What question did this study set out to answer?

To show how simulations and machine learning can optimize laser beam welding parameters and outcomes.

June 12, 2026Open Access

Presentation: Predicting Laser Beam Welding Parameters

JNJakob David NeuhauserTU Wien LVLaszlo VörösTU Wien TFT. FlorianTU Wien

Key Points

To show how simulations and machine learning can optimize laser beam welding parameters and outcomes.
Presented high-fidelity multi-physics simulations for laser beam welding validated with experimental data.
Used a dataset from 73 CFD simulations of 316L stainless steel analyzing laser power and welding velocity.
Applied machine learning models to predict weld geometry from parameters and identify optimal parameters.
Demonstrated successful prediction of weld geometry including depth and width from welding parameters.
Showed how inverse problems can be solved to find suitable welding parameters for target weld geometries.

Abstract

This presentation was given at a workshop in Stuttgart on 10 February 2026 as part of the Lasers4MaaS project (EU Horizon Europe, grant agreement 101178719). It demonstrates how high-fidelity multi-physics simulations of laser beam welding, validated against experimental data, can be used to generate training datasets for data-driven reduced-order models (ROMs). Based on a dataset of 73 CFD simulations of 316L stainless steel covering laser power, welding velocity, and different beam shapes, the presentation shows how machine learning models (Neural Networks, Gaussian Process Regression, and Support Vector Regression) can solve both the forward problem of predicting the weld geometry (depth, width) from process parameters, and the inverse problem of identifying suitable welding parameters for a target weld geometry. Both a PDF version and the original PowerPoint file (including animations and videos) are provided.

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