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Fuzzy Logic Based Modelling of Cast Component Properties
Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).ORCID iD: 0000-0001-6671-6157
Jönköping University, School of Engineering, JTH, Materials and Manufacturing.ORCID iD: 0000-0002-0101-0062
Jönköping University, School of Engineering, JTH, Materials and Manufacturing.ORCID iD: 0000-0001-6481-5530
2019 (English)In: IFAC-PapersOnLine, E-ISSN 2405-8963, Vol. 52, no 13, p. 1132-1137Article in journal (Refereed) Published
Abstract [en]

Digitalization of manufacturing requires building models to represent accumulated data and knowledge on the products and processes. The use of formal knowledge models allows for increase of the automation level leading to more sustainable manufacturing. Casting is important for different industries because it offers a great freedom of designing for weight reduction. This paper presents an approach to modelling of cast component properties that is based on fuzzy logic. The approach includes learning of the fuzzy inference rules from the data. The constructed fuzzy logic models can be used to tune the manufacturing process to produces cast components with desired properties. The evaluation of the results demonstrates that the accuracy of the two created models are 3.58% and 3.15% respectively with the learned fuzzy inference rules being identical to the manually created ones. The presented approach can help to automate the management of cast component manufacturing. 

Place, publisher, year, edition, pages
Elsevier, 2019. Vol. 52, no 13, p. 1132-1137
Keywords [en]
knowledge modelling; fuzzy logic; fuzzy systems; mechanical properties prediction
National Category
Other Computer and Information Science
Identifiers
URN: urn:nbn:se:hj:diva-47362DOI: 10.1016/j.ifacol.2019.11.348ISI: 000504282400193Scopus ID: 2-s2.0-85078888003Local ID: POA JTH 2019OAI: oai:DiVA.org:hj-47362DiVA, id: diva2:1385292
Conference
9th IFAC Conference on Manufacturing Modelling, Management and Control MIM 2019: Berlin, Germany, 28–30 August 2019
Available from: 2020-01-14 Created: 2020-01-14 Last updated: 2020-02-24Bibliographically approved

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Tan, HeTarasov, VladimirJarfors, Anders E.W.Seifeddine, Salem

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