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TRUVACE RECORD VERSION
record: TRV-2026-0970
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-09-03T06:03:15.265398Z
status: published
lens: g_space
sector: climate
headline: Explainable AI-Supported Cyber-Physical Collaboration for Sustainable Manufacturing in Industry 5.0
dek: 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…
gain_title: 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.
problem_title: (none)
trace_subject: (none)
gain_reading: 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.
gain_evidence: (none)
problem_reading: (none)
problem_evidence: (none)
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.
limitation: 
tag: Evidence-backed gain
key_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.
rundown: 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.
sources:
- peer_reviewed | Journal of Visualized Experiments | https://doi.org/10.3791/72379 | 2026-09-01
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