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.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
years
2026 2verdicts
UNVERDICTED 2representative citing papers
MAKA is a physics-grounded multi-agent system that raises multi-step tool execution success by up to 87.5 percentage points and enables traceable compensations that reduce simulated surface deviations from ~0.01 in to ~0.001 in on Ti-6Al-4V rotor blades.
citing papers explorer
-
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.
-
Physics-Grounded Multi-Agent Architecture for Traceable, Risk-Aware Human-AI Decision Support in Manufacturing
MAKA is a physics-grounded multi-agent system that raises multi-step tool execution success by up to 87.5 percentage points and enables traceable compensations that reduce simulated surface deviations from ~0.01 in to ~0.001 in on Ti-6Al-4V rotor blades.