Optimization of the end milling process is a combinatorial task due to the involvement of a large number of process variables and performance characteristics. Process-specific numerical models or mathematical functions are required for the evaluation of parametric combinations in order to improve the quality of the machined parts and machining time. This problem could be categorized as the offline data-driven optimization problem. For such problems, the surrogate or predictive models are useful, which could be employed to approximate the objective functions for the optimization algorithms. This paper presents a data-driven surrogate-assisted optimizer to model the end mill cutting of aluminum alloy on a desktop milling machine. To facilitate that, material removal rate (MRR), surface roughness (Ra), and cutting forces are considered as the functions of tool diameter, spindle speed, feed rate, and depth of cut. The principal methodology is developed using a Bayesian regularized neural network (surrogate) and a beetle antennae search algorithm (optimizer) to perform the process optimization. The relationships among the process responses are studied using Kohonen’s self-organizing map. The proposed methodology is successfully compared with three different optimization techniques and shown to outperform them with improvements of 40.98% for MRR and 10.56% for Ra. The proposed surrogate-assisted optimization method is prompt and efficient in handling the offline machining data. Finally, the validation has been done using the experimental end milling cutting carried out on aluminum alloy to measure the surface roughness, material removal rate, and cutting forces using dynamometer for the optimal cutting parameters on desktop milling center. From the estimated surface roughness value of 0.4651 μm, the optimal cutting parameters have given a maximum material removal rate of 44.027 mm³/s with less amplitude of cutting force on the workpiece. The obtained test results show that more optimal surface quality and material removal can be achieved with the optimal set of parameters.

由于涉及大量的过程变量和性能特征，因此优化端铣削过程是一项组合任务。评估参数组合需要特定于过程的数值模型或数学函数，以提高加工零件的质量和加工时间。此问题可以归类为离线数据驱动的优化问题。对于此类问题，替代模型或预测模型很有用，可用于近似优化算法的目标函数。本文提出了一种数据驱动的替代辅助优化器，以对台式铣床上的铝合金立铣刀切削进行建模。为此，材料去除率（MRR），表面粗糙度（Ra），切削力被认为是刀具直径，主轴转速，进给速度和切削深度的函数。使用贝叶斯正则化神经网络（代理）和甲虫触角搜索算法（优化器）开发主要方法，以执行过程优化。使用Kohonen的自组织图研究过程响应之间的关系。所提出的方法已成功地与三种不同的优化技术进行了比较，并显示出其优于MRR的性能，其MRR改善了40.98％，Ra改善了10.56％。所提出的替代辅助优化方法可以快速有效地处理离线加工数据。最后，验证是通过对铝合金进行的实验立铣刀切割来进行的，以测量表面粗糙度，材料去除率，使用测力计在台式铣削中心上获得最佳切削参数，从而获得最佳的切削力。根据估计的表面粗糙度值0.4651μm，最佳切削参数给出的最大材料去除率为44.027 mm³ / s，对工件的切削力幅度较小。获得的测试结果表明，使用最佳参数集可以实现更理想的表面质量和材料去除。