Application of orthogonal array technique and particle swarm optimization approach in surface roughness modification when face milling AISI1045 steel parts
الموضوعات :Masoud Azadi Moghaddam 1 , Farhad Kolahan 2
1 - Department of Mechanical Engineering, Ferdowsi University of Mashhad, P.O. Box 91775-111, Mashhad, Iran
2 - Department of Mechanical Engineering, Ferdowsi University of Mashhad, P.O. Box 91775-111, Mashhad, Iran
الکلمات المفتاحية: Face milling process . Surface roughness . Optimization . Particle swarm optimization (PSO) . Analysis of variance (ANOVA) . Taguchi approach,
ملخص المقالة :
Face milling is an important and common machining operation because of its versatility and capability to produce various surfaces. Face milling is a machining process of removing material by the relative motion between a work piece and rotating cutter with multiple cutting edges. It is an interrupted cutting operation in which the teeth of the milling cutter enter and exit the work piece during each revolution. This paper is concerned with the experimental and numerical study of face milling of AISI1045. The proposed approach is based on statistical analysis on the experimental data gathered using Taguchi design matrix. Surface roughness is the most important performance characteristics of the face milling process. In this study the effect of input face milling process parameters on surface roughness of AISI1045 steel milled parts have been studied. The input parameters are cutting speed (v), feed rate (fz) and depth of cut (ap ). The experimental data are gathered using Taguchi L9design matrix. In order to establish the relations between the input and the output parameters, various regression functions have been fitted on the data based on output characteristics. The significance of the process parameters on the quality characteristics of the process was also evaluated quantitatively using the analysis of variance method. Then, statistical analysis and validation experiments have been carried out to compare and select the best and most fitted models. In the last section of this research, mathematical model has been developed for surface roughness prediction using particle swarm optimization (PSO) on the basis of experimental results. The model developed for optimization has been validated by confirmation experiments. It has been found that the predicted roughness using PSO is in good agreement with the actual surface roughness.
