Classification Framework for Machine Learning Support in Manufacturing

Baris Ördek; Yuri Borgianni; Eric Coatanea

doi:10.1007/978-3-031-14317-5_6

Classification Framework for Machine Learning Support in Manufacturing

Baris Ördek, Yuri Borgianni, Eric Coatanea

Automation Technology and Mechanical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

5 Citations (Scopus)

Abstract

Advancement in the production industry moves towards automation due to an increase in demand. Artificial intelligence (AI) has been introduced in industrial applications to this scope. With the help of AI, industrial applications become more efficient, accurate, and adaptive. Machine learning (ML) is a branch of AI and a popular tool for the improvement of the industrial operations. Many believe that it is a suitable tool for the evolution of traditional manufacturing systems into Industry 4.0. Top manufacturing companies started to use ML to enhance their applications in production. The main objective of this research is to present a classification framework for the use of ML in design and manufacturing. The proposed framework includes four steps: design, material selection, testing, and decision-making steps. The classification framework methodology is validated with examples from the available literature. The framework highlights the areas most supported by ML in manufacturing and presents their potential integration as an open issue.

Original language	English
Title of host publication	Managing and Implementing the Digital Transformation - Proceedings of the 1st International Symposium on Industrial Engineering and Automation, ISIEA 2022
Editors	Dominik T. Matt, Renato Vidoni, Erwin Rauch, Patrick Dallasega, Dominik T. Matt
Publisher	Springer
Pages	61-73
Number of pages	13
ISBN (Print)	9783031143168
DOIs	https://doi.org/10.1007/978-3-031-14317-5_6
Publication status	Published - 2022
Publication type	A4 Article in conference proceedings
Event	International Symposium on Industrial Engineering and Automation - Bolzano, Italy Duration: 21 Jun 2022 → 22 Jun 2022

Publication series

Name	Lecture Notes in Networks and Systems
Volume	525 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	International Symposium on Industrial Engineering and Automation
Country/Territory	Italy
City	Bolzano
Period	21/06/22 → 22/06/22

Keywords

AI
Classification framework
Design
Machine learning
Manufacturing

Publication forum classification

Publication forum level 1

ASJC Scopus subject areas

Control and Systems Engineering
Signal Processing
Computer Networks and Communications

Access to Document

10.1007/978-3-031-14317-5_6

Cite this

Ördek, B., Borgianni, Y., & Coatanea, E. (2022). Classification Framework for Machine Learning Support in Manufacturing. In D. T. Matt, R. Vidoni, E. Rauch, P. Dallasega, & D. T. Matt (Eds.), Managing and Implementing the Digital Transformation - Proceedings of the 1st International Symposium on Industrial Engineering and Automation, ISIEA 2022 (pp. 61-73). (Lecture Notes in Networks and Systems; Vol. 525 LNNS). Springer. https://doi.org/10.1007/978-3-031-14317-5_6

Ördek, Baris ; Borgianni, Yuri ; Coatanea, Eric. / Classification Framework for Machine Learning Support in Manufacturing. Managing and Implementing the Digital Transformation - Proceedings of the 1st International Symposium on Industrial Engineering and Automation, ISIEA 2022. editor / Dominik T. Matt ; Renato Vidoni ; Erwin Rauch ; Patrick Dallasega ; Dominik T. Matt. Springer, 2022. pp. 61-73 (Lecture Notes in Networks and Systems).

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title = "Classification Framework for Machine Learning Support in Manufacturing",

abstract = "Advancement in the production industry moves towards automation due to an increase in demand. Artificial intelligence (AI) has been introduced in industrial applications to this scope. With the help of AI, industrial applications become more efficient, accurate, and adaptive. Machine learning (ML) is a branch of AI and a popular tool for the improvement of the industrial operations. Many believe that it is a suitable tool for the evolution of traditional manufacturing systems into Industry 4.0. Top manufacturing companies started to use ML to enhance their applications in production. The main objective of this research is to present a classification framework for the use of ML in design and manufacturing. The proposed framework includes four steps: design, material selection, testing, and decision-making steps. The classification framework methodology is validated with examples from the available literature. The framework highlights the areas most supported by ML in manufacturing and presents their potential integration as an open issue.",

keywords = "AI, Classification framework, Design, Machine learning, Manufacturing",

author = "Baris {\"O}rdek and Yuri Borgianni and Eric Coatanea",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; International Symposium on Industrial Engineering and Automation ; Conference date: 21-06-2022 Through 22-06-2022",

year = "2022",

doi = "10.1007/978-3-031-14317-5_6",

language = "English",

isbn = "9783031143168",

series = "Lecture Notes in Networks and Systems",

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Ördek, B, Borgianni, Y & Coatanea, E 2022, Classification Framework for Machine Learning Support in Manufacturing. in DT Matt, R Vidoni, E Rauch, P Dallasega & DT Matt (eds), Managing and Implementing the Digital Transformation - Proceedings of the 1st International Symposium on Industrial Engineering and Automation, ISIEA 2022. Lecture Notes in Networks and Systems, vol. 525 LNNS, Springer, pp. 61-73, International Symposium on Industrial Engineering and Automation, Bolzano, Italy, 21/06/22. https://doi.org/10.1007/978-3-031-14317-5_6

Classification Framework for Machine Learning Support in Manufacturing. / Ördek, Baris; Borgianni, Yuri; Coatanea, Eric.
Managing and Implementing the Digital Transformation - Proceedings of the 1st International Symposium on Industrial Engineering and Automation, ISIEA 2022. ed. / Dominik T. Matt; Renato Vidoni; Erwin Rauch; Patrick Dallasega; Dominik T. Matt. Springer, 2022. p. 61-73 (Lecture Notes in Networks and Systems; Vol. 525 LNNS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Classification Framework for Machine Learning Support in Manufacturing

AU - Ördek, Baris

AU - Borgianni, Yuri

AU - Coatanea, Eric

PY - 2022

Y1 - 2022

N2 - Advancement in the production industry moves towards automation due to an increase in demand. Artificial intelligence (AI) has been introduced in industrial applications to this scope. With the help of AI, industrial applications become more efficient, accurate, and adaptive. Machine learning (ML) is a branch of AI and a popular tool for the improvement of the industrial operations. Many believe that it is a suitable tool for the evolution of traditional manufacturing systems into Industry 4.0. Top manufacturing companies started to use ML to enhance their applications in production. The main objective of this research is to present a classification framework for the use of ML in design and manufacturing. The proposed framework includes four steps: design, material selection, testing, and decision-making steps. The classification framework methodology is validated with examples from the available literature. The framework highlights the areas most supported by ML in manufacturing and presents their potential integration as an open issue.

AB - Advancement in the production industry moves towards automation due to an increase in demand. Artificial intelligence (AI) has been introduced in industrial applications to this scope. With the help of AI, industrial applications become more efficient, accurate, and adaptive. Machine learning (ML) is a branch of AI and a popular tool for the improvement of the industrial operations. Many believe that it is a suitable tool for the evolution of traditional manufacturing systems into Industry 4.0. Top manufacturing companies started to use ML to enhance their applications in production. The main objective of this research is to present a classification framework for the use of ML in design and manufacturing. The proposed framework includes four steps: design, material selection, testing, and decision-making steps. The classification framework methodology is validated with examples from the available literature. The framework highlights the areas most supported by ML in manufacturing and presents their potential integration as an open issue.

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BT - Managing and Implementing the Digital Transformation - Proceedings of the 1st International Symposium on Industrial Engineering and Automation, ISIEA 2022

A2 - Matt, Dominik T.

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A2 - Dallasega, Patrick

A2 - Matt, Dominik T.

PB - Springer

T2 - International Symposium on Industrial Engineering and Automation

Y2 - 21 June 2022 through 22 June 2022

ER -

Ördek B, Borgianni Y, Coatanea E. Classification Framework for Machine Learning Support in Manufacturing. In Matt DT, Vidoni R, Rauch E, Dallasega P, Matt DT, editors, Managing and Implementing the Digital Transformation - Proceedings of the 1st International Symposium on Industrial Engineering and Automation, ISIEA 2022. Springer. 2022. p. 61-73. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-031-14317-5_6

Classification Framework for Machine Learning Support in Manufacturing

Abstract

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Conference

Keywords

Publication forum classification

ASJC Scopus subject areas

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