In-situ process monitoring for defect detection in wire-arc additive manufacturing: an agentic AI approach

2026-04-10

AI Summary

This study addresses the challenge of real-time detection of defects such as porosity in wire arc additive manufacturing (WAAM) by proposing an in situ monitoring framework that integrates multi-agent systems with a large language model (LLM). The framework synergistically combines a process-monitoring agent driven by electrical signals (current/voltage) and a defect-detection agent guided by acoustic emissions, leveraging X-ray computed tomography as ground-truth data. Through LLM-mediated coordination, the dual-modal agents achieve collaborative decision-making. This work represents the first application of a multi-agent architecture coupled with an LLM to WAAM in situ monitoring, demonstrating superior performance over single-agent approaches with a decision accuracy of 91.6%, an F1 score of 0.821, and an inference quality rating of 3.74 out of 5 across 15 independent experiments.

Technology Category

• Multiagent Systems: Agent Communication
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

AI agents are being increasingly deployed across a wide range of real-world applications. In this paper, we propose an agentic AI framework for in-situ process monitoring for defect detection in wire-arc additive manufacturing (WAAM). The autonomous agent leverages a WAAM process monitoring dataset and a trained classification tool to build AI agents and uses a large language model (LLM) for in-situ process monitoring decision-making for defect detection. A processing agent is developed based on welder process signals, such as current and voltage, and a monitoring agent is developed based on acoustic data collected during the process. Both agents are tasked with identifying porosity defects from processing and monitoring signals, respectively. Ground truth X-ray computed tomography (XCT) data are used to develop classification tools for both the processing and monitoring agents. Furthermore, a multi-agent framework is demonstrated in which the processing and monitoring agents are orchestrated together for parallel decision-making on the given task of defect classification. Evaluation metrics are proposed to determine the efficacy of both individual agents, the combined single-agent, and the coordinated multi-agent system. The multi-agent configuration outperforms all individual-agent counterparts, achieving a decision accuracy of 91.6% and an F1 score of 0.821 on decided runs, across 15 independent runs, and a reasoning quality score of 3.74 out of 5. These in-situ process monitoring agents hold significant potential for autonomous real-time process monitoring and control toward building qualified parts for WAAM and other additive manufacturing processes.

Problem

• defect detection
• wire-arc additive manufacturing
• in-situ process monitoring
• porosity
• additive manufacturing

Innovation

• agentic AI
• in-situ process monitoring
• wire-arc additive manufacturing
• multi-agent system
• defect detection