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The Sustainable Adoption of Industry 4.0 and Machine Learning in the Automotive Industry / JAMES FLYNN

Swansea University Author: JAMES FLYNN

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DOI (Published version): 10.23889/SUthesis.63690

Abstract

The automotive industry is undergoing a major transformation. New environmental leg­islation, changing consumer requirements, Industry 4.0 technologies, and advancements in battery technologies, have contributed to an industry-wide shift towards electric pow­ertrain. To remain competitive in this ra...

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Published: Swansea, Wales, UK 2023
Institution: Swansea University
Degree level: Doctoral
Degree name: Ph.D
Supervisor: Giannetti, Cinzia.
URI: https://cronfa.swan.ac.uk/Record/cronfa63690
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first_indexed 2023-06-22T13:19:01Z
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To remain competitive in this rapidly changing environment, automotive manu­facturers must ensure high levels of technical and organisational innovation to transition towards digital and data-driven business practices. This research aims to address these growth opportunities and manage ongoing change in three steps. First, the literature on machine learning applications in automotive manu­facturing is critically reviewed and the barriers to developing and implementing machine learning are discussed. Secondly, a structured framework is developed to assess the indus­try 4.0 maturity of automotive manufacturing operations and guide digital transformation at the factory level. In the third and final step of this research, two machine learning projects identified by the assessment are presented in detail. The first case study presents an anomaly detection solution to identify process errors in engine assembly. This research introduces multiple advancements in anomaly detection in manufacturing, including the introduction of the Anomaly No Concern class. The second case study is a greenfield project to explore new digital value chains to add value to EV customers and explore data­as-a-service business models. This case study uses a combination of Google Street View data and GIS data to identify houses suitable for EV charging and represents a major advancement towards fully automated remote surveying of the built environment. To conclude, multiple advancements are presented that contribute to the academic lit­erature. Clear stepwise frameworks support the proposed industrial solutions to develop, implement and replicate these solutions across the business. 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spelling v2 63690 2023-06-22 The Sustainable Adoption of Industry 4.0 and Machine Learning in the Automotive Industry 7e93312c102d0932639828a4af464c13 JAMES FLYNN JAMES FLYNN true false 2023-06-22 The automotive industry is undergoing a major transformation. New environmental leg­islation, changing consumer requirements, Industry 4.0 technologies, and advancements in battery technologies, have contributed to an industry-wide shift towards electric pow­ertrain. To remain competitive in this rapidly changing environment, automotive manu­facturers must ensure high levels of technical and organisational innovation to transition towards digital and data-driven business practices. This research aims to address these growth opportunities and manage ongoing change in three steps. First, the literature on machine learning applications in automotive manu­facturing is critically reviewed and the barriers to developing and implementing machine learning are discussed. Secondly, a structured framework is developed to assess the indus­try 4.0 maturity of automotive manufacturing operations and guide digital transformation at the factory level. In the third and final step of this research, two machine learning projects identified by the assessment are presented in detail. The first case study presents an anomaly detection solution to identify process errors in engine assembly. This research introduces multiple advancements in anomaly detection in manufacturing, including the introduction of the Anomaly No Concern class. The second case study is a greenfield project to explore new digital value chains to add value to EV customers and explore data­as-a-service business models. This case study uses a combination of Google Street View data and GIS data to identify houses suitable for EV charging and represents a major advancement towards fully automated remote surveying of the built environment. To conclude, multiple advancements are presented that contribute to the academic lit­erature. Clear stepwise frameworks support the proposed industrial solutions to develop, implement and replicate these solutions across the business. These contributions have been used to support ongoing digitalisation efforts, implement state-of-the-art anomaly detec­tion solutions, and explore new data-as-a-service business models in one of the world's largest automotive companies. E-Thesis Swansea, Wales, UK Machine Learning, Industry 4.0, Anomaly Detection, Image Classification, Maturity Model, Change Management 12 6 2023 2023-06-12 10.23889/SUthesis.63690 A selection of third party content is redacted or is partially redacted from this thesis due to copyright restrictions. COLLEGE NANME COLLEGE CODE Swansea University Giannetti, Cinzia. Doctoral Ph.D Ford Motor Company, UK Engineering and Physical Sciences Research (EP/S001387/1 and EP/L015099/1), European Social Fund via the Welsh Government (c80816), Ford Motor Company, UK Engineering and Physical Sciences Research (EP/S001387/1 and EP/L015099/1), M2A European Social Fund via the Welsh Government (c80816), 2024-07-11T15:38:22.3503391 2023-06-22T14:13:15.4051886 Faculty of Science and Engineering School of Engineering and Applied Sciences - Materials Science and Engineering JAMES FLYNN 1 63690__28671__1709aec3d5634393b576011ac7ab3e30.pdf 2023_Flynn_J.final.63690.pdf 2023-10-02T10:22:02.9855630 Output 28657785 application/pdf Redacted version - restricted access true 2024-06-12T00:00:00.0000000 Copyright: The Author, James Flynn, 2023. true eng
title The Sustainable Adoption of Industry 4.0 and Machine Learning in the Automotive Industry
spellingShingle The Sustainable Adoption of Industry 4.0 and Machine Learning in the Automotive Industry
JAMES FLYNN
title_short The Sustainable Adoption of Industry 4.0 and Machine Learning in the Automotive Industry
title_full The Sustainable Adoption of Industry 4.0 and Machine Learning in the Automotive Industry
title_fullStr The Sustainable Adoption of Industry 4.0 and Machine Learning in the Automotive Industry
title_full_unstemmed The Sustainable Adoption of Industry 4.0 and Machine Learning in the Automotive Industry
title_sort The Sustainable Adoption of Industry 4.0 and Machine Learning in the Automotive Industry
author_id_str_mv 7e93312c102d0932639828a4af464c13
author_id_fullname_str_mv 7e93312c102d0932639828a4af464c13_***_JAMES FLYNN
author JAMES FLYNN
author2 JAMES FLYNN
format E-Thesis
publishDate 2023
institution Swansea University
doi_str_mv 10.23889/SUthesis.63690
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Materials Science and Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Materials Science and Engineering
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description The automotive industry is undergoing a major transformation. New environmental leg­islation, changing consumer requirements, Industry 4.0 technologies, and advancements in battery technologies, have contributed to an industry-wide shift towards electric pow­ertrain. To remain competitive in this rapidly changing environment, automotive manu­facturers must ensure high levels of technical and organisational innovation to transition towards digital and data-driven business practices. This research aims to address these growth opportunities and manage ongoing change in three steps. First, the literature on machine learning applications in automotive manu­facturing is critically reviewed and the barriers to developing and implementing machine learning are discussed. Secondly, a structured framework is developed to assess the indus­try 4.0 maturity of automotive manufacturing operations and guide digital transformation at the factory level. In the third and final step of this research, two machine learning projects identified by the assessment are presented in detail. The first case study presents an anomaly detection solution to identify process errors in engine assembly. This research introduces multiple advancements in anomaly detection in manufacturing, including the introduction of the Anomaly No Concern class. The second case study is a greenfield project to explore new digital value chains to add value to EV customers and explore data­as-a-service business models. This case study uses a combination of Google Street View data and GIS data to identify houses suitable for EV charging and represents a major advancement towards fully automated remote surveying of the built environment. To conclude, multiple advancements are presented that contribute to the academic lit­erature. Clear stepwise frameworks support the proposed industrial solutions to develop, implement and replicate these solutions across the business. These contributions have been used to support ongoing digitalisation efforts, implement state-of-the-art anomaly detec­tion solutions, and explore new data-as-a-service business models in one of the world's largest automotive companies.
published_date 2023-06-12T15:38:21Z
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score 11.016235

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