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Title Using Regression Analysis for Automated Material Selection in Smart Manufacturing
Authors Pavlenko, Ivan Volodymyrovych  
Pitel, J.
Ivanov, Vitalii Oleksandrovych  
Berladir, Khrystyna Volodymyrivna  
Mizakova, J.
Kolos, Vitalii Oleksandrovych​  
Trojanowska, J.
ORCID http://orcid.org/0000-0002-6136-1040
http://orcid.org/0000-0003-0595-2660
http://orcid.org/0000-0002-4287-8204
http://orcid.org/0000-0003-3518-7146
Keywords mechanical properties
phase composition
process innovation
predictive maintenance
decision-making approach
industrial growth
Type Article
Date of Issue 2022
URI https://essuir.sumdu.edu.ua/handle/123456789/88226
Publisher MDPI
License Creative Commons Attribution 4.0 International License
Citation Pavlenko, I.; Piteľ, J.; Ivanov, V.; Berladir, K.; Mižáková, J.; Kolos, V.; Trojanowska, J. Using Regression Analysis for Automated Material Selection in Smart Manufacturing. Mathematics 2022, 10, 1888. https://doi.org/10.3390/math10111888
Abstract In intelligent manufacturing, the phase content and physical and mechanical properties of construction materials can vary due to different suppliers of blanks manufacturers. Therefore, evaluating the composition and properties for implementing a decision-making approach in material selection using up-to-date software is a topical problem in smart manufacturing. Therefore, the article aims to develop a comprehensive automated material selection approach. The proposed method is based on the comprehensive use of normalization and probability approaches and the linear regression procedure formulated in a matrix form. As a result of the study, analytical dependencies for automated material selection were developed. Based on the hypotheses about the impact of the phase composition on physical and mechanical properties, the proposed approach was proven qualitatively and quantitively for carbon steels from AISI 1010 to AISI 1060. The achieved results allowed evaluating the phase composition and physical properties for an arbitrary material from a particular group by its mechanical properties. Overall, an automated material selection approach based on decision-making criteria is helpful for mechanical engineering, smart manufacturing, and industrial engineering purposes.
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