Explainable AI-Supported Cyber-Physical Collaboration for Sustainable Manufacturing in Industry 5.0

In this study, we present an innovative approach to the sustainable manufacturing of industrial parts using an explainable artificial intelligence (XAI)- based cyber-physical collaboration system for Industry 5.0. Current cyber-physical human systems (CPHSs) have been found to integrate AI only to a limited extent and often lack explainability. Consequently, there is a need to improve their scalability and flexibility, in keeping with the tenets of Industry 5.0: resilience, long-term viability, and human-centricity. To address these shortcomings, we introduce an architecture that integrates deep learning and explainable AI into the Cyber-Physical System (CPS) ecosystem to enable smart, explainable decision-making. Specifically, we use the Malfunctioning Industrial Machine Investigation and Inspection (MIMII) dataset for machine fault detection from acoustic signals. Audio signals undergo spectral transformation to generate spectrogram images, which are then processed by a convolutional neural network (CNN) for spatial feature extraction and a transformer encoder for temporal features. To improve transparency and facilitate human-in-the-loop cooperation, the Shapley additive explanations (SHAP)-based approach helps operators understand the system's decision-making process by highlighting input features that influence model predictions. In addition, the framework is applied in an edge-computing-based cyber-physical system to achieve low latency and low energy consumption while providing timely responses, thereby helping ensure a sustainable production environment. Experimental findings show that the proposed CNN-Transformer architecture achieves 98.5% accuracy and outperforms existing CPHS systems while maintaining low latency. The use of explainable AI in such a cyber-physical cooperation system greatly increases operator confidence and enables seamless human-machine collaboration.