Machine learning models based wear performance prediction of AZ31/TiC composites
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F25%3A10260311" target="_blank" >RIV/61989100:27230/25:10260311 - isvavai.cz</a>
Result on the web
<a href="https://www.webofscience.com/wos/woscc/full-record/WOS:001671760600003" target="_blank" >https://www.webofscience.com/wos/woscc/full-record/WOS:001671760600003</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1038/s41598-025-33417-5" target="_blank" >10.1038/s41598-025-33417-5</a>
Alternative languages
Result language
angličtina
Original language name
Machine learning models based wear performance prediction of AZ31/TiC composites
Original language description
This study presents the fabrication of AZ31 magnesium matrix composites reinforced with 5, 10 and 15 vol% TiC particles using the Friction Stir Processing (FSP) technique and evaluates their wear behavior under varying loads (10-50 N) and sliding speeds (75-225 mm/s). The incorporation of TiC significantly enhanced the microstructural and mechanical properties of the composites. In particular, the AZ31/15 vol% TiC composite exhibited a refined grain structure with an average grain size of similar to 8 mu m, compared to similar to 60 mu m in the unreinforced AZ31 alloy. The same composite also demonstrated a substantial increase in hardness from 62 HV (base alloy) to 116 HV, highlighting the effectiveness of TiC reinforcement in improving strength. A key innovation of this work is the application of five machine learning (ML) algorithms, trained on experimental data using input features such as load, sliding speed and reinforcement content, to model and predict wear performance. After rigorous hyperparameter optimization, the Gradient boost algorithm achieved the highest predictive accuracy (R-2 = 0.9987), with errors falling within the range of experimental uncertainty. The study further includes residual analysis and computational efficiency assessment, supporting interpretable and robust AI-driven modeling. This integrated experimental-ML approach establishes a new benchmark for predictive modeling and data-driven material design in magnesium-based metal matrix composites.
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
20301 - Mechanical engineering
Result continuities
Project
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Continuities
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
Scientific Reports
ISSN
2045-2322
e-ISSN
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Volume of the periodical
16
Issue of the periodical within the volume
1
Country of publishing house
GB - UNITED KINGDOM
Number of pages
22
Pages from-to
nestránkováno
UT code for WoS article
001671760600003
EID of the result in the Scopus database
2-s2.0-105028744541