Online real-time machining chatter sound detection using convolutional neural network by adopting expert knowledge

In machining processes, chatter detection remains a pivotal challenge, impacting both the quality and efficiency of manufacturing operations. This study introduces an approach that synergizes expert knowledge with the capabilities of advanced convolutional neural networks (CNNs) to enhance chatter detection. A comprehensive monitoring framework is proposed to adopt expert knowledge that digitizes machine tool and sound data, effectively labeling chatter events. This study merges human expertise in identifying milling tool chatter sounds with CNN architecture, marking a notable advancement in blending machining insights of experts with modern artificial intelligence (AI) technologies. The proposed chatter prediction architecture is distinguished by its incorporation of an attention block, fusing outputs from the AlexNet model with cutting parameters. This model outshines baseline models in both in-distribution (ID) and out-of-distribution (OOD) testing datasets. In OOD testing, the proposed model achieved an impressive accuracy of 94.51%, markedly surpassing the standalone CNN model's accuracy of 88.66%. Real-time 3D visualization of machining operations is demonstrated through the successful implementation of the trained model on a Raspberry Pi. (c) 2024 The Authors. Published by ELSEVIER Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)