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Using Regression Analysis for Automated Material Selection in Smart Manufacturing

Author

Listed:
  • Ivan Pavlenko

    (Department of Computational Mechanics Named after Volodymyr Martsynkovskyy, Sumy State University, 40007 Sumy, Ukraine)

  • Ján Piteľ

    (Department of Industrial Engineering and Informatics, Faculty of Manufacturing Technologies, Technical University of Košice, Bayerova 1, 080 01 Prešov, Slovakia)

  • Vitalii Ivanov

    (Department of Manufacturing Engineering, Machines and Tools, Sumy State University, 40007 Sumy, Ukraine)

  • Kristina Berladir

    (Department of Applied Materials Science and Technology of Constructional Materials, Sumy State University, 2, Rymskogo-Korsakova St., 40007 Sumy, Ukraine)

  • Jana Mižáková

    (Department of Industrial Engineering and Informatics, Faculty of Manufacturing Technologies, Technical University of Košice, Bayerova 1, 080 01 Prešov, Slovakia)

  • Vitalii Kolos

    (Department of Manufacturing Engineering, Machines and Tools, Sumy State University, 40007 Sumy, Ukraine)

  • Justyna Trojanowska

    (Department of Production Engineering, Poznan University of Technology, 5, M. Sklodowskej-Curie Sq., 60-965 Poznan, Poland)

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.

Suggested Citation

  • Ivan Pavlenko & Ján Piteľ & Vitalii Ivanov & Kristina Berladir & Jana Mižáková & Vitalii Kolos & Justyna Trojanowska, 2022. "Using Regression Analysis for Automated Material Selection in Smart Manufacturing," Mathematics, MDPI, vol. 10(11), pages 1-16, May.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:11:p:1888-:d:829344
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    References listed on IDEAS

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    1. Saikat Chatterjee & Shankar Chakraborty, 2022. "A multi-attributive ideal-real comparative analysis-based approach for piston material selection," OPSEARCH, Springer;Operational Research Society of India, vol. 59(1), pages 207-228, March.
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