Machine learning algorithms comparison for manufacturing applications
Almanei, Mohammed ; Oleghe, Omogbai ; Jagtap, Sandeep LU
and Salonitis, Konstantinos
(2021)
18th International Conference on Manufacturing Research, ICMR 2021
In Advances in Transdisciplinary Engineering
15.
p.377-382
- Abstract
With the vast amount of data available, and its increasing complexity in manufacturing processes, traditional statistical approaches have started to fall short. This is where machine learning plays a key role, addressing the challenges by bringing the ability to analyse large and complex datasets from multiple sources, finding non-linear and intricate patterns on data, relationships between several factors and their influence on the manufacturing process outputs. This paper demonstrates the advantages and applications of using supervised machine learning techniques in the manufacturing industry. It focuses on binary classification and compares the performance of three different machine learning algorithms: logistic regression, support... (More)
With the vast amount of data available, and its increasing complexity in manufacturing processes, traditional statistical approaches have started to fall short. This is where machine learning plays a key role, addressing the challenges by bringing the ability to analyse large and complex datasets from multiple sources, finding non-linear and intricate patterns on data, relationships between several factors and their influence on the manufacturing process outputs. This paper demonstrates the advantages and applications of using supervised machine learning techniques in the manufacturing industry. It focuses on binary classification and compares the performance of three different machine learning algorithms: logistic regression, support vector machine, and neural networks. A case study has been conducted on a manufacturing company, using the techniques and algorithms mentioned. The case study focuses on analysing the relationship between different manufacturing process variables and their impact on one key output variable of a product, which in this case is the result of a quality test that measures product performance. The modelling problem has been oriented towards a Boolean goal to predict whether the parts will pass this test.
(Less)
- author
- Almanei, Mohammed
; Oleghe, Omogbai
; Jagtap, Sandeep
LU
and Salonitis, Konstantinos
- publishing date
- 2021-08-21
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Logistic regression, Machine learning, Neural networks, Support vector machine
- host publication
- Advances in Manufacturing Technology XXXIV - Proceedings of the 18th International Conference on Manufacturing Research, ICMR 2021, incorporating the 35th National Conference on Manufacturing Research
- series title
- Advances in Transdisciplinary Engineering
- editor
- Shafik, Mahmoud and Case, Keith
- volume
- 15
- pages
- 6 pages
- publisher
- IOS Press
- conference name
- 18th International Conference on Manufacturing Research, ICMR 2021
- conference location
- Derby, United Kingdom
- conference dates
- 2021-09-07 - 2021-09-10
- external identifiers
-
- scopus:85116378589
- ISBN
- 9781614994398
- DOI
- 10.3233/ATDE210065
- language
- English
- LU publication?
- no
- additional info
- Publisher Copyright: © 2021 The authors and IOS Press.
- id
- 5fc90c6e-ff2f-448d-bd42-b6f2760e83e9
- date added to LUP
- 2023-09-17 18:30:58
- date last changed
- 2024-03-22 00:27:12
@inproceedings{5fc90c6e-ff2f-448d-bd42-b6f2760e83e9,
abstract = {{<p>With the vast amount of data available, and its increasing complexity in manufacturing processes, traditional statistical approaches have started to fall short. This is where machine learning plays a key role, addressing the challenges by bringing the ability to analyse large and complex datasets from multiple sources, finding non-linear and intricate patterns on data, relationships between several factors and their influence on the manufacturing process outputs. This paper demonstrates the advantages and applications of using supervised machine learning techniques in the manufacturing industry. It focuses on binary classification and compares the performance of three different machine learning algorithms: logistic regression, support vector machine, and neural networks. A case study has been conducted on a manufacturing company, using the techniques and algorithms mentioned. The case study focuses on analysing the relationship between different manufacturing process variables and their impact on one key output variable of a product, which in this case is the result of a quality test that measures product performance. The modelling problem has been oriented towards a Boolean goal to predict whether the parts will pass this test.</p>}},
author = {{Almanei, Mohammed and Oleghe, Omogbai and Jagtap, Sandeep and Salonitis, Konstantinos}},
booktitle = {{Advances in Manufacturing Technology XXXIV - Proceedings of the 18th International Conference on Manufacturing Research, ICMR 2021, incorporating the 35th National Conference on Manufacturing Research}},
editor = {{Shafik, Mahmoud and Case, Keith}},
isbn = {{9781614994398}},
keywords = {{Logistic regression; Machine learning; Neural networks; Support vector machine}},
language = {{eng}},
month = {{08}},
pages = {{377--382}},
publisher = {{IOS Press}},
series = {{Advances in Transdisciplinary Engineering}},
title = {{Machine learning algorithms comparison for manufacturing applications}},
url = {{http://dx.doi.org/10.3233/ATDE210065}},
doi = {{10.3233/ATDE210065}},
volume = {{15}},
year = {{2021}},
}