Citation: Pavlenko, I.; Pitel’, 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 Academic Editors: Idelfonso B. R. Nogueira and Jozef Husar Received: 6 May 2022 Accepted: 27 May 2022 Published: 31 May 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). mathematics Article Using Regression Analysis for Automated Material Selection in Smart Manufacturing Ivan Pavlenko 1 ,Ján Pitel’ 2 , Vitalii Ivanov 3 , Kristina Berladir 4 , Jana Mižáková 2, * , Vitalii Kolos 3 and Justyna Trojanowska 5 1 Department of Computational Mechanics Named after Volodymyr Martsynkovskyy, Sumy State University, 40007 Sumy, Ukraine; i.pavlenko@omdm.sumdu.edu.ua 2 Department of Industrial Engineering and Informatics, Faculty of Manufacturing Technologies, Technical University of Košice, Bayerova 1, 080 01 Prešov, Slovakia; jan.pitel@tuke.sk 3 Department of Manufacturing Engineering, Machines and Tools, Sumy State University, 40007 Sumy, Ukraine; ivanov@tmvi.sumdu.edu.ua (V.I.); v.kolos@tmvi.sumdu.edu.ua (V.K.) 4 Department of Applied Materials Science and Technology of Constructional Materials, Sumy State University, 2, Rymskogo-Korsakova St., 40007 Sumy, Ukraine; kr.berladir@pmtkm.sumdu.edu.ua 5 Department of Production Engineering, Poznan University of Technology, 5, M. Sklodowskej-Curie Sq., 60-965 Poznan, Poland; justyna.trojanowska@put.poznan.pl * Correspondence: jana.mizakova@tuke.sk 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. Keywords: mechanical properties; phase composition; process innovation; predictive maintenance; decision-making approach; industrial growth MSC: 62J05; 90B50; 65F45; 15A24; 08A70 1. Introduction Traditionally, the choice of structural materials for designing machines is primarily determined based on calculations of strength, rigidity, stability, fatigue, and other static and dynamic loads [1]. However, in today’s globalized market, the same structural materials may vary in their phase composition and physical and mechanical properties depend- ing on the supplier country [2]. Therefore, the problem of determining the impact of the material’s phase composition on its physical and mechanical properties (the direct problem), and vice versa (the inverse problem), is urgent in mechanical engineering. Its solution requires a comprehensive analysis of databases for various materials, composi- tions, and properties, including up-to-date computational means according to intelligent manufacturing tendencies. Mathematics 2022, 10, 1888. https://doi.org/10.3390/math10111888 https://www.mdpi.com/journal/mathematics