Fuzzy Linguistic Optimization on Multi-Attribute Machining

  • Tian-Syung Lan Department of Information Management, Yu Da University
  • Chen-Feng Wu Department of Information Management, Yu Da University
  • Ming-Yung Wang Graduate Institute of Engineering Management, Tatung University
  • Chih-Yao Lo Department of Information Management, Yu Da University
Keywords: computer numerical control, orthogonal array, fuzzy deduction, Technique for Order Preference by Similarity to Ideal Solution

Abstract

Most existing multi-attribute optimization researches for the modern CNC (computer numerical control) turning industry were either accomplished within certain manufacturing circumstances, or achieved through numerous equipment operations. Therefore, a general deduction optimization scheme proposed is deemed to be necessary for the industry. In this paper, four parameters (cutting depth, feed rate, speed, tool nose runoff) with three levels (low, medium, high) are considered to optimize the multi-attribute (surface roughness, tool wear, and material removal rate) finish turning. Through FAHP (Fuzzy Analytic Hierarchy Process) with eighty intervals for each attribute, the weight of each attribute is evaluated from the paired comparison matrix constructed by the expert judgment. Additionally, twenty-seven fuzzy control rules using trapezoid membership function with respective to seventeen linguistic grades for each attribute are constructed. Considering thirty input and eighty output intervals, the defuzzifierion using center of gravity is thus completed. The TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) is moreover utilized to integrate and evaluate the multiple machining attributes for the Taguchi experiment, and thus the optimum general deduction parameters can then be received. The confirmation experiment for optimum general deduction parameters is furthermore performed on an ECOCA-3807 CNC lathe. It is shown that the attributes from the fuzzy linguistic optimization parameters are all significantly advanced comparing to those from benchmark. This paper not only proposes a general deduction optimization scheme using orthogonal array, but also contributes the satisfactory fuzzy linguistic approach for multiple CNC turning attributes with profound insight.

Author Biography

Chen-Feng Wu, Department of Information Management, Yu Da University
Chen-Feng Wu received the B.S. degree in Computer Science and Engineering from Feng-Chia University in 1993, and his M.S. and Ph.D. degrees in Computer Science and Engineering from Tatung University in 1998 and 2007, respectively. Currently, he is the Assistant Professor of the Department of Information Management, Yu-Da College of Business. His research interests include wireless networks, QoS guarantee in networks, architecture design of ATM switch, parallel and distributed systems, embedded systems, and high speed switching architectures.
Published
2010-06-30
Section
Articles