A multi-agent AI framework using processing and acoustic agents achieves 91.6% accuracy and 0.821 F1 score for in-situ porosity defect detection in wire-arc additive manufacturing.
Safe human –robot collaboration for industrial settings: A survey,
4 Pith papers cite this work. Polarity classification is still indexing.
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2026 4representative citing papers
DS-MTNet uses frequency-constrained source decomposition and reusable information slots to jointly decode three EEG-based cognitive readouts in a driving task, outperforming single-task and multi-task baselines.
A combined SHARD and STPA hazard analysis of a mammography support robot reveals interaction-based risks and translates them into safety constraints that reduce dependence on perfect human timing.
Neural networks and random forests predict surface roughness from laser parameters and material data with high accuracy, speeding up optimization and reducing experimental effort.
citing papers explorer
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In-situ process monitoring for defect detection in wire-arc additive manufacturing: an agentic AI approach
A multi-agent AI framework using processing and acoustic agents achieves 91.6% accuracy and 0.821 F1 score for in-situ porosity defect detection in wire-arc additive manufacturing.
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DS-MTNet:Structured Multi-Task EEG Decoding for Human-Machine Collaboration
DS-MTNet uses frequency-constrained source decomposition and reusable information slots to jointly decode three EEG-based cognitive readouts in a driving task, outperforming single-task and multi-task baselines.
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Hazard Management in Robot-Assisted Mammography Support
A combined SHARD and STPA hazard analysis of a mammography support robot reveals interaction-based risks and translates them into safety constraints that reduce dependence on perfect human timing.
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Enhancing Laser Surface Texturing through Advanced Machine Learning Techniques
Neural networks and random forests predict surface roughness from laser parameters and material data with high accuracy, speeding up optimization and reducing experimental effort.