Design optimization for minimum technological parameters when dry turning of AISI D3 steel using Taguchi method

• Published in: International journal of advanced manufacturing technology 2017-03, Vol.89 (5-8), p.1915-1934
• Main Authors: Zerti, Oussama, Yallese, Mohamed Athmane, Khettabi, Riad, Chaoui, Kamel, Mabrouki, Tarek
• Format: Article
• Language: English
• Subjects: CAE) and Design; Cold work tool steels; Computer-Aided Engineering (CAD; Cutting force; Cutting parameters; Cutting speed; Design optimization; Engineering; Feed rate; Industrial and Production Engineering; Inserts; Machinability; Mathematical analysis; Mechanical Engineering; Media Management; Nose; Original Article; Orthogonal arrays; Parameter estimation; Production planning; Regression analysis; Surface roughness; Taguchi methods; Turning (machining); Variance analysis
• ISSN: 0268-3768; 1433-3015
• DOI: 10.1007/s00170-016-9162-7

The development in the manufacturing flied requires the continuous optimization using various methods. In order to minimize some technological output (such as surface roughness, tangential force, specific cutting force, and cutting power) characterizing material machinability, it is intended in the present paper to perform an optimizing approach of cutting parameters based on Taguchi method. Selected input cutting parameters are major cutting edge angle, cutting insert nose radius, cutting speed, feed rate, and depth of cut. The tests were performed on AISI D3 steel using mixed ceramic inserts under dry cutting conditions. A Taguchi L 18 orthogonal array is used to design the optimization experiment. The analysis of variance (ANOVA) is exploited to evaluate the foremost effects on the output parameters. The results indicate that both feed rate and cutting insert nose radius are the mainly influencing factors on surface roughness while both tangential force and specific cutting force are affected principally by depth of cut followed by feed rate. The most significant parameter affecting cutting power is depth of cut followed by cutting speed and feed rate. Regression equations are formulated for estimating predicted values of technological parameters. Optimal cutting parameters are determined using the signal-to-noise (S/N) ratio which was calculated for the precited technological output according to the “the smaller-the-better” approach. Based on the confirmation experiments and laboratory results, it is concluded that the Taguchi method is successfully adapted to describe the optimization of cutting parameters (inputs) for improved technological ones (output).