基于机器学习和多目标算法的钛合金插铣优化

针对钛合金的插铣加工过程开展试验和优化研究。以材料去除率和切削力为目标，采用机器学习和多目标优化算法相结合的方法来优化插铣切削参数；以主轴转速、径向切削宽度、切削步距和每齿进给量为试验变量，采用田口方法对试验变量组进行缩减。将机器学习方法与传统一阶和二阶回归方法比较，发现机器学习有很好的预测精度且解集分布更合理。分别采用MOEA/D、NSGA-Ⅱ、SPEA2、NSPSO算法对问题进行求解，并比较它们的性能，结果表明NSGA-Ⅱ综合表现最佳。最后将优化结果与初始参照进行比较，发现优化结果可以显著提高材料去除率并减小切削力，达到了高效稳定加工的目的。

Plunge Milling of Titanium Alloys Based on Machine Learning and Multi-objective Optimization

Based on the experimental work and optimization research of plunge milling of titanium alloys, a hybrid method integrated with machine learning and multi-objective optimization algorithm was proposed herein to optimize the processing parameters considering material removal rate and cutting force as objectives. Spindle speed, radial cutting width, cutting step and feed per tooth were used as test variables. Taguchi method was used to reduce the number of test variable groups. The performance of the model given by machine learning was compared with traditional first-order and second-order regression models. Results show that machine learning has a better performance in prediction accuracies and the distributions of solutions. Four algorithms(MOEA/D, NSGA-Ⅱ, SPEA2, and NSPSO) were used and compared in solving the problem, and NSGA-Ⅱ shows a better comprehensive performance. Finally, the optimal results were compared with the initial reference. Results show that the optimal results may improve the material removal rate and reduce the cutting forces, which may help to achieve the goal of efficient and stable machining.