Productivity has always been a major concern in the industry. It can be improved by increasing material removal rate. Regenerative chatter during machining is the major obstacle to attain this. In the present work, a methodology has been proposed to select a proper combination of input cutting parameters for stable turning with improved metal removal rate (MRR). Chatter signals generated during the turning of Al 6061 have been acquired using a microphone. Initially, acquired signals have been processed using local mean decomposition (LMD) signal processing technique. The decomposed signals have been analyzed using different statistical chatter indicators considering Nakagami distribution approach for ascertaining the thresholds of chatter severity. Prediction models of most effective statistical chatter indicator and MRR have been developed using an artificial neural network (ANN). Moreover, this prediction models have been optimized using multi-objective genetic algorithm for ascertaining the optimal range of cutting parameters for stable turning with higher MRR. Finally, obtained stable range has been validated by performing more experiments.

生产力一直是业界的主要关注点。可以通过增加材料去除率来改善它。加工过程中的再生颤动是实现这一目标的主要障碍。在当前的工作中，已经提出了一种方法来选择输入切削参数的适当组合，以稳定的车削并提高金属去除率（MRR）。在Al 6061转向过程中产生的颤动信号已使用麦克风获取。最初，已使用局部均值分解（LMD）信号处理技术来处理获取的信号。考虑到中震分布方法来确定震颤严重度的阈值，已使用不同的统计震颤指标对分解后的信号进行了分析。使用人工神经网络（ANN）已开发出最有效的统计颤动指标和MRR的预测模型。此外，已使用多目标遗传算法优化了该预测模型，以确定具有较高MRR的稳定车削的切削参数的最佳范围。最后，通过进行更多的实验验证了获得的稳定范围。