Additive manufacturing: A machine learning model of process-structure-property linkages for machining behavior of Ti-6Al-4V

Xi Gong, Dongrui Zeng, Willem Groeneveld-Meijer, Guha P. Manogharan

Materials Science in Additive Manufacturing 2022 article cited by 47

Source Open-access PDF More in Engineering & Materials

importance 4/10 ai assisted Additive Manufacturing Materials and Processes additive manufacturing stage: research

Problem

A knowledge gap exists in how additive manufacturing processes and heat treatments link to microstructure and machining behavior of Ti-6Al-4V parts.

Why it matters

Understanding machinability of AM titanium parts is described as crucial for their manufacture and use.

What the AI did

A new machine learning model was developed and validated to characterize material structure and predict machining behavior of Ti-6Al-4V AM samples from SEM/EBSD data.

Results

not stated in source

Impact

Evidence supports a validated ML model addressing the PSP linkage, but no quantitative metrics are given.

Record

Venue	Materials Science in Additive Manufacturing
Published	2022
Work type	article
Authors	Xi Gong, Dongrui Zeng, Willem Groeneveld-Meijer, Guha P. Manogharan
Citations	47 (OpenAlex, at import time)
Model or method	not stated in source
Organization	not stated in source
Primary source	https://doi.org/10.18063/msam.v1i1.6
Open access	hybrid
Keywords	additive manufacturing, Ti-6Al-4V, machine learning, machinability, process-structure-property, microstructure

Importance rating

4 / 10

real but narrower advance

Addresses a knowledge gap in process-structure-property linkage for AM Ti-6Al-4V machining; called crucial but no scale stated.

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

More in Engineering & Materials

#	Paper	Year	Citations	Importance
1	Scaling deep learning for materials discovery Amil Merchant, Simon Batzner, Samuel S. Schoenholz, Muratahan Aykol, G...	2023	1,471	8
2	Application of Machine Learning in Material Synthesis and Property Prediction Guannan Huang, Yani Guo, Ye Chen, Zhengwei Nie	2023	165	7
3	Machine learning-guided discovery of ionic polymer electrolytes for lithium metal batteries Kai Li, Jifeng Wang, Yuanyuan Song, Ying Wang	2023	115	7
4	Machine Learning for Structural Health Monitoring of Aerospace Structures: A Review Gennaro Scarselli, Francesco Nicassio	2025	44	7
5	Power System Transient Stability Assessment Based on Machine Learning Algorithms and Grid Topology Mihail D. Senyuk, Murodbek Safaraliev, Firuz Kamalov, Hana Sulieman	2023	42	7