Please use this identifier to cite or link to this item:
https://essuir.sumdu.edu.ua/handle/123456789/88226
Or use following links to share this resource in social networks:
Tweet
Recommend this item
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. |
Appears in Collections: |
Наукові видання (ТеСЕТ) |
Views

1

1

40

29037

338

169

17134

236394

1
Downloads

1

1

1

1

1

49510

5231

49508

142950
Files
File | Size | Format | Downloads |
---|---|---|---|
Pavlenko_et_al_Using_Regression_Analysis_2022.pdf | 1.35 MB | Adobe PDF | 247204 |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.