Fuzzy Linguistic Optimization on Surface Roughness for CNC Turning

Surface roughness is often considered the main purpose in contemporary computer numerical controlled (CNC) machining industry. Most existing optimization researches for CNC finish turning were either accomplished within certain manufacturing circumstances or achieved through numerous equipment operations. Therefore, a general deduction optimization scheme is deemed to be necessary for the industry. In this paper, the cutting depth, feed rate, speed, and tool nose runoff with low, medium, and high level are considered to optimize the surface roughness for finish turning based on orthogonal array. Additionally, nine fuzzy control rules using triangle membership function with respective to five linguistic grades for surface roughness are constructed. Considering four input and twenty output intervals, the defuzzification using center of gravity is then completed. Thus, the optimum general fuzzy linguistic parameters can then be received. The confirmation experiment result showed that the surface roughness from the fuzzy linguistic optimization parameters is significantly advanced compared to that from the benchmark. This paper certainly proposes a general optimization scheme using orthogonal array fuzzy linguistic approach to the surface roughness for CNC turning with profound insight.

[1]  T. R. Bement,et al.  Taguchi techniques for quality engineering , 1995 .

[2]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[3]  A. Kaufmann,et al.  Introduction to fuzzy arithmetic : theory and applications , 1986 .

[4]  G.-J. Lai,et al.  A Study of the Monitoring and Suppression System for Turning Slender Workpieces , 1995 .

[5]  Chiuh-Cheng Chyu,et al.  A Comparative Study on Taguchi’s SN Ratio, Minimising MSD and Variance for Nominal-the-Best Characteristic Experiment , 2002 .

[6]  Joseph A. Arsecularatne,et al.  Calculation of optimum cutting conditions for turning operations using a machining theory , 2000 .

[7]  K. Palanikumar,et al.  Application of Taguchi and response surface methodologies for surface roughness in machining glass fiber reinforced plastics by PCD tooling , 2008 .

[8]  N. Tosun,et al.  Optimisation for hot turning operations with multiple performance characteristics , 2004 .

[9]  S. Kim,et al.  Optimization of manufacturing parameters for a brake lining using Taguchi method , 2003 .

[10]  J. Paulo Davim,et al.  Statistical study of surface roughness in turning of peek composites , 2008 .

[11]  Jen Fin Lin,et al.  Grey theory applied to evaluate the tribological performances of the a-C:H(N) coating films prepared by differing the nitrogen content and the film thickness , 2006 .

[12]  Jae Hyun Park,et al.  A quality function deployment methodology with signal and noise ratio for improvement of Wasserman’s weights , 2005 .

[13]  H. Huh,et al.  Optimization of a roller levelling process for Al7001T9 pipes with finite element analysis and Taguchi method , 2002 .

[14]  Makarand S. Kulkarni,et al.  Combined Taguchi and dual response method for optimization of a centerless grinding operation , 2003 .

[15]  K S Anastasiou.  Optimization of the aluminium die casting process based on the Taguchi method , 2002 .

[16]  Tian-Syung Lan,et al.  Competitive parameter optimization of multi-quality CNC turning , 2009 .

[17]  B. Lee,et al.  Modeling the surface roughness and cutting force for turning , 2001 .

[18]  Hans-Jürgen Zimmermann,et al.  Fuzzy Set Theory - and Its Applications , 1985 .

[19]  R. Venkata Rao,et al.  Machinability evaluation of work materials using a combined multiple attribute decision-making method , 2006 .

[20]  Janez Kopac,et al.  Optimal machining parameters for achieving the desired surface roughness in fine turning of cold pre-formed steel workpieces , 2002 .

[21]  Zülal Güngör,et al.  Using fuzzy decision making system to improve quality-based investment , 2007, J. Intell. Manuf..

[22]  J. Paulo Davim,et al.  Optimisation of cutting conditions in machining of aluminium matrix composites using a numerical and experimental model , 2001 .