Next-generation laser micromachining: AI tools for process parameter prediction on stainless steel, brass, and titanium

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Authors: Céline Petit, Nathanaël Mariaule, Sven Wauters, Arnaud De Decker, David Bruneel, Anne Henrottin
Cited by: 0

Abstract

Optimizing laser micromachining processes is often complex and time-consuming, requiring many tests to fine-tune parameters like pulse energy, speed, or frequency—especially when high precision and productivity are key. To simplify this, we developed an AI-powered solution that predicts optimal process parameters based on a dedicated database and machine learning models. This tool dramatically reduces testing time, cuts development costs, and speeds up process optimization—helping manufacturers quickly reach their quality and productivity goals. In this presentation, we will showcase how this solution accelerates laser micromachining development while reducing resource use.

Fields of Study & Subjects

Advanced Machining and Optimization Techniques Laser Material Processing Techniques Advanced machining processes and optimization

Publication details

Journal / source
Laser-based Micro- and Nanoprocessing
Pages
39–39
Publication date
4 March 2026
Language
EN