The need to monitor tool wear is crucial, particularly in advanced manufacturing industries, as it aims to maximise the lifespan of the cutting tool whilst guaranteeing the quality of workpiece to be manufactured. Although there have been many studies conducted on monitoring the health of cutting tools under a specific cutting condition, the monitoring of tool wear across multi-cutting conditions still remains a challenging proposition. In addressing this, this paper presents a framework for monitoring the health of the cutting tool, operating under multi-cutting conditions. A predictive model, using advanced machine learning methods with multi-feature multi-model ensemble and dynamic smoothing scheme, is developed. The applicability of the framework is that it takes into account machining parameters, including depth of cut, cutting speed and feed rate, as inputs into the model, thus generating the key features for the predictions. Real data from the machining experiments were collected, investigated and analysed, with prediction results showing high agreement with the experiments in terms of the trends of the predictions as well as the accuracy of the averaged root mean squared error values.

监控刀具磨损的需求至关重要，尤其是在先进制造业中，因为它旨在最大程度地延长切削刀具的使用寿命，同时又保证待加工工件的质量。尽管已经进行了许多有关在特定切削条件下监控切削刀具健康状况的研究，但是在多切削条件下监控刀具磨损仍然是一个具有挑战性的主张。为了解决这个问题，本文提出了一个用于监视在多切削条件下运行的切削工具的健康状况的框架。利用先进的机器学习方法，多特征多模型集成和动态平滑方案，开发了一种预测模型。该框架的适用性是考虑到加工参数，包括切削深度，切削速度和进给速度，作为模型的输入，从而生成预测的关键特征。收集，研究和分析了来自加工实验的真实数据，预测结果在预测趋势以及平均均方根误差值的准确性方面与实验高度吻合。