{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T04:20:09Z","timestamp":1783398009943,"version":"3.54.6"},"reference-count":30,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2022,2,26]],"date-time":"2022-02-26T00:00:00Z","timestamp":1645833600000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,2,25]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>In this study, rough elliptic bore journal bearing performance is predicted using an artificial neural network (ANN) technique. The effects of non-circularity and roughness are quantified to elliptic and isotropic in macro and micro scale, respectively. The numerically estimated performance parameters like load, friction, and flow-in at different eccentricities [0.3 (low), 0.5 (medium), and 0.8 (high)], non-circularities [0.5 (low), 1.0 (medium), and 2.0 (high)], and roughness factors [0.1 (low), 0.2 (medium), 0.3 (medium), and 0.4 (high)] are used to train and build the ANN model. The training continued until the maximum mean square error is achieved, and the best-fitting plot is generated. With a confidence level of 99.75% or an R-value of 0.99757, the results predicted are found to be satisfactory.<\/jats:p>","DOI":"10.1093\/jcde\/qwab004","type":"journal-article","created":{"date-parts":[[2021,1,19]],"date-time":"2021-01-19T20:13:21Z","timestamp":1611087201000},"page":"279-295","source":"Crossref","is-referenced-by-count":3,"title":["Application of artificial neural network for lubrication performance evaluation of rough elliptic bore journal bearing"],"prefix":"10.1093","volume":"9","author":[{"given":"Sushanta Kumar","family":"Pradhan","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, Veer Surendra Sai University of Technology, Burla 768018, Odisha, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Prabhudatta","family":"Mishra","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Veer Surendra Sai University of Technology, Burla 768018, Odisha, 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