An imperative requirement of a modern machining system is to detect tool wear while machining to maintain the surface quality of the product. Vibration signatures emanating during machining with a single point cutting tool have proven to be good indicators for the tool’s health. The current research undertaken utilizes vibration signatures while turning EN9 and EN24 steel alloy to predict tool life using Artificial Neural Network (ANN). During initial meager experimentation, tool acceleration during machining was recorded, and the width of the flank wear at the end of each run was measured using Tool Makers Microscope. The recorded experimental data is utilized to develop the neural network with the variation of operating parameters and corresponding tool vibration with measured tool flank wear. The endeavor undertaken for the development of ANN flank wear prediction model was effective with a regression coefficient of 0.9964. The proposed methodology of indirect measurement of tool wear is efficient, economical for the machining industry to predict tool life, which in turn avoids catastrophic tool failure.

现代加工系统的一项必要要求是在加工时检测刀具磨损，以保持产品的表面质量。使用单点切削刀具加工过程中发出的振动信号已被证明是刀具健康状况的良好指标。当前进行的研究利用车削 EN9 和 EN24 钢合金时的振动特征，使用人工神经网络 (ANN) 来预测刀具寿命。在最初的微薄实验中，记录了加工过程中的刀具加速度，并使用刀具制造显微镜测量了每次运行结束时的后刀面磨损宽度。记录的实验数据用于开发神经网络，随着操作参数的变化和相应的工具振动以及测量的工具后刀面磨损。为开发 ANN 后刀面磨损预测模型所做的努力是有效的，回归系数为 0.9964。所提出的间接测量刀具磨损的方法对于机械加工行业来说是有效、经济的，可以预测刀具寿命，从而避免灾难性的刀具故障。