Multiobjective Optimization of Surface Roughness and Tool Wear in High-Speed Milling of AA6061 by Machine Learning and NSGA-II. (27th May 2022) - Nguyen, Anh-Tu; Nguyen, Van-Hai; Le, Tien-Thinh; Nguyen, Nhu-Tung
Abstract : This work addresses the prediction and optimization of average surface roughness (Ra) and maximum flank wear (Vbmax) of 6061 aluminum alloy during high-speed milling. The investigation was done using a DMU 50 CNC 5-axis machine with Ultracut FX 6090 fluid. Four factors were examined: the table feed rate, cutting speed, depth of cut, and cutting length. Three levels of each factor were examined to conduct 81 experiment runs. The response parameters in these experiments were measurements of Ra and Vbmax. We applied a two-pronged approach that combines machine learning (ML) and a Nondominated Sorting Genetic Algorithm (NSGA-II) to model and optimize Ra and Vbmax. Four ML models were used to predict Ra and Vbmax: linear regression (LIN), support vector machine regression (SVR), a gradient boosting tree (GBR), and an artificial neural network (ANN). The input variables were the significant factors that affect the surface quality and tool wear: the feed rate, depth of cut, cutting speed, and cutting time. Several quality metrics were employed to quantify the performance of the models, such as the root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). As a result, SVR and ANN were found to have the best predictive performance for Ra and Vbmax. These models and the NSGA-II-based approach were then employed for multiobjective optimization of cutting parameters during high-speed milling of aluminum 6061. Fifty Pareto solutions were found with Ra in the range of 0.257 to 0.308 µm and Vbmax in the range of 136.198 to 137.133  μ m. Experimental validations were then conducted to confirm that the optimum solution was within an acceptable error range. More precisely, the absolute percentage errors for Ra and Vbmax were 2.5% and 1.5%, respectively. This work proposes an effective strategy for efficiently combining machine learning techniques and the NSGA-II multiobjective optimization algorithm. The experimental validations have reflected the potential for applying this strategy in various machining-optimization problems.