Tool Wear Classification in Chipboard Milling Processes Using 1-D CNN and LSTM Based on Sequential Features

Abstract

The paper presents the comparative analysis of Long short-term memory (LSTM) and one-dimensional convolutional neural networks (1-D CNNs) for tool wear classification in chipboard milling processes. The complexity of sequence data in various fields makes selecting the right model for sequence classification very important. This research aims to show the distinct capabilities and performance nuances of LSTM and 1-D CNN models, leveraging their inherent strengths in understanding temporal dependencies and feature extraction, respectively. Through a series of experiments, the study unveils that while both models demonstrate competencies in handling sequence data, the 1-D CNN model, with its superior feature extraction capabilities, achieved the best performance, boasting an accuracy of 94.5% on the test dataset. The insights gained from this comparison not only help to understand LSTM and 1-D CNN models better, but also open the door for future improvements in using neural networks for complex sequence classification challenges.

Keywords

• tool condition monitoring (TCM)
• milling processes
• tool wear classification
• 1-D CNN
• long short-term memory (LSTM)
• sequential features