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Optimization of Surface Roughness of AISI 1040 Stainless Steel in Milling Process Using Taguchi Method
Author(s) -
Neslihan Özsoy,
Murat Özsoy
Publication year - 2018
Publication title -
sakarya university journal of science
Language(s) - English
Resource type - Journals
eISSN - 2147-835X
pISSN - 1301-4048
DOI - 10.16984/saufenbilder.466053
Subject(s) - taguchi methods , surface roughness , materials science , machining , orthogonal array , surface finish , response surface methodology , design of experiments , composite material , metallurgy , mechanical engineering , engineering , computer science , mathematics , statistics , machine learning
Surface roughness significantly affects the work efficiency and life of machine parts interacting with each other. There are many parameters that affect surface roughness such as processed material, cutting tool, cutting parameters, cooling type. For this reason, optimization of the machining parameters and proper processing conditions are very important. In this study, the optimum machining conditions were determined by investigating the surface roughness of the milled AISI 1040 steel alloy depending on the feed per tooth (0.08 mm/tooth, 0.12 mm/tooth, 0.16 mm/tooth, 0.20 mm/tooth), spindle speed (2000 rpm, 3000 rpm, 40000 rpm, 5000 rpm) and the cooling type (liquid, air) parameters. The Taguchi experimental design method was used to help achieve results at acceptable levels and to save time and cost in achieving optimal results. Experiments designed with the Taguchi method were based on the L16 orthogonal array and signal / noise (S/N) ratios were used in the evaluation of the experimental results. The optimum levels of the control factors for minimizing the surface roughness using S/N rates were determined. The optimal conditions for surface roughness were observed at A1B1C2 (i.e.,feed per tooth=0.08 mm/tooth and 2000 rpm spindle speed, cooling type=liquid). Variance analysis was also conducted in the study. According to the results of analyses, it was found that spindle speed was the most significant parameter for surface roughness. Finally, confirmation tests were conducted to check the success of the optimization.

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