Rapid data acquisition and machine learning-assisted composition design of functionally graded alloys via wire arc additive manufacturing

Wang, Xin; Sridar, Soumya; Klecka, Michael; Xiong, Wei

doi:10.1038/s44334-025-00028-x

Rapid data acquisition and machine learning-assisted composition design of functionally graded alloys via wire arc additive manufacturing

Journal Article · Fri May 02 00:00:00 EDT 2025 · npj Advanced Manufacturing

DOI:https://doi.org/10.1038/s44334-025-00028-x· OSTI ID:2564315

Wang, Xin; Sridar, Soumya; Klecka, Michael; Xiong, Wei

Abstract

The lack of high-quality datasets in materials science hinders artificial intelligence (AI)-driven alloy design. To address this challenge, wire arc additive manufacturing (WAAM) was employed to fabricate graded alloys, generating extensive data for machine learning (ML)-assisted property prediction. ML models were developed using high-throughput experiments, computational models, and genetic algorithm to optimize feature selection, successfully predicting hardness and porosity. The ML model demonstrated its efficacy by designing a gradient alloy with enhanced properties. However, scaling up revealed uncertainties in tensile property and porosity due to differences in size and thermal conditions between the designed alloy build and the gradient print used to construct the ML model. This underscores the need for uncertainty quantification and process optimization in WAAM-driven alloy design. Our work advances AI-integrated additive manufacturing, offering a rapid approach to exploring process–structure–property relationships and accelerating materials development.

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Sponsoring Organization:: USDOE

Grant/Contract Number:: FE0031637

OSTI ID:: 2564315

Journal Information:: npj Advanced Manufacturing, Journal Name: npj Advanced Manufacturing Journal Issue: 1 Vol. 2; ISSN 3004-8621

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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