Machining using vertical CNC end mill is popular in the modern material removal industries because of its ability to remove the material at a fast rate with a reasonably good surface quality. In this work, the influence of important common machining process variables like feed, cutting speed and axial depth of cut on the output parameters such as surface roughness and amplitude of tool vibration levels in Al-6061 workpieces has been studied. With the use of experimental result analysis and mathematical modelling, correlations between the cutting process conditions and process outputs are studied in detail. The cutting experiments are planned with response surface methodology (RSM) using Box-Behnken design (BBD). This work proposes a multi-objective optimization approach based on genetic algorithms using experimental data so as to simultaneously minimize the tool vibration amplitudes and work-piece surface roughness. The optimum combination of process variable is further verified by the radial basis neural network model. Finally, based on the multi-objective optimization approach and neural network models an interactive platform is developed to obtain the correct combination of process parameters.

中文翻译：

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多目标方法在Al-6061工件端铣中的最佳工作参数选择

在现代材料去除行业中，使用立式CNC立铣刀进行机械加工是很流行的，因为它能够以合理的良好表面质量快速去除材料。在这项工作中，研究了重要的常见加工工艺变量（如进给，切削速度和切削轴向深度）对输出参数（如Al-6061工件中的表面粗糙度和刀具振动水平的幅度）的影响。利用实验结果分析和数学建模，详细研究了切削过程条件与过程输出之间的相关性。使用Box-Behnken设计（BBD）的响应面方法（RSM）计划切割实验。这项工作提出了一种基于遗传算法并使用实验数据的多目标优化方法，以同时最小化刀具振动幅度和工件表面粗糙度。径向基神经网络模型进一步验证了过程变量的最佳组合。最后，基于多目标优化方法和神经网络模型，开发了一个交互式平台以获得过程参数的正确组合。