Intelligent Process Planning Optimization for Product Cost Estimation

  • Authors:

  • W. D. Li;S. K. Ong;A. Y. C. Nee;L. Ding;C. A. McMahon

  • Affiliations:

  • Department of Mechanical Engineering, University of Bath, Bath BA2 7AY, U.K.;Department of Mechanical Engineering, National University of Singapore, 10 Kent Ridge Crescent, 119260, Singapore;Department of Mechanical Engineering, National University of Singapore, 10 Kent Ridge Crescent, 119260, Singapore;Department of Mechanical Engineering, University of Bath, Bath BA2 7AY, U.K.;Department of Mechanical Engineering, University of Bath, Bath BA2 7AY, U.K.

  • Venue:
  • Proceedings of the 2006 conference on Integrated Intelligent Systems for Engineering Design
  • Year:
  • 2006

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Abstract

Manufacturing cost is crucial for the economic success of a product, and early and accurate estimation of manufacturing cost can support a designer to evaluate a designed model dynamically and efficiently for making cost-effective decisions. Manufacturing cost estimation is closely related to process planning problems, in which machining operations, machining resources, operation sequences, etc., are selected, determined and optimized. To solve the intractable decision-making issues in process planning with complex machining constraints, three intelligent optimization methods, i.e., Genetic Algorithm (GA), Simulated Annealing (SA) and Tabu Search (TS), have been developed to determine the optimal or near-optimal allocation of machining resources and sequence of machining operations for a process plan simultaneously, and a fuzzy logic-based Analytical Hierarchical Process technique has been applied to evaluate the satisfaction degree of the machining constraints for the process plan. Case studies, which are used to compare the three developed methods, are discussed to highlight their characteristics in the aspects of solution quality, computation efficiency and optimization result robustness.