A transformer-based end-to-end data-driven model for multisensor time series monitoring of machine tool condition

Abstract

Online determination of a cutter’s health status is crucial for the attainment of condition-based automated tool change in computer numerically controlled (CNC) machining. Due to the impracticalities associated with direct condition measurements, data-based modeling of monitoring signals provides a viable practical route. However, the highly noisy and redundant nature of the associ ated data impacts negatively on model’s accuracy and typically calls for addi tional initial preprocessing before modeling. Additionally, the long sequential data entails widely varying condition distributions exhibited by different cutters, even from the same batch on similar machining parameters, posing a challenge to model generalization. An end-to-end model has thus been developed to work directly on unprocessed data to establish global sensitive features from varying distributions for online tool wear estimation in CNC machining. The model uti lizes three main functional blocks. First, a data denoising and feature selection block automatically processes raw multisensor data directly, dispensing with scaling or preprocessing of inputs as conventionally done. Each sensor chan nel’s independence is preserved at initial processing ensuring complementary information from different sensors is utilized while simultaneously minimizing existing redundancies. The weighted denoised data is then processed through a transformer encoder block for determination of global dependencies in the time-series sequence, regardless of the time-step position. The learned features are then fed to an upper supervised learning block for association with the mon itored wear condition. The developed model works directly on raw noisy data irrespective of scaling differences, saving on preprocessing computational cost. The global associations extracted on long sequences by the transformer-encoder allow for model generalization to varying wear distributions. The parallel pro cessing structure of all channels ensures complementary information is utilized minimizing unforeseen model bias. The model’s performance as evaluated on