{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,3,31]],"date-time":"2022-03-31T08:19:24Z","timestamp":1648714764269},"reference-count":0,"publisher":"IOS Press","license":[{"start":{"date-parts":[[2020,11,9]],"date-time":"2020-11-09T00:00:00Z","timestamp":1604880000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,11,9]]},"abstract":"<jats:p>For many industries, an understanding of the fatigue behavior of cast iron is important but this topic is still under extensive research in materials science. This paper offers fuzzy logic as a data-driven approach to address the challenge of predicting casting performance. However, data scarcity is an issue when applying a data-driven approach in this field; the presented study tackled this problem. Four fuzzy logic systems were constructed and compared in the study, two based solely upon experimental data and the others combining the same experimental data with data drawn from relevant literature. The study showed that the latter demonstrated a higher accuracy for the prediction of the ultimate tensile strength for cast iron.<\/jats:p>","DOI":"10.3233\/faia200743","type":"book-chapter","created":{"date-parts":[[2020,11,10]],"date-time":"2020-11-10T19:10:53Z","timestamp":1605035453000},"source":"Crossref","is-referenced-by-count":0,"title":["Data-Driven Modeling of Mechanical Properties of Cast Iron Using Fuzzy Logic"],"prefix":"10.3233","author":[{"given":"He","family":"Tan","sequence":"first","affiliation":[{"name":"Department of Computer Science and Informatics, School of Engineering, J\u00f6nk\u00f6ping University, Sweden"}]},{"given":"Vladimir","family":"Tarasov","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Informatics, School of Engineering, J\u00f6nk\u00f6ping University, Sweden"}]},{"given":"Vasileios","family":"Fourlakidis","sequence":"additional","affiliation":[{"name":"Department of Materials & Manufacturing, School of Engineering, J\u00f6nk\u00f6ping University, Sweden"}]},{"given":"Attila","family":"Dioszegi","sequence":"additional","affiliation":[{"name":"Department of Materials & Manufacturing, School of Engineering, J\u00f6nk\u00f6ping University, Sweden"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Fuzzy Systems and Data Mining VI"],"original-title":[],"link":[{"URL":"http:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA200743","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,11,10]],"date-time":"2020-11-10T19:10:53Z","timestamp":1605035453000},"score":1,"resource":{"primary":{"URL":"http:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA200743"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia200743","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,9]]}}}