{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T18:45:33Z","timestamp":1778265933386,"version":"3.51.4"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2013,3,12]],"date-time":"2013-03-12T00:00:00Z","timestamp":1363046400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2014,2]]},"DOI":"10.1007\/s13042-013-0155-7","type":"journal-article","created":{"date-parts":[[2013,3,11]],"date-time":"2013-03-11T05:25:32Z","timestamp":1362979532000},"page":"135-150","source":"Crossref","is-referenced-by-count":22,"title":["Surface roughness prediction in end milling process using intelligent systems"],"prefix":"10.1007","volume":"5","author":[{"given":"Abdel Badie","family":"Sharkawy","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mahmoud A.","family":"El-Sharief","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M-Emad S.","family":"Soliman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2013,3,12]]},"reference":[{"key":"155_CR1","doi-asserted-by":"crossref","first-page":"1065","DOI":"10.1016\/j.ijmachtools.2010.08.009","volume":"50","author":"S Agarwal","year":"2010","unstructured":"Agarwal S, Rao PV (2010) Modeling and prediction of surface roughness in ceramic grinding. Int J Mach Tools Manuf 50:1065\u20131076","journal-title":"Int J Mach Tools Manuf"},{"key":"155_CR2","doi-asserted-by":"crossref","first-page":"5826","DOI":"10.1016\/j.eswa.2010.11.041","volume":"38","author":"I Asilturk","year":"2011","unstructured":"Asilturk I, Cunkas M (2011) Modeling and prediction of surface roughness in turning operations using artificial neural network and multiple regression method. Expert Syst Appl 38:5826\u20135832","journal-title":"Expert Syst Appl"},{"key":"155_CR3","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1016\/S0890-6955(03)00059-2","volume":"43","author":"PG Benardos","year":"2003","unstructured":"Benardos PG, Vosniakos G-C (2003) Predicting surface roughness in machining: a review. Intern J Mach Tools Manuf 43:833\u2013844","journal-title":"Intern J Mach Tools Manuf"},{"key":"155_CR4","doi-asserted-by":"crossref","first-page":"1081","DOI":"10.1016\/j.neunet.2007.09.017","volume":"20","author":"SA Billings","year":"2007","unstructured":"Billings SA, Wei H-L, Balikhin MA (2007) Generalized multiscale radial basis function networks. Neural Netw 20:1081\u20131094","journal-title":"Neural Netw"},{"key":"155_CR5","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1016\/j.ijrmhm.2009.11.011","volume":"28","author":"K Bouacha","year":"2010","unstructured":"Bouacha K, Yallese MA, Mabrouki T, Rigal J-F (2010) Statistical analysis of surface roughness and cutting forces using response surface methodology in hard turning of AISI 52100 bearing steel with CBN tool. Int J Refract Metal Hard Mater 28:349\u2013361","journal-title":"Int J Refract Metal Hard Mater"},{"key":"155_CR6","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1016\/S0890-6955(02)00008-1","volume":"42","author":"JF Briceno","year":"2002","unstructured":"Briceno JF, El-Mounayri H, Mukhopadhyay S (2002) Selecting an artificial neural network for efficient modeling and accurate simulation of the milling process. Int J Mach Tools Manuf 42:663\u2013674","journal-title":"Int J Mach Tools Manuf"},{"key":"155_CR7","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1007\/s12289-009-0679-2","volume":"3","author":"SZ Chavoshi","year":"2010","unstructured":"Chavoshi SZ, Tajdari M (2010) Surface roughness modeling in hard turning operation of AISI 4140 using CBN cutting tool. IntJ Mater Form 3:233\u2013239","journal-title":"IntJ Mater Form"},{"issue":"2","key":"155_CR8","doi-asserted-by":"crossref","first-page":"898","DOI":"10.1109\/TSMCB.2003.817107","volume":"34","author":"S Chen","year":"2004","unstructured":"Chen S, Hong X, Harris CJ, Sharkey PM (2004) Sparse modeling using orthogonal forward regression with PRESS statistic and regulation. IEEE Trans Syst Man Cybern Part B (Cybern) 34(2):898\u2013911","journal-title":"IEEE Trans Syst Man Cybern Part B (Cybern)"},{"issue":"7","key":"155_CR9","doi-asserted-by":"crossref","first-page":"895","DOI":"10.1007\/s11269-007-9200-1","volume":"22","author":"CT Cheng","year":"2008","unstructured":"Cheng CT, Wang WC, Xu DM, Chau KW (2008) Optimizing hydropower reservoir operation using hybrid genetic algorithm and chaos. Water Resour Manag 22(7):895\u2013909","journal-title":"Water Resour Manag"},{"key":"155_CR10","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1016\/j.matdes.2005.07.004","volume":"28","author":"O Colak","year":"2007","unstructured":"Colak O, Kurbanoglu C, Kayacan MC (2007) Milling surface roughness prediction using evolutionary programming methods. Mater Des 28:657\u2013666","journal-title":"Mater Des"},{"key":"155_CR11","doi-asserted-by":"crossref","first-page":"1024","DOI":"10.1016\/j.apm.2010.07.048","volume":"35","author":"M Dong","year":"2011","unstructured":"Dong M, Wang N (2011) Adaptive network-based fuzzy inference system with leave-one-out cross-validation approach for prediction of surface roughness. Appl Math Model 35:1024\u20131035","journal-title":"Appl Math Model"},{"key":"155_CR12","doi-asserted-by":"crossref","DOI":"10.1002\/0471427950","volume-title":"Static and Dynamic Neural Networks: From Fundamental to Advanced Theory","author":"MM Gupta","year":"2003","unstructured":"Gupta MM, Jin L, Homma N (2003) Static and Dynamic Neural Networks: From Fundamental to Advanced Theory. Wiley, Hoboken"},{"key":"155_CR13","unstructured":"Haykin S (1999) \u201cNeural Networks: A Comprehensive Foundation,\u201d 2nd edn, Prentice Hall International, Inc., New Jersey"},{"key":"155_CR14","first-page":"3216","volume":"2","author":"W-H Ho","year":"2009","unstructured":"Ho W-H, Tsai J-T, Lin B-T, Chou J-H (2009) \u201cAdaptive network-based fuzzy inference system for prediction of surface roughness in end milling process using hybrid Taguchi-genetic learning algorithm\u201d, Expert Systems with Applications, 36(2). Part 2:3216\u20133222","journal-title":"Part"},{"key":"155_CR15","first-page":"3","volume":"23","author":"J-SR Jang","year":"1993","unstructured":"Jang J-SR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23:3","journal-title":"IEEE Trans Syst Man Cybern"},{"key":"155_CR16","doi-asserted-by":"crossref","unstructured":"Jang J-SR, Sun CT, Mizutani E (1997) \u201cNeuro-fuzzy and soft computing: a computational approach to learning and machine intelligence,\u201d Printice-Hall International, Inc., New Jersey","DOI":"10.1109\/TAC.1997.633847"},{"key":"155_CR17","doi-asserted-by":"crossref","first-page":"3125","DOI":"10.1016\/j.jmatprotec.2008.07.023","volume":"209","author":"D Karayel","year":"2009","unstructured":"Karayel D (2009) Prediction and control of surface roughness in CNC lathe using artificial neural network. J Mat Proc Technol 209:3125\u20133137","journal-title":"J Mat Proc Technol"},{"key":"155_CR18","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1016\/S0924-0136(03)00687-3","volume":"142","author":"S-P Lo","year":"2003","unstructured":"Lo S-P (2003) An adaptive-network based fuzzy inference system for prediction of workpiece surface roughness in end milling. J Mat Proc Technol 142:665\u2013675","journal-title":"J Mat Proc Technol"},{"key":"155_CR19","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1016\/j.jmatprotec.2007.11.270","volume":"205","author":"C Lu","year":"2008","unstructured":"Lu C (2008) Study on prediction of surface quality in machining process. J Mat Proc Technol 205:439\u2013450","journal-title":"J Mat Proc Technol"},{"key":"155_CR20","volume-title":"An introduction to genetic algorithms","author":"M Mitchell","year":"1999","unstructured":"Mitchell M (1999) An introduction to genetic algorithms. MIT Press, Cambridge"},{"key":"155_CR21","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1016\/j.jmatprotec.2004.02.011","volume":"148","author":"AK Nandi","year":"2004","unstructured":"Nandi AK, Pratihar DK (2004) Automatic design of fuzzy logic controller using genetic algorithm to predict power requirement and surface finish in grinding. J Mater Proc Technol 148:288\u2013300","journal-title":"J Mater Proc Technol"},{"key":"155_CR22","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1016\/j.matdes.2005.01.010","volume":"27","author":"H Oktem","year":"2006","unstructured":"Oktem H, Erzurumlu T, Erzincanli F (2006) Prediction of minimum surface roughness in end milling mold parts using neural network and genetic algorithm. Mat Des 27:735\u2013744","journal-title":"Mat Des"},{"key":"155_CR23","doi-asserted-by":"crossref","first-page":"1512","DOI":"10.1016\/j.jmatprotec.2008.04.003","volume":"209","author":"GKM Rao","year":"2009","unstructured":"Rao GKM, Rangajanardhaa G, Rao DH, Rao MS (2009) Development of Hybrid model and optimization of surface roughness in electric discharge machining using artificial neural networks and genetic algorithm. J Mater Process Technol 209:1512\u20131520","journal-title":"J Mater Process Technol"},{"key":"155_CR24","unstructured":"Rashid MFF, Lani MRA (2010) \u201cSurface roughness prediction for CNC milling process using artificial neural network,\u201d In: Proceedings of the World Congress on Engineering 2010, vol III, WCE 2010, London, June 30\u2013July 2, 2010"},{"key":"155_CR25","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1007\/s00170-010-2757-5","volume":"52","author":"MR Razfar","year":"2011","unstructured":"Razfar MR, Zinati RF, Haghshenas M (2011) Optimum surface roughness prediction in face milling by using neural netwok and harmony search algorithm. Int J Adv Manuf Technol 52:487\u2013495","journal-title":"Int J Adv Manuf Technol"},{"key":"155_CR26","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.jmatprotec.2005.12.003","volume":"173","author":"SS Roy","year":"2006","unstructured":"Roy SS (2006) Design of genetic-fuzzy expert system for predicting surface finish in ultra-precision diamond turning of metal matrix composite. J Mater Process Technol 173:337\u2013344","journal-title":"J Mater Process Technol"},{"issue":"7","key":"155_CR27","doi-asserted-by":"crossref","first-page":"1041","DOI":"10.1016\/j.engappai.2010.06.011","volume":"23","author":"AB Sharkawy","year":"2010","unstructured":"Sharkawy AB (2010) Genetic fuzzy self-tuning PID controllers for antilock braking systems. Eng Appl Artif Intell 23(7):1041\u20131052","journal-title":"Eng Appl Artif Intell"},{"key":"155_CR28","doi-asserted-by":"crossref","first-page":"6903","DOI":"10.1016\/j.eswa.2008.08.072","volume":"36","author":"H-L Shieh","year":"2009","unstructured":"Shieh H-L, Yang Y-K, Chang P-L, Jeng J-T (2009) Robust neural-fuzzy method for function approximation. Expert Syst Appl 36:6903\u20136913","journal-title":"Expert Syst Appl"},{"key":"155_CR29","doi-asserted-by":"crossref","first-page":"1115","DOI":"10.1007\/s00170-006-0429-2","volume":"32","author":"D Singh","year":"2007","unstructured":"Singh D, Rao PV (2007) A surface roughness prediction model for hard turning process. Int J Adv Manuf Technol 32:1115\u20131124","journal-title":"Int J Adv Manuf Technol"},{"key":"155_CR30","doi-asserted-by":"crossref","first-page":"782","DOI":"10.1016\/j.ijmecsci.2009.09.003","volume":"51","author":"ES Topla","year":"2009","unstructured":"Topla ES (2009) The role of stepover ratio in prediction of surface roughness in flat end milling. Int J Mech Sci 51:782\u2013789","journal-title":"Int J Mech Sci"},{"key":"155_CR31","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1007\/s10846-010-9427-6","volume":"60","author":"NC Tsourveloudis","year":"2010","unstructured":"Tsourveloudis NC (2010) Predictive modeling of the Ti6A14V alloy surface roughness. J Intell Robot Syst 60:513\u2013530","journal-title":"J Intell Robot Syst"},{"key":"155_CR32","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.ijmachtools.2003.08.011","volume":"44","author":"M Wang","year":"2004","unstructured":"Wang M, Chang H (2004) Experimental study of surface roughness in slot end milling AL2014-T6. Interna J Mach Tools Manuf 44:51\u201357","journal-title":"Interna J Mach Tools Manuf"},{"key":"155_CR33","volume-title":"Advanced methods in neural computing","author":"PD Wasserman","year":"1993","unstructured":"Wasserman PD (1993) Advanced methods in neural computing. Van Nostrand, New York"},{"key":"155_CR34","doi-asserted-by":"crossref","unstructured":"Wu Jun, Wang Shitong, Fu-lai Chung (2011) Positive and negative fuzzy rule system, extreme learning machine and image classification. Intern J Mach Learn Cybern 2(4): 261\u2013271","DOI":"10.1007\/s13042-011-0024-1"},{"key":"155_CR35","doi-asserted-by":"crossref","unstructured":"Wu CL, Chau KW, Li YS (2009) \u201cPredicting monthly stream flow using data-driven models coupled with data-preprocessing techniques,\u201d Water Resour Res 45: W08432, doi: 10.1029\/2007WR006737","DOI":"10.1029\/2007WR006737"},{"key":"155_CR36","doi-asserted-by":"crossref","unstructured":"Yuhua Qian, Jiye Liang and Wei Wei (2012) Consistency-preserving attribute reduction in fuzzy rough set framework. Intern J Mach Learn Cybern doi: 10.1007\/s13042-012-0090-z","DOI":"10.1007\/s13042-012-0090-z"},{"key":"155_CR37","doi-asserted-by":"crossref","first-page":"1755","DOI":"10.1016\/j.eswa.2009.07.033","volume":"37","author":"AM Zain","year":"2010","unstructured":"Zain AM, Haron H, Sharif S (2010) Prediction of surface roughness in the end milling machining using artificial neural network. Expert Syst Appl 37:1755\u20131768","journal-title":"Expert Syst Appl"},{"key":"155_CR38","doi-asserted-by":"crossref","first-page":"1194","DOI":"10.1016\/j.engappai.2009.04.009","volume":"22","author":"M Zanaganeh","year":"2009","unstructured":"Zanaganeh M, Mousavi SJ, Shahidi AFE (2009) A hybrid genetic algorithm-adaptive network-based fuzzy inference system in prediction of wave parameters. Eng Appl Artif Intell 22:1194\u20131202","journal-title":"Eng Appl Artif Intell"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-013-0155-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s13042-013-0155-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-013-0155-7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,7,10]],"date-time":"2019-07-10T19:44:19Z","timestamp":1562787859000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s13042-013-0155-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2013,3,12]]},"references-count":38,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2014,2]]}},"alternative-id":["155"],"URL":"https:\/\/doi.org\/10.1007\/s13042-013-0155-7","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2013,3,12]]}}}