Mehdi Jalali Azizpour - Department of Mechanic, Ahvaz branch, Islamic Azad University, Ahvaz, Iran
Ata Fardaghaie - Department of Mechanics, Ahvaz branch Islamic Azad University, Ahvaz, Iran

In this study, the main cutting parameters of high speed machining (HSM) including cutting speed, feed rate, depth of cut as well as deposition method were optimized using genetic algorithm considering the average surface roughness (Ra) of work piece and flank wear (Vb) of CVD and PVD coated tool criteria in high speed turning of hardened AISI ۴۱۴۰ Steel. Standard L۱۸ orthogonal array has been used for the design of experiment (DOE) applying Taguchi approach. Multiple linear regression model applying Minitab, was used to determine the relationship and interaction between machining parameters and outputs. For genetic algorithm(GA) optimization, the average was applied as a functional output of design of experiments. The results of GA for smaller- the better quality characterization shows the optimum roughness of ۱.۱۰۷ mm and optimum flank wear of ۰.۴۶۱mm. The confirmation tests were carried out in order to validate the response of predicted optimum condition. The results of validation test show a good agreement between obtained optimum condition and the results of genetic algorithm. The analysis of variance was used in order to obtain the contribution of each factor on the output statistically. ANOVA results indicated that the cutting speed and cut depth are the most effective factors on the flank wear by ۳۷.۰۲ and ۲۷.۸۰ percent contribution respectively.  The most effective factors on surface roughness were feed rate and cutting speed by ۸۲.۴۹ and ۱۰.۵۰ percent contribution respectively. Stereoscopy and Scanning electron microscopy was used to evaluate the wear mechanism and topography of worn surface. 

CVD, Flank wear, Genetic Algorithm, HSM, PVD, Roughness, Tool wear