Comparison of fuzzy expert system based strategies of offline and online estimation of flank wear in hard milling process

  • Authors:

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

  • Affiliations:

  • College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, 29, Yu Dao Street, Nanjing, Jiangsu 210016, PR China;College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, 29, Yu Dao Street, Nanjing, Jiangsu 210016, PR China;Department of Mechanical Engineering, University of Engineering and Technology, Taxila, Pakistan;College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, 29, Yu Dao Street, Nanjing, Jiangsu 210016, PR China

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2007

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Abstract

Accurate estimation of flank wear during any in-progress machining process is highly important for the purpose of controlling product quality and the production rate. Hard milling is among few of the recently popularized technologies of metal cutting domain and is found under intense research for the purpose of estimation and control of tool wear. In the presented paper two fuzzy rules based strategies are explained and compared for accurate estimation of tool's flank wear in hard milling process. The offline strategy uses length of cut (LoC) as major input besides tool helix angle and workpiece material hardness, while for the online strategy LoC is replaced with the cutting force. Series of hard milling experiments were performed in order to obtain data for the development of two fuzzy expert systems as well as for testing of both of the strategies. ANOVA showed LoC and cutting force were more significant than other input parameters for the estimation of flank wear, and the design of fuzzy sets for input parameters was based upon this analysis. Two expert systems were tested using experimental data and the results showed that online strategy was 67.9% more accurate than offline one in estimating flank wear.