PHYSICS-INFORMED MACHINE LEARNING FOR MULTI-OBJECTIVE OPTIMIZATION IN ADDITIVE MANUFACTURING: A DATA-EFFICIENT APPROACH
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F25%3A10259256" target="_blank" >RIV/61989100:27230/25:10259256 - isvavai.cz</a>
Result on the web
<a href="https://www.webofscience.com/wos/woscc/full-record/WOS:001594510900001" target="_blank" >https://www.webofscience.com/wos/woscc/full-record/WOS:001594510900001</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.17973/MMSJ.2025_10_2025094" target="_blank" >10.17973/MMSJ.2025_10_2025094</a>
Alternative languages
Result language
angličtina
Original language name
PHYSICS-INFORMED MACHINE LEARNING FOR MULTI-OBJECTIVE OPTIMIZATION IN ADDITIVE MANUFACTURING: A DATA-EFFICIENT APPROACH
Original language description
Additive manufacturing (AM) quality control relies on empirical approaches due to complex process-property relationships. While machine learning (ML) offers promising solutions, most approaches treat parameters independently without leveraging thermomechanical principles governing the properties of the printed materials. This is essential for understanding the behaviour of fused deposition modelling (FDM) printing. This study investigates whether integrating elementary thermomechanical knowledge into feature engineering improves mechanical property prediction for polylactic acid components under data-constrained conditions. Using 50 experimental samples from controlled printing conditions, three feature engineering strategies were systematically compared: raw process parameters, physics-informed features based on heat transfer and material flow principles, and polynomial interactions across five ML algorithms. Physics-informed features consistently outperformed baseline approaches, with Huber Regressor achieving coefficient of determination equal to 0.817 (51.3% improvement over raw parameters). Feature importance analysis using SHapley Additive exPlanations identified layer height and nozzle temperature as primary predictors, with engineered thermal diffusion and density features contributing significantly to model performance. This study demonstrates the potential of physics-informed feature engineering for improving prediction accuracy in data-constrained AM scenarios, providing methodological insights for thermomechanical integration and actionable guidance for industrial artificial intelligence (AI) implementation.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20300 - Mechanical engineering
Result continuities
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
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
MM Science Journal
ISSN
1803-1269
e-ISSN
1805-0476
Volume of the periodical
2025
Issue of the periodical within the volume
OCT
Country of publishing house
CZ - CZECH REPUBLIC
Number of pages
8
Pages from-to
8630-8637
UT code for WoS article
001594510900001
EID of the result in the Scopus database
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