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Fuzzy logic-based modelling of yield strength of as-cast A356 alloy
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, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
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: Neural computing & applications (Print), ISSN 0941-0643, E-ISSN 1433-3058Article in journal (Refereed) Epub ahead of print
Abstract [en]

Uncertain and imprecise data are inherent to many domains, e.g. casting lightweight components. Fuzzy logic offers a way to handle such data, which makes it possible to create predictive models even with small and imprecise data sets. Modelling of cast components under fatigue load leads to understanding of material behaviour on component level. Such understanding is important for the design for minimum warranty risk and maximum weight reduction of lightweight cast components. This paper contributes with a fuzzy logic-based approach to model fatigue-related mechanical properties of as-cast components, which has not been fully addressed by the current research. Two fuzzy logic models are constructed to map yield strength to the chemical composition and the rate of solidification of castings for two A356 alloys. Artificial neural networks are created for the same data sets and then compared to the fuzzy logic approach. The comparison shows that although the neural networks yield similar prediction accuracy, they are less suitable for the domain because they are opaque models. The prediction errors exhibited by the fuzzy logic models are 3.53% for the model and 3.19% for the second, which is the same error level as reported in related work. An examination of prediction errors indicated that these are affected by parameters of the membership functions of the fuzzy logic model.

Place, publisher, year, edition, pages
Springer, 2019.
Keywords [en]
Fuzzy logic; Membership functions; Artificial neural networks; Prediction accuracy; Mechanical properties prediction; A356 alloy; Cast components
National Category
Materials Engineering Computer Engineering
Identifiers
URN: urn:nbn:se:hj:diva-42912DOI: 10.1007/s00521-019-04056-5ISI: XYZOAI: oai:DiVA.org:hj-42912DiVA, id: diva2:1287107
Funder
Knowledge Foundation, 20170066Available from: 2019-02-08 Created: 2019-02-08 Last updated: 2019-08-21

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Tarasov, VladimirTan, HeJarfors, Anders E.W.Seifeddine, Salem

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