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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Non-conformal immersed and union-based isogeometric methods with boundary-conformal quadrature reduce patch count and preprocessing for magnetostatics while union variants maintain accuracy on benchmarks.
A multi-agent LLM framework autonomously completes the full computational mechanics pipeline from a photograph to a code-compliant engineering report on a steel L-bracket example.
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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Immersed boundary-conformal isogeometric methods for magnetostatics
Non-conformal immersed and union-based isogeometric methods with boundary-conformal quadrature reduce patch count and preprocessing for magnetostatics while union variants maintain accuracy on benchmarks.
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From Perception to Autonomous Computational Modeling: A Multi-Agent Approach
A multi-agent LLM framework autonomously completes the full computational mechanics pipeline from a photograph to a code-compliant engineering report on a steel L-bracket example.