A learning algorithm based estimation method for maintenance cost of product concepts

Life cycle concerns have been realized a major issue of increasing importance. Life cycle cost as analytical method has been developed to enable comprehensive cost analysis to improve economic performance of products during their life cycle. This paper present a learning algorithm based estimation method for maintenance cost as life cycle cost of product concepts. In order to develop the proposed method, we identify some attributes that represent corrective maintainability of product concepts and add them to the product attributes used to make a selection amongst product concepts. From the list of all the product attributes, 24 product attributes strongly correlated with maintenance cost are chosen. To estimate maintenance cost of product concepts, the selected product attributes are used as inputs and maintenance cost are used as outputs in a learning model based on based on artificial neural networks. The proposed approach does not replace the detailed cost estimation but it would give some cost-effective decision making for product concepts.

[1]  Hiroyuki Hiraoka,et al.  Facility Model for Life-Cycle Maintenance System , 1995 .

[2]  David M. Skapura,et al.  Neural networks - algorithms, applications, and programming techniques , 1991, Computation and neural systems series.

[3]  J. P. Rombouts.  LEADS-II. A knowledge-based system for ranking DfE-Options , 1998, Proceedings of the 1998 IEEE International Symposium on Electronics and the Environment. ISEE - 1998 (Cat. No.98CH36145).

[4]  M.H. Hassoun,et al.  Fundamentals of Artificial Neural Networks , 1996, Proceedings of the IEEE.

[5]  Wieslaw Tarelko.  Control model of maintainability level , 1995 .

[6]  M. F. Wani,et al.  Development of maintainability index for mechanical systems , 1999 .

[7]  Kosuke Ishii,et al.  ADVANCED FAILURE MODES AND EFFECTS ANALYSIS USING BEHAVIOR MODELING , 1997 .

[8]  T.A. Bhamra,et al.  Integrating environmental decisions into the product development process. I. The early stages , 1999, Proceedings First International Symposium on Environmentally Conscious Design and Inverse Manufacturing.

[9]  Kwang-Kyu Seo,et al.  Approximate life cycle assessment of classified products using artificial neural network and statistical analysis in conceptual product design , 2001, Proceedings Second International Symposium on Environmentally Conscious Design and Inverse Manufacturing.

[10]  Han Brezet,et al.  Ecodesign : a promising approach to sustainable production and consumption , 1997 .

[11]  Charles Leave.  Neural Networks: Algorithms, Applications and Programming Techniques , 1992 .

[12]  V. Rich.  Personal communication , 1989, Nature.

[13]  Kosuke Ishii,et al.  Life-Cycle Engineering Design , 1995 .

[14]  Clair E. Cunningham,et al.  Applied maintainability engineering , 1972 .

[15]  Robert Paasch,et al.  Evaluation of Failure Diagnosis in Conceptual Design of Mechanical Systems , 1997 .

[16]  Surendra M. Gupta,et al.  ANALYSIS OF DESIGN EFFICIENCY FOR THE DISASSEMBLY OF MODULAR ELECTRONIC PRODUCTS , 1999 .

[17]  Zineb Simeu-Abazi,et al.  Maintenance Integration in Manufacturing Systems: From the Modeling Tool to Evaluation , 2001 .

[18]  Ramesh Raskar,et al.  Simulation, animation, and analysis of design disassembly for maintainability analysis , 1995 .