzzy multiple attribute decision-making approach , 2000 .

[18]  Andrew C. Long,et al.  Liquid Moulding Technologies , 1997 .

[19]  Ray S. Fertig,et al.  Influence of constituent properties and microstructural parameters on the tensile modulus of a polymer/clay nanocomposite , 2004 .

[20]  Ben Wang,et al.  Flow modeling and simulation of SCRIMP for composites manufacturing , 2000 .

[21]  K. Reifsnider Modelling of the interphase in polymer-matrix composite material systems , 1994 .

[22]  V. P. Agrawal,et al.  Structural Modeling and Analysis of Composite Product System: A Graph Theoretic Approach , 2006 .

[23]  V. P. Agrawal,et al.  Computer-aided evaluation and selection of optimum grippers , 1992 .

[24]  Ching-Lai Hwang,et al.  Multiple Attribute Decision Making: Methods and Applications - A State-of-the-Art Survey , 1981, Lecture Notes in Economics and Mathematical Systems.

[25]  D. Lee,et al.  Manufacture of composite screw rotors for air compressors by RTM process , 2001 .