Toward practical tool wear prediction paradigm with optimized regressive Siamese neural network

Abstract

Data-driven models, such as deep learning and transfer learning algorithms, have achieved leading results in essential tool condition monitoring (TCM) during manufacturing process. However, these models perform differently under available dataset with various sample scales in these factories, leading to poor application extensiveness in engineering. Besides, Siamese neural network (SNN) has been applied in machine condition identification, but how to apply the model on tool wear regression has not attracted much attention. Therefore, a novel SNN framework, called global frequency context channel optimized regressive SNN — support vector regression (GFCC-RSNN-SVR), is proposed and optimized to predict tool wear values practically. Firstly, Stockwell transform is utilized for time–frequency image generation from raw signal. Then, regressive SNN framework is constructed based on convolutional neural blocks and enhanced by novel attention mechanism layer. After model training, support vector regression (SVR) submodel is constructed for final tool wear prediction. Moreover, milling experiments has been designed and conducted, and the dataset and public ones have been chosen for the following cases study. Further comparison experiments have validated the brilliant performance of the proposed model, and the effectiveness of Stockwell transform, RSNN framework, and the attention mechanism, regardless of training sample scales.