Machine learning algorithms comparison for manufacturing applications

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dc.contributor.authorAlmanei, Mohammed
dc.contributor.authorOleghe, Omogbai
dc.contributor.authorJagtap, Sandeep
dc.contributor.authorSalonitis, Konstantinos
dc.date.accessioned2021-09-10T10:09:05Z
dc.date.available2021-09-10T10:09:05Z
dc.date.issued2021-09-07
dc.description.abstractWith 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.en_UK
dc.identifier.citationAlmanei M, Oleghe O, Jagtap S, Salonitis K. (2021) Machine learning algorithms comparison for manufacturing applications. In: Advances in Manufacturing Technology XXXIV: Proceedings of the 18th International Conference on Manufacturing Research, incorporating the 35th National Conference on Manufacturing Research, 7-10 September 2021, Derby, IOS Press, pp. 377-382en_UK
dc.identifier.isbn978-1-64368-198-6
dc.identifier.issn2352-751X
dc.identifier.urihttps://doi.org/10.3233/ATDE210065
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/17067
dc.language.isoenen_UK
dc.publisherIOS Pressen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectMachine learningen_UK
dc.subjectlogistic regressionen_UK
dc.subjectsupport vector machineen_UK
dc.subjectneural networksen_UK
dc.titleMachine learning algorithms comparison for manufacturing applicationsen_UK
dc.typeConference paperen_UK

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