  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.
