AI-driven laser material processing and precision manufacturing

Artificial intelligence (AI) is rapidly transforming laser material processing and precision manufacturing by enabling adaptive, autonomous, and data-driven control of complex laser–matter interactions. This Special Topic Collection focuses on recent advances at the intersection of AI, data science, and laser-based manufacturing, spanning fundamental understanding to real-world implementation.

Laser processing inherently involves highly nonlinear, multiscale phenomena in which outcomes depend sensitively on process parameters, material properties, and environmental conditions. Conventional trial-and-error approaches are often inefficient and limited in scalability. AI-driven approaches—including machine learning, deep learning, reinforcement learning, and digital twins—offer new pathways for predictive modeling, real-time process optimization, defect detection, and closed-loop control.

This collection aims to highlight breakthroughs in AI-enabled laser processing across a wide range of applications, including additive manufacturing, nanomanufacturing, surface engineering, micro- and nanofabrication, and advanced functional materials. Contributions addressing autonomous manufacturing systems, in situ monitoring, physics-informed AI models, and hybrid computational–experimental frameworks are particularly encouraged.

We welcome original research articles, reviews, and perspectives that demonstrate how AI can enhance precision, efficiency, scalability, and functionality in laser material processing. This Special Topic will serve as a platform to bring together researchers from photonics, materials science, manufacturing, and artificial intelligence to define the future of intelligent laser-based manufacturing systems.

Topics covered include, but are not limited to:

Guest Editors

Masoud Mahjouri Samani, Auburn University

Anming Hu, University of Tennessee Knoxville

Sumner Harris, Oak Ridge National Laboratory