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

arXiv: https://arxiv.org/abs/2604.09889 · HTML: https://mlsanity.com/paper/2604.09889/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 103 references that are in the map.
- First posted on arXiv 10 Apr 2026.

## Builds on (most cited first, 26 of 26)

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- [LLM Trainer: Automated Robotic Data Generation via Demonstration Augmentation using LLMs](https://mlsanity.com/paper/2509.20070/) (2025)
- [Agentic Additive Manufacturing Alloy Evaluation](https://mlsanity.com/paper/2510.02567/) (2025)
- [Unsupervised Welding Defect Detection Using Audio And Video](https://mlsanity.com/paper/2409.02290/) (2024)
- [LLM-Drone: Aerial Additive Manufacturing with Drones Planned Using Large Language Models](https://mlsanity.com/paper/2503.17566/) (2025)
- [Advancing Additive Manufacturing through Deep Learning: A Comprehensive Review of Current Progress and Future Challenges](https://mlsanity.com/paper/2403.00669/) (2024)

## Abstract (arXiv)

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.