Deep Learning Based Tool Wear Estimation Considering Cutting Conditions
This addresses tool wear monitoring for manufacturing industries, offering incremental improvements in accuracy and transferability.
The study tackled tool wear estimation in milling by proposing a deep learning approach that incorporates cutting conditions as inputs, resulting in improved accuracy and zero-shot transferability to new parameters, with consistent advantages over conventional models.
Tool wear conditions impact the final quality of the workpiece. In this study, we propose a deep learning approach based on a convolutional neural network that incorporates cutting conditions as extra model inputs, aiming to improve tool wear estimation accuracy and fulfill industrial demands for zero-shot transferability. Through a series of milling experiments under various cutting parameters, we evaluate the model's performance in terms of tool wear estimation accuracy and its transferability to new fixed or variable cutting parameters. The results consistently highlight our approach's advantage over conventional models that omit cutting conditions, maintaining superior performance irrespective of the stability of the wear development or the limitation of the training dataset. This finding underscores its potential applicability in industrial scenarios.