Tool wear represents a central challenge for manufacturing companies. The resulting workpiece rejects and machine downtimes cause significant costs. One difficulty lies in predicting the optimal tool change timing. In practice, two suboptimal scenarios occur: Either tools are changed too early, not fully utilizing their service life, or too late, which can result in quality losses or tool breakage. In the context of Industry 4.0 and manufacturing digitalization, large amounts of process data are continuously generated, enabling indirect process control of tool wear. The temporal dependence of process data and the multitude of influencing factors require the development of powerful analysis methods. This paper examines the development of a concept for detecting tool condition using a Transformer-based approach in milling and drilling processes. The captured motor current of the machine axes is analysed. The concept uses implicit labelling of training data, utilizing only sensor signals from unworn tools. The Transformer encoder learns a representation of the unworn machining state, based on which a linear decoder performs time series prediction. The reconstruction error, i.e., the deviation between predicted and actual values, serves as an indicator of tool condition. Statistical parameters of the reconstruction error enable quantitative comparison between normal and worn tool behaviour. Besides presenting the concept, the implementation, development of a suitable model architecture and determination of optimal hyperparameters are addressed.
Science Journal 