Chatter-free turning has been a matter of concern for engineers. Improper selection of cutting parameters leads to regenerative chatter and loss of productivity. In the present work, a methodology has been proposed to select an appropriate combination of input cutting parameters for stable turning with improved metal removal rate (MRR). Chatter signals generated during the turning of Al 6061 are acquired using a microphone and processed using local mean decomposition signal processing technique. Thereafter, these decomposed signals have been analyzed in order to extract tool chatter features. Prediction models of chatter indices and MRR have been developed using response surface methodology. Moreover, these 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.

中文翻译：

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使用声音信号以更高的金属去除率研究刀具颤振特性

无颤动的转弯一直是工程师关注的问题。切削参数选择不当会导致再生颤振和生产率下降。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）。使用麦克风获取在Al 6061旋转过程中产生的颤动信号，并使用局部均值分解信号处理技术进行处理。此后，对这些分解后的信号进行了分析，以提取工具的颤振特征。颤振指数和MRR的预测模型已使用响应面方法开发。此外，这些预测模型已使用多目标遗传算法进行了优化，以确定切削参数的最佳范围，以实现具有更高MRR的稳定车削。最后，通过进行更多的实验验证了获得的稳定范围。