Auxiliary input-enhanced siamese neural network: A robust tool wear prediction framework with improved feature extraction and generalization ability

Abstract

Tool wear monitoring is essential for automated and resilient manufacturing, as it can prevent catastrophic failures caused by severe wear on cutting edges during machining. The conventional tool wear monitoring approaches depend on features extracted from signals, which require sensitive signals and consistent tool wear. In practical scenarios, however, neither the sensitivity of collected signals to the tool wear status nor the consistency of the actual tool wear evolution is hard to meet the requirement of the tool condition monitoring algorithm, which greatly limit the wide spread of its industrial applications. To overcome this challenge, we propose an Auxiliary Input-enhanced Siamese Neural Network (AISNN) framework by incorporating a Siamese structure into the feature extraction part of a convolutional neural network (CNN), and introducing an auxiliary input to its nonlinear regression part. The Siamese structure, instead of extracting features directly from signals, distinguishes the difference between the features extracted from signals of the examined cut and the first cut, and uses this difference as the indicator of tool wear status. Moreover, the auxiliary input provides an additional feature that has heavy dependence on the tool wear, which enables the model learning the general wear evolution of the examined cutting tool. The effectiveness of the proposed AISNN framework is verified in a set of milling experiments where input signal is insensitive to the flank wear of cutting tool and different tools’ wear evolution exhibits obvious inconsistency. Compared to the traditional CNN, the proposed AISNN significantly improves the accuracy on the verification set from 63% to 95% and on the testing set from 50% to 81%. The results demonstrate that the AISNN framework achieves significant improvement in feature extraction and generalization ability. The proposed AISNN, as a universal framework, can empower most existing deep learning-based tool wear prediction methods, enhancing their robustness in handling insensitive signals and inconsistent wear evolution and thereby promoting more industrial applications.