Data imputation strategies for process optimization of laser powder bed fusion of Ti6Al4V using machine learning

¹ Singapore Centre for 3D Printing, School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue 639798, Singapore

² HP-NTU Digital Manufacturing Corporate Lab, Nanyang Technological University, Singapore

³ School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue 639798, Singapore

MSAM 2023, 2(1), 50 https://doi.org/10.36922/msam.50

Submitted: 1 February 2023 | Accepted: 7 March 2023 | Published: 22 March 2023

© 2023 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )

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Abstract

A database linking process parameters and material properties for additive manufacturing enables the performance of the material to be determined based on the process parameters, which are useful in the design and fabrication stage of a product. The data, however, are often incomplete as each individual research work focused on certain process parameters and material properties due to the wide range of variables available. Imputation of missing data is thus required to complete the material library. In this work, we attempt to collate the data of Ti6Al4V, a popular alloy used in aerospace and biomedical industries, fabricated using powder bed fusion, or commonly known as selective laser melting (SLM). Various imputation techniques of missing data of the SLM Ti6Al4V dataset, such as the k-nearest neighbor (kNN), multivariate imputation by chained equations, and graph imputation neural network (GINN) are investigated in this article. It was observed that kNN performed better in imputing variables related to process parameters, whereas GINN performed better in variables related to material properties. To further improve the quality of imputation, a strategy to use the median of the imputed values obtained from the three models has resulted in significant improvement in terms of the relative mean square error. Self-organizing map was used to visualize the relationship among the process parameters and the material properties.

Keywords

Additive manufacturing

3D printing

Selective laser melting

Powder bed fusion

Machine learning

Data analytics

Imputation

Funding

This research is supported by the National Research Foundation, Prime Minister’s Office, Singapore under its Medium-Sized Centre funding scheme.

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Editorial Disclosure

Wai Yee Yeong serves as the Editorial Board Member of the journal, but did not in any way involve in the editorial and peer-review process conducted for this paper, directly or indirectly.

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