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

Guo Dong Goh, Xi Huang, Sheng Huang, Jia Li Janessa Thong, Jia Jun Seah, Wai Yee Yeong

Materials Science in Additive Manufacturing 2023 article cited by 22

Source Open-access PDF More in Engineering & Materials

importance 3/10 ai assisted Additive Manufacturing Materials and Processes additive manufacturing stage: research

Problem

Additive manufacturing datasets for SLM Ti6Al4V are incomplete because individual studies cover only some parameters, hindering process-property linkage.

Why it matters

Complete process-property databases are needed to design aerospace and biomedical parts.

What the AI did

Investigated kNN, multivariate imputation by chained equations, and graph imputation neural network (GINN) techniques to fill missing values in the SLM Ti6Al4V dataset.

Results

kNN observed to perform better than other imputation methods; no quantitative metrics in abstract.

Impact

Guides choice of imputation method for building SLM Ti6Al4V material-property databases.

Record

Venue	Materials Science in Additive Manufacturing
Published	2023
Work type	article
Authors	Guo Dong Goh, Xi Huang, Sheng Huang, Jia Li Janessa Thong, Jia Jun Seah, Wai Yee Yeong
Citations	22 (OpenAlex, at import time)
Model or method	graph imputation neural network (GINN); also kNN, MICE
Organization	not stated in source
Primary source	https://doi.org/10.36922/msam.50
Open access	hybrid
Keywords	data imputation, Ti6Al4V, selective laser melting, machine learning, material database

Importance rating

3 / 10

incremental / niche

Imputation of missing values in one alloy's SLM dataset; supports process-property databases for aerospace/biomedical design but is a narrow methodological advance.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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