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Designing alloys for additive manufacturing (AM) presents significant opportunities. Still, the chemical composition and processing conditions required for printability (ie., their suitability for fabrication via AM) are challenging to explore using solely experimental means. In this work, we develop a high-throughput (HTP) computational framework to guide the search for highly printable alloys and appropriate processing parameters. The framework uses material properties from stateof- the-art databases, processing parameters, and simulated melt pool profiles to predict processinduced defects, such as lack-of-fusion, keyholing, and balling. We accelerate the printability assessment using a deep learning surrogate for a thermal model, enabling a 1,000-fold acceleration in assessing the printability of a given alloy at no loss in accuracy when compared with conventional physics-based thermal models. We verify and validate the framework by constructing printability maps for the CoCrFeMnNi Cantor alloy system and comparing our predictions to an exhaustive ’in-house’ database. The framework enables the systematic investigation of the printability of a wide range of alloys in the broader Co-Cr-Fe-Mn-Ni HEA system. We identified the most promising alloys that were suitable for high-temperature applications and had the narrowest solidification ranges, and that was the least susceptible to balling, hot-cracking, and the formation of macroscopic printing defects. A new metric for the global printability of an alloy is constructed and is further used for the ranking of candidate alloys. The proposed framework is expected to be integrated into ICME approaches to accelerate the discovery and optimization of novel high-performance, printable alloys.
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This content will become publicly available on December 1, 2026
High-throughput alloy and process design for metal additive manufacturing
Many engineering alloys originally designed for conventional manufacturing lack considerations for additive manufacturing (AM), presenting opportunities for novel alloy designs. Evaluating alloy printability requires extensive analysis of chemical composition and processing conditions. The complexity of experimental exploration drives the need for high-throughput computational frameworks. This study introduces a framework that integrates material properties, processing parameters, and melt pool profiles from three thermal models to assess process-induced defects, such as lack-of-fusion, balling, and keyholing. A deep learning surrogate model accelerates the printability assessment by 1000 times without losing accuracy. We validate the framework with printability maps for the equiatomic CoCrFeMnNi system and apply it to explore printable alloys in the Co-Cr-Fe-Mn-Ni high-entropy alloy space. Ensemble probabilistic printability maps further provide insights into defect likelihood and uncertainty, enhancing alloy design for AM by efficiently navigating vast design spaces.
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- Award ID(s):
- 2323611
- PAR ID:
- 10657724
- Publisher / Repository:
- Nature
- Date Published:
- Journal Name:
- npj Computational Materials
- Volume:
- 11
- Issue:
- 1
- ISSN:
- 2057-3960
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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