ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025
High-Speed 3D Printing Coupled with Machine Learning to Accelerate Alloy Development for Additive Manufacturing.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- RBF-assisted surrogate modeling and machine learning for mechanical property prediction in friction stir additive manufacturing: Application to dissimilar AA6061/AA7075 aluminum alloys.Scientific reports · 2026Article
- Functional personalization in digital manufacturing: a translational framework for patient-specific implants, arthroscopic locators, and therapeutic constructs.Frontiers in bioengineering and biotechnology · 2026Article
- Advanced Light Metal and Alloys: Preparation, Characterization, and Applications.Materials (Basel, Switzerland) · 2025Article
- Mechanism-Aware Digital Twin for High-Temperature Creep Prediction in Mo-Re Alloys.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- High-Speed 3D Printing Coupled with Machine Learning to Accelerate Alloy Development for Additive Manufacturing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Developing novel alloys for 3D printing of metals is a time- and resource-intensive challenge. High-throughput 3D printing and material characterization protocols are used in this work to rapidly screen a wide range of chemical compositions and processing conditions. In situ, alloying of high-strength steel with pure Al in the targeted range of 0-10 wt.% and flexible adjustment of the volumetric energy input is performed to derive 20 individual alloy combinations. These conditions are characterized using large-area crystallographic analysis combined with chemistry and nanoindentation protocols. The significant influence of Al content and processing conditions on the constitutive material behavior of the metastable base alloy allowed for efficient exploration of the underlying process-structure-properties (PSP) relationships. The extracted PSP relations are discussed based on the dominant physical mechanisms observed in the samples. Furthermore, the microstructure-property relationship based on limited experimental data is supported by an explainable machine-learning approach.
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Registered trials
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