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Climate·G Space·Evidence-backed gain·Published 2026-09-03

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

Abstract: 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-centric…

TRV-2026-0970Peer-reviewedPermanent record — cite & verify
Explainable AI-Supported Cyber-Physical Collaboration for Sustainable Manufacturing in Industry 5.0

Resilient and fractionated cyber physical system by Connett, Brian. Public domain

The quick read

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.

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. 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.

Main points
  • 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.
Gain

Explainable AI-Supported Cyber-Physical Collaboration for Sustainable Manufacturing in Industry 5.0: Experimental findings show that the proposed CNN-Transformer architecture achieves 98.5% accuracy and outperforms existing CPHS systems while maintaining low latency.

The rundown

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.

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