Simulated annealing assisted optimization of fuzzy rules for maximizing tool life in high-speed milling process

  • Authors:

  • Asif Iqbal;Ning He;Liang Li;Naeem Ullah Dar

  • Affiliations:

  • CMEE, Nanjing University of Aeronautics & Astronautics., Nanjing, P.R. China;CMEE, Nanjing University of Aeronautics & Astronautics, Nanjing, P.R. China;CMEE, Nanjing University of Aeronautics & Astronautics, Nanjing, P.R. China;Department of Mechanical Engineering, University of Engineering & Technology, Taxila, MED, UET Taxila, Pakistan

  • Venue:
  • AIA'06 Proceedings of the 24th IASTED international conference on Artificial intelligence and applications
  • Year:
  • 2006

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Abstract

In metal cutting industry it is a common practice to search for optimal combination of cutting parameters in order to maximize the tool life for a fixed minimum value of material removal rate (MRR). After the advent of high-speed milling (HSM) process, lot of experimental research has been done for optimization of the parameters. It is highly beneficial to convert raw data into a comprehensive knowledge-based expert system using fuzzy logic as the reasoning mechanism. In this paper an attempt has been presented for the optimization of the rules so as to have the effective most knowledge-base for given set of data. Experiments were conducted to determine the best values of cutting speeds that can maximize tool life for different combinations of input parameters. Triangular fuzzy sets were developed for input parameters and the cutting speed, based upon the range of values obtained. Max-min strategy was used for aggregation of fuzzy rules. Simulated annealing algorithm was used to work out the optimal combination of fuzzy rules out of 5.815 × 1025 possible combinations. Optimized combination of fuzzy rules provided the estimation error of only 7.18m/min as compared to 232m/min of that of randomized combination of rules.