In-situ process monitoring for defect detection in wire-arc additive manufacturing: an agentic AI approach
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 defec...
| Authors | Pallock Halder, Satyajit Mojumder |
|---|---|
| Primary category | cs.AI |
| Published | 10 April 2026 |
| Last updated at source | 10 April 2026 |
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