Multiple Response Prediction and Optimization in Thin-Walled Milling of 6061 Aluminum Alloy
Received: 10 January 2023 | Revised: 4 February 2023 | Accepted: 15 February 2023 | Online: 2 April 2023
Corresponding author: Van Canh Nguyen
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.
Keywords:
thin-walled milling, multiple objective optimization, desirable function approach, Taguchi method, 6061 aluminum alloyDownloads
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Copyright (c) 2023 Van Que Nguyen, Hoang Tien Dung, Van Thien Nguyen, Van Dong Pham, Van Canh Nguyen
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