journal article·2026-05Open access

Temperature control in powder bed fusion of metals via inverse-designed laser beam shapes

Vijaya Holla, Jonas Grünewald, Richard Off, Ruihang Dai, Philipp Kopp, Katrin Wudy, Stefan Kollmannsberger

Abstract

Modern beam shaping technology provides much flexibility in customizing laser-based powder bed fusion of metals (PBF-LB/M). This work introduces several new laser beam shapes by gradient-based optimization of the temperature field obtained by a conduction-based simulation. The goal is to produce rectangular melt tracks with different levels of temperature control inside the melt pool. Choosing axi-symmetric, laterally symmetric, and unconstrained beam shape parametrizations automatically constrains the optimization to different machine capabilities and scan strategy requirements. The new beam shapes are experimentally reproduced using a highly flexible beam shaping setup based on spatial light modulators. The differences in melt track dimensions generally stay well below 20 % compared to micrograph images. The simulated surface temperatures agree well with the measurements performed by multispectral imaging (MSI), a novel technique for obtaining absolute temperature maps. Relaxing the beam shape constraints results in a closer match to the optimization target, highlighting the advantages of flexible beam shaping. Limiting the liquid temperatures reduces the simulated maximum temperature inside the melt pool by up to 46 %. The experimental validation of such a diverse set of inverse-designed laser beam shapes demonstrates that numerical optimization can guide advanced beam shaping for improved process control in PBF-LB/M. The presented methodology, including the optimization code, is made available under an MIT license.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Additive Manufacturing Materials and Processes · Additive Manufacturing and 3D Printing Technologies · Laser Material Processing Techniques

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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