# In-situ process monitoring and adaptive quality enhancement in laser additive manufacturing: a critical review

arXiv: https://arxiv.org/abs/2404.13673 · HTML: https://mlsanity.com/paper/2404.13673/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2024.

## Built on by (most cited first, 5 of 5)

- [Audio-visual cross-modality knowledge transfer for machine learning-based in-situ monitoring in laser additive manufacturing](https://mlsanity.com/paper/2408.05307/) (2024)
- [An Efficient and Uncertainty-aware Reinforcement Learning Framework for Quality Assurance in Extrusion Additive Manufacturing](https://mlsanity.com/paper/2503.00971/) (2025)
- [Redundancy Analysis and Mitigation for Machine Learning-Based Process Monitoring of Additive Manufacturing](https://mlsanity.com/paper/2504.21317/) (2025)
- [Feature-Aware Anisotropic Local Differential Privacy for Utility-Preserving Graph Representation Learning in Metal Additive Manufacturing](https://mlsanity.com/paper/2604.05077/) (2026)
- [LLM-ADAM: A Generalizable LLM Agent Framework for Pre-Print Anomaly Detection in Additive Manufacturing](https://mlsanity.com/paper/2605.03328/) (2026)

## Abstract (arXiv)

Laser Additive Manufacturing (LAM) presents unparalleled opportunities for fabricating complex, high-performance structures and components with unique material properties. Despite these advancements, achieving consistent part quality and process repeatability remains challenging. This paper provides a comprehensive review of various state-of-the-art in-situ process monitoring techniques, including optical-based monitoring, acoustic-based sensing, laser line scanning, and operando X-ray monitoring. These techniques are evaluated for their capabilities and limitations in detecting defects within Laser Powder Bed Fusion (LPBF) and Laser Directed Energy Deposition (LDED) processes. Furthermore, the review discusses emerging multisensor monitoring and machine learning (ML)-assisted defect detection methods, benchmarking ML models tailored for in-situ defect detection. The paper also discusses in-situ adaptive defect remediation strategies that advance LAM towards zero-defect autonomous operations, focusing on real-time closed-loop feedback control and defect correction methods. Research gaps such as the need for standardization, improved reliability and sensitivity, and decision-making strategies beyond early stopping are highlighted. Future directions are proposed, with an emphasis on multimodal sensor fusion for multiscale defect prediction and fault diagnosis, ultimately enabling self-adaptation in LAM processes. This paper aims to equip researchers and industry professionals with a holistic understanding of the current capabilities, limitations, and future directions in in-situ process monitoring and adaptive quality enhancement in LAM.