Prediction of machine learning-based hardness for the polycarbonate using additive manufacturing
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F24%3A10255509" target="_blank" >RIV/61989100:27230/24:10255509 - isvavai.cz</a>
Result on the web
<a href="https://www.webofscience.com/wos/woscc/full-record/WOS:001309862800001" target="_blank" >https://www.webofscience.com/wos/woscc/full-record/WOS:001309862800001</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3389/fmats.2024.1410277" target="_blank" >10.3389/fmats.2024.1410277</a>
Alternative languages
Result language
angličtina
Original language name
Prediction of machine learning-based hardness for the polycarbonate using additive manufacturing
Original language description
Introduction: Additive manufacturing (AM) is a revolutionary technology transforming traditional production processes by providing exceptional mechanical characteristics. Methods: The present study aims explicitly to predict the hardness of Polycarbonate (PC) parts produced using AM. The objectives of this study are: (1) To investigate the process parameters that impact the ability to estimate the hardness of PC materials accurately, and (2) To develop a best-performing ML model from a range of models that can reliably predict the hardness of additively manufactured PC parts. Initially, fused filament fabrication (FFF), the most affordable AM technique, was used for the manufacturing of parts. Four process parameters, infill density, print direction, raster angle, and layer thickness, are selected for investigation. A heatmap is generated to obtain the influence of process parameters on hardness. Then, machine learning (ML) techniques create a range of predictive models that can predict hardness value considering the level of process parameters. Results: The developed ML models include Linear Regression, Decision Tree, Random Forest, K-nearest neighbor, Support Vector Regression, AdaBoost, and Artificial Neural Network. Further, an investigation has been done that includes choosing and improving ML algorithms and assessing the models' performance. Discussion: Prediction plots, residual plots, and evaluation metrics plots are prepared to gauge the performance of the developed models. Thus, the research enhances AM capabilities by applying predictive modeling to process parameters and improving the quality and reliability of fabricated components.
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
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Frontiers in Materials
ISSN
2296-8016
e-ISSN
2296-8016
Volume of the periodical
11
Issue of the periodical within the volume
11
Country of publishing house
CH - SWITZERLAND
Number of pages
14
Pages from-to
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UT code for WoS article
001309862800001
EID of the result in the Scopus database
2-s2.0-85203831034