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

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/61989100:27240/25:10259326

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

20300 - Mechanical engineering

Projekt

<a href="/cs/project/EH23_021%2F0010117" target="_blank" >EH23_021/0010117: Inovativní a aditivní technologie pro udržitelný energetický průmysl</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Results in Engineering

ISSN

2590-1230

e-ISSN

2590-1230

Svazek periodika

28

Číslo periodika v rámci svazku

DEC

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

15

Strana od-do

nestránkováno

Kód UT WoS článku

001589369900004

EID výsledku v databázi Scopus

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