Additive manufacturing: Application and validation of machine learning-based process-structure-property linkages in Ti-6Al-4V

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

Materials Science in Additive Manufacturing 2023 article cited by 14

importance 5/10 partially solved Additive Manufacturing Materials and Processes additive manufacturing stage: research

Predicting how additive manufacturing process conditions and heat treatments affect the microstructure, properties and machining behavior of Ti-6Al-4V.

Reliable process-structure-property linkages let manufacturers choose AM conditions that guarantee material performance.

A machine learning workflow combining statistical microstructure descriptors, a cutting force model and data mining was built and validated to create process-structure-property linkages for metal AM.

Method claimed proven valid for PSP linkages; prediction accuracy found highly correlated with AM process, build orientation and machining conditions (no numbers in abstract).

The abstract claims a validated ML workflow, but no quantitative accuracy figures are given.

Venue Materials Science in Additive Manufacturing
Published 2023
Work type article
Authors Xi Gong, Willem Groeneveld-Meijer, Guha P. Manogharan
Citations 14 (OpenAlex, at import time)
Model or method not stated in source
Organization not stated in source
Primary source https://doi.org/10.36922/msam.0999
Open access hybrid
Keywords additive manufacturing, Ti-6Al-4V, machine learning, process-structure-property, powder bed fusion, directed energy deposition
5 / 10

real but narrower advance

Process-structure-property linkages for metal AM of Ti-6Al-4V matter to manufacturers, but abstract gives no scale of impact; a real but narrow materials advance.

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

# 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