Machine Learning application in predicting the properties of Ti-6Al-4V samples produced with Power Bed Fusion technology

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F25%3A10259326" target="_blank" >RIV/61989100:27230/25:10259326 - isvavai.cz</a>
Alternative codes found
RIV/61989100:27240/25:10259326
Result on the web
<a href="https://www.webofscience.com/wos/woscc/full-record/WOS:001589369900004" target="_blank" >https://www.webofscience.com/wos/woscc/full-record/WOS:001589369900004</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.rineng.2025.107404" target="_blank" >10.1016/j.rineng.2025.107404</a>

Result language
angličtina
Original language name
Machine Learning application in predicting the properties of Ti-6Al-4V samples produced with Power Bed Fusion technology
Original language description
In the context of Additive Manufacturing (AM) with high-cost materials such as Ti-6Al-4V (Ti64), minimizing physical experimentation through predictive models offers a strategic advantage in reducing lead time and manufacturing costs. In view of this, the present study attempts to build a systematic framework to establish such a predictive compacity. In particular, eight supervised Machine Learning (ML) models are compared based on their performance in predicting critical physical properties of AM components, i.e., 3D surface roughness, relative density, and hardness. The curated dataset comprises both traditional dimensional printing parameters and dimensionless parameters. Specifically, in addition to the conventional Volumetric Energy Density (VED) and four primary printing parameters (laser power, hatching distance, scanning speed, and layer height), the study discusses two dimensionless numbers Pi 1 and Pi 2 for property prediction. This work underscores the role of selecting predictors and models in advancing data-driven process optimization, offering a scalable approach for reducing experimental overhead in metal AM.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
20300 - Mechanical engineering

Project
<a href="/en/project/EH23_021%2F0010117" target="_blank" >EH23_021/0010117: Innovative and additive technologies for sustainable energy industry</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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