TOOL CONDITION MONITORING IN MACHINING USING ROBUST RESIDUAL NEURAL NETWORKS

Abstract

Tool condition monitoring (TCM) aims to improve process efficiency, quality and tool maintenance costs by monitoring critical variables such as tool wear. This study proposes a deep learning (DL) architecture based on process-informed robust residual networks (Robust-ResNet) to predict tool wear in milling processes using time series of internal computer numerical control (CNC) signals. The Robust-ResNet architecture uses skip connections to move through multiple convolutional layers, avoiding the vanishing gradient problem of other neural network algorithms. The study includes an evaluation of the binding of process information as input to the architecture and an attention mechanism between skips to make more robust predictions. The proposed architecture has been trained and optimised using an open access data set of face milling time series. In this particular case, AC and DC signals have been used together with the corresponding tool wear values. The results of this study demonstrate the benefits of using deep learning techniques in the prediction of tool wear using internal signals provided by the CNC itself. The implementation of the proposed architecture is expected to help reduce maintenance costs, improve product quality and increase production efficiency in milling manufacturing processes.