{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T03:47:22Z","timestamp":1774928842282,"version":"3.50.1"},"reference-count":38,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2019,6,13]],"date-time":"2019-06-13T00:00:00Z","timestamp":1560384000000},"content-version":"vor","delay-in-days":163,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51665005"],"award-info":[{"award-number":["51665005"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61806058"],"award-info":[{"award-number":["61806058"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2019,1]]},"abstract":"<jats:p>It is difficult to accurately predict the response of some stochastic and complicated manufacturing processes. Data\u2010driven learning methods which can mine unseen relationship between influence parameters and outputs are regarded as an effective solution. In this study, support vector machine (SVM) is applied to develop prediction models for machining processes. Kernel function and loss function are Gaussian radial basis function and \u03b5\u2010insensitive loss function, respectively. To improve the prediction accuracy and reduce parameter adjustment time of SVM model, artificial bee colony algorithm (ABC) is employed to optimize internal parameters of SVM model. Further, to evaluate the optimization performance of ABC in parameters determination of SVM, this study compares the prediction performance of SVM models optimized by well\u2010known evolutionary and swarm\u2010based algorithms (differential evolution (DE), genetic algorithm (GA), particle swarm optimization (PSO), and ABC) and analyzes ability of these optimization algorithms from their optimization mechanism and convergence speed based on experimental datasets of turning and milling. Experimental results indicate that the selected four evaluation indicators values that reflect prediction accuracy and adjustment time for ABC\u2010SVM are better than DE\u2010SVM, GA\u2010SVM, and PSO\u2010SVM except three indicator values of DE\u2010SVM for AISI 1045 steel under the case that training set is enough to develop the prediction model. ABC algorithm has less control parameters, faster convergence speed, and stronger searching ability than DE, GA, and PSO algorithms for optimizing the internal parameters of SVM model. These results shed light on choosing a satisfactory optimization algorithm of SVM for manufacturing processes.<\/jats:p>","DOI":"10.1155\/2019\/3094670","type":"journal-article","created":{"date-parts":[[2019,6,13]],"date-time":"2019-06-13T23:31:01Z","timestamp":1560468661000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["An Effective ABC\u2010SVM Approach for Surface Roughness Prediction in Manufacturing Processes"],"prefix":"10.1155","volume":"2019","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5567-8933","authenticated-orcid":false,"given":"Juan","family":"Lu","sequence":"first","affiliation":[]},{"given":"Xiaoping","family":"Liao","sequence":"additional","affiliation":[]},{"given":"Steven","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4196-6552","authenticated-orcid":false,"given":"Haibin","family":"Ouyang","sequence":"additional","affiliation":[]},{"given":"Kai","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Bing","family":"Huang","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2019,6,13]]},"reference":[{"key":"e_1_2_11_1_2","doi-asserted-by":"publisher","DOI":"10.1115\/1.4036350"},{"key":"e_1_2_11_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2008.2005820"},{"key":"e_1_2_11_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.wear.2013.01.048"},{"key":"e_1_2_11_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmachtools.2014.11.002"},{"key":"e_1_2_11_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rcim.2018.03.011"},{"key":"e_1_2_11_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/s0890-6955(03)00059-2"},{"key":"e_1_2_11_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10845-008-0145-x"},{"key":"e_1_2_11_8_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-009-2104-x"},{"key":"e_1_2_11_9_2","doi-asserted-by":"publisher","DOI":"10.1115\/1.4040267"},{"key":"e_1_2_11_10_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-016-8933-5"},{"key":"e_1_2_11_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmatprotec.2004.04.189"},{"key":"e_1_2_11_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2012.12.016"},{"key":"e_1_2_11_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2011.11.011"},{"key":"e_1_2_11_14_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10845-008-0097-1"},{"key":"e_1_2_11_15_2","doi-asserted-by":"crossref","unstructured":"BeatriceB. 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