Multiple Response Prediction and Optimization in Thin-Walled Milling of 6061 Aluminum Alloy

Abstract

In this study, the multi-objective optimization method for thin-wall milling of 6061 aluminum alloy is addressed. The technological parameters including the cutting speed Vc, the feed of tooth fz, and the width of cut ar are considered input variables, while the manufacturing responses are surface roughness Ra, production rate MRR, and flatness deviation FL. The goal is to find the optimum cutting parameters to minimize Ra and FL and maximize MRR, at the same time. To solve this problem, the desirability function approach was used based on Taguchi orthogonal array. Twenty-seven experiments were conducted and the measured data were collected. The mathematical regression models for responses Ra, MRR, and FL were then generated and evaluated by using the analysis of variance method. Then, the multiple objective optimization problems were solved by using the desirability function approach. The optimum cutting parameters set are Vc=120m/min, fz=0.06mm, and ar=0.13131mm, corresponding to Ra=0.1613µm, MRR=17197.45cm3/min, and FL=0.0995mm.

Development of Surface Roughness Prediction and Monitoring System in Milling Process

“…Cutting parameters such as axial depth of cut, spindle speed, and feed rate were shown to have great impacts on surface quality [8][9][10]. The optimization of cutting conditions is a prerequisite for producing better surface accuracy [11][12][13][14]. Therefore, monitoring the surface quality within the desired range is of great importance and worthy of investigation.…”