Material-agnostic machine learning approach enables high relative density in powder bed fusion products

Jaemin Wang, Sang Guk Jeong, Eun Seong Kim, Hyoung Seop Kim and Byeong-Joo Lee ()
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Jaemin Wang: Pohang University of Science and Technology (POSTECH)
Sang Guk Jeong: Pohang University of Science and Technology (POSTECH)
Eun Seong Kim: Pohang University of Science and Technology (POSTECH)
Hyoung Seop Kim: Pohang University of Science and Technology (POSTECH)
Byeong-Joo Lee: Pohang University of Science and Technology (POSTECH)

Nature Communications, 2023, vol. 14, issue 1, 1-12

Abstract: Abstract This study introduces a method that is applicable across various powder materials to predict process conditions that yield a product with a relative density greater than 98% by laser powder bed fusion. We develop an XGBoost model using a dataset comprising material properties of powder and process conditions, and its output, relative density, undergoes a transformation using a sigmoid function to increase accuracy. We deeply examine the relationships between input features and the target value using Shapley additive explanations. Experimental validation with stainless steel 316 L, AlSi10Mg, and Fe60Co15Ni15Cr10 medium entropy alloy powders verifies the method’s reproducibility and transferability. This research contributes to laser powder bed fusion additive manufacturing by offering a universally applicable strategy to optimize process conditions.

Date: 2023
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DOI: 10.1038/s41467-023-42319-x

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