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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2026 3representative citing papers
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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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.