Multiple Activation Functions and Data Augmentation-Based Lightweight Network for In Situ Tool Condition Monitoring

Abstract

Intelligent manufacturing raises higher requirements for tool condition monitoring (TCM) in terms of accuracy, robustness, and adaptability. At present, direct methods based on image processing and deep learning have made breakthroughs in TCM. However, some issues, such as image quality, model parameters, and dataset scale in the abovementioned methods, restrict industrial applications of TCM. Regarding the abovementioned issue, the purpose of this article is to propose a lightweight network model based on multiple activation functions to promote the intelligent industrial application of TCM. First, the image quality mechanism caused by complex working conditions is analyzed in industrial environments. Correspondingly, data augmentation is adopted to solve the problem of data scale under the premise of ensuring data quality and richness. Then, the adaptive activation function and the hard version of swish are introduced at the front and second half of the network to avoid information loss and reduce the activation function cost. Finally, a lightweight network based on cloud-edge collaboration for TCM is constructed. The model is iteratively optimized in the cloud and inferenced on the edge embedded device. The accuracy and adaptability of the proposed network are verified by accelerating milling cutter life under multiple working conditions.