References
- Suresh PSV. A genetic algorithm approach for optimization of surface roughness prediction model. Int. J. Mach. Tools Manuf. 2002;675–680.
- Sick B. On-line tool wear monitoring in turning using neural networks. Neural Comput. Appl. 1998;7:356–366.
- Benardos PG, Vosniakos G-C. Predicting surface roughness in machining: a review. Int. J. Mach. Tools Manuf. 2003;43:833–844.
- Zain AM, Haron H, Sharif S. Application of GA to optimize cutting conditions for minimizing surface roughness in end milling machining process. Expert Syst. Appl. 2010;4650–4659.10.1016/j.eswa.2009.12.043
- Lye, LM. Tools and toys for teaching design of experiments methodology. 33rd Annual General Conference of the Canadian Society for Civil Engineering; Toronto, Ontario, Canada; 2005.
- Montgomery DC. Design and analysis of experiments. New York, NY: Wiley; 2005.
- Tanco M, Viles E, Pozuet L. Comparing different approaches for design of experiments; 2009.
- Bagci E, Aykut Ş. A study of Taguchi optimization method for identifying optimum surface roughness in CNC face milling of cobalt-based alloy (stellite 6). Int. J. Adv. Manuf. Technol. 2006;29:940–947.10.1007/s00170-005-2616-y
- Öktem H, Erzurumlu T, Çöl M. A study of the Taguchi optimization method for surface roughness in finish milling of mold surfaces. Int. J. Adv. Manuf. Technol. 2006;28:694–700.10.1007/s00170-004-2435-6
- American National Standard. ASME/ANSIB46.1-1985 surface texture. New York: American Society of Mechanical Engineers; 1995.
- SHAPA Technical Bulletin. Guide to the selection of the surface finish of stainless steel on fabricated items; 2000.