{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T20:37:01Z","timestamp":1770842221495,"version":"3.50.1"},"reference-count":67,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2014,1,8]],"date-time":"2014-01-08T00:00:00Z","timestamp":1389139200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J Intell Manuf"],"published-print":{"date-parts":[[2016,4]]},"DOI":"10.1007\/s10845-013-0867-2","type":"journal-article","created":{"date-parts":[[2014,1,7]],"date-time":"2014-01-07T09:26:24Z","timestamp":1389086784000},"page":"343-361","source":"Crossref","is-referenced-by-count":16,"title":["Prediction of drill flank wear using ensemble of co-evolutionary particle swarm optimization based-selective neural network ensembles"],"prefix":"10.1007","volume":"27","author":[{"given":"Wen-An","family":"Yang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenhe","family":"Liao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2014,1,8]]},"reference":[{"issue":"7","key":"867_CR1","doi-asserted-by":"crossref","first-page":"707","DOI":"10.1016\/S0890-6955(03)00023-3","volume":"43","author":"I Abu-Mahfouz","year":"2003","unstructured":"Abu-Mahfouz, I. (2003). Drilling wear detection and classification using vibration signals and artificial neural network. International Journal of Machine Tools and Manufacture, 43(7), 707\u2013720.","journal-title":"International Journal of Machine Tools and Manufacture"},{"issue":"3","key":"867_CR2","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1007\/s00521-004-0436-x","volume":"14","author":"I Abu-Mahfouz","year":"2005","unstructured":"Abu-Mahfouz, I. (2005). Drill flank wear estimation using supervised vector quantization neural networks. Neural Computing and Applications, 14(3), 167\u2013175.","journal-title":"Neural Computing and Applications"},{"issue":"2","key":"867_CR3","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1016\/j.ymssp.2008.02.010","volume":"23","author":"C Aliustaoglu","year":"2009","unstructured":"Aliustaoglu, C., Ertunc, H. M., & Ocak, H. (2009). Tool wear condition monitoring using a sensor fusion model based on fuzzy inference system. Mechanical Systems and Signal Processing, 23(2), 539\u2013546.","journal-title":"Mechanical Systems and Signal Processing"},{"issue":"2","key":"867_CR4","first-page":"123","volume":"24","author":"L Breiman","year":"1996","unstructured":"Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123\u2013140.","journal-title":"Machine Learning"},{"issue":"3","key":"867_CR5","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1007\/s10845-010-0436-x","volume":"23","author":"D Brezak","year":"2012","unstructured":"Brezak, D., Majetic, D., Udiljak, T., & Kasac, J. (2012). Tool wear estimation using an analytic fuzzy classifier and support vector machines. Journal of Intelligent Manufacturing, 23(3), 797\u2013809.","journal-title":"Journal of Intelligent Manufacturing"},{"issue":"5","key":"867_CR6","doi-asserted-by":"crossref","first-page":"1893","DOI":"10.1007\/s10845-011-0506-8","volume":"23","author":"A Bustillo","year":"2012","unstructured":"Bustillo, A., & Correa, M. (2012). Using artificial intelligence to predict surface roughness in deep drilling of steel components. Journal of Intelligent Manufacturing, 23(5), 1893\u20131902.","journal-title":"Journal of Intelligent Manufacturing"},{"issue":"3","key":"867_CR7","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1007\/s10845-010-0415-2","volume":"23","author":"U \u00c7ayda\u015f","year":"2012","unstructured":"\u00c7ayda\u015f, U., & Ekici, S. (2012). Support vector machines models for surface roughness prediction in CNC turning of AISI 304 austenitic stainless steel. Journal of Intelligent Manufacturing, 23(3), 639\u2013650.","journal-title":"Journal of Intelligent Manufacturing"},{"issue":"6","key":"867_CR8","doi-asserted-by":"crossref","first-page":"887","DOI":"10.1016\/S0890-6955(99)00089-9","volume":"40","author":"SK Choudhury","year":"2000","unstructured":"Choudhury, S. K., & Raju, G. (2000). Investigation into crater wear in drilling. International Journal of Machine Tools and Manufacture, 40(6), 887\u2013898.","journal-title":"International Journal of Machine Tools and Manufacture"},{"key":"867_CR9","doi-asserted-by":"crossref","unstructured":"Desai, C. K., & Shaikh, D. A. A. (2006). Drill wear monitoring using artificial neural network with differential evolution learning. In: IEEE International Conference on Industrial Technology (pp. 2019\u20132022). Mumbai, India: Renaissance Mumbai Hotel and Convention Centre.","DOI":"10.1109\/ICIT.2006.372500"},{"issue":"9","key":"867_CR10","doi-asserted-by":"crossref","first-page":"1347","DOI":"10.1016\/S0890-6955(00)00111-5","volume":"41","author":"HM Ertunc","year":"2001","unstructured":"Ertunc, H. M., & Loparo, K. A. (2001). A decesion fusion algorithm for tool wear condition monitoring in drilling. International Journal of Machine Tools and Manufacture, 41(9), 1347\u20131362.","journal-title":"International Journal of Machine Tools and Manufacture"},{"issue":"9","key":"867_CR11","doi-asserted-by":"crossref","first-page":"1363","DOI":"10.1016\/S0890-6955(00)00112-7","volume":"41","author":"HM Ertunc","year":"2001","unstructured":"Ertunc, H. M., Loparo, K. A., & Ocak, H. (2001). Tool wear condition monitoring in drilling operations using hidden Markov models (HMMs). International Journal of Machine Tools and Manufacture, 41(9), 1363\u20131384.","journal-title":"International Journal of Machine Tools and Manufacture"},{"issue":"5","key":"867_CR12","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1016\/j.mechatronics.2003.10.005","volume":"14","author":"HM Ertunc","year":"2004","unstructured":"Ertunc, H. M., & Oysu, C. (2004). Drill wear monitoring using cutting force signal. Mechatronics, 14(5), 533\u2013548.","journal-title":"Mechatronics"},{"issue":"3","key":"867_CR13","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1007\/s10845-010-0443-y","volume":"23","author":"A Gajate","year":"2012","unstructured":"Gajate, A., Haber, R., Toro, R., Vega, P., & Bustillo, A. (2012). Tool wear monitoring using neuro-fuzzy techniques: A comparative study in a turning process. Journal of Intelligent Manufacturing, 23(3), 869\u2013882.","journal-title":"Journal of Intelligent Manufacturing"},{"issue":"4\u20135","key":"867_CR14","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1007\/s00521-006-0065-7","volume":"16","author":"S Garg","year":"2007","unstructured":"Garg, S., Pal, S. K., & Chakraborty, D. (2007). Evaluation of the performance of backpropagation and radial basis function neural networks in predicting the drill flank wear. Neural Computing and Applications, 16(4\u20135), 407\u2013417.","journal-title":"Neural Computing and Applications"},{"issue":"8","key":"867_CR15","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1007\/s00500-007-0236-1","volume":"12","author":"S Garg","year":"2008","unstructured":"Garg, S., Patra, K., Pal, S. K., & Chakraborty, D. (2008). Effect of different basis functions on a radial basis function network in prediction of drill flank wear from motor current signals. Soft Computing, 12(8), 777\u2013787.","journal-title":"Soft Computing"},{"issue":"7","key":"867_CR16","doi-asserted-by":"crossref","first-page":"1112","DOI":"10.1016\/j.engappai.2010.02.012","volume":"23","author":"S Garg","year":"2010","unstructured":"Garg, S., Patra, K., Khetrapal, V., Pal, S. K., & Chakraborty, D. (2010). Genetically evolved radial basis function network based prediction of drill flank wear. Engineering Applications of Artificial Intelligence, 23(7), 1112\u20131120.","journal-title":"Engineering Applications of Artificial Intelligence"},{"issue":"5","key":"867_CR17","doi-asserted-by":"crossref","first-page":"1733","DOI":"10.1007\/s10845-010-0478-0","volume":"23","author":"M Grzenda","year":"2012","unstructured":"Grzenda, M., Bustillo, A., & Zawistowski, P. (2012). A soft computing system using intelligent imputation strategies for roughness prediction in deep drilling. Journal of Intelligent Manufacturing, 23(5), 1733\u20131743.","journal-title":"Journal of Intelligent Manufacturing"},{"issue":"10","key":"867_CR18","doi-asserted-by":"crossref","first-page":"993","DOI":"10.1109\/34.58871","volume":"12","author":"LK Hansen","year":"1990","unstructured":"Hansen, L. K., & Salamon, P. (1990). Neural network ensembles. IEEE Transaction on Pattern Analysis and Machine Intelligence, 12(10), 993\u20131001.","journal-title":"IEEE Transaction on Pattern Analysis and Machine Intelligence"},{"key":"867_CR19","doi-asserted-by":"crossref","unstructured":"Hansen, J. V. (2000). Combining predictors: Meta machine learning methods and bias\/variance and ambiguity decompositions. Ph.D. Dissertation of University of Aarhus, Denmark.","DOI":"10.7146\/dpb.v29i550.7203"},{"issue":"2","key":"867_CR20","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/0893-6080(89)90020-8","volume":"2","author":"KM Hornik","year":"1989","unstructured":"Hornik, K. M., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks, 2(2), 359\u2013366.","journal-title":"Neural Networks"},{"issue":"3","key":"867_CR21","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1109\/72.846750","volume":"11","author":"G Huang","year":"2000","unstructured":"Huang, G., Chen, Y., & Babri, H. A. (2000). Classification ability of single hidden layer feed forward neural networks. IEEE Transaction on Neural Networks, 11(3), 799\u2013801.","journal-title":"IEEE Transaction on Neural Networks"},{"key":"867_CR22","unstructured":"Judd, J. S. (1987). Learning in networks is hard. In: Proceedings of the First International Conference on Neural Networks (pp. 685\u2013692). San Diego, California, New York: IEEE."},{"key":"867_CR23","doi-asserted-by":"crossref","unstructured":"Kennedy, J., & Eberhart, R. C. (1995). Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks (pp. 1942\u20131948), November\/December 27, Perth, Australia, IV. Piscataway, NJ: IEEE Service Centre.","DOI":"10.1109\/ICNN.1995.488968"},{"key":"867_CR24","unstructured":"Kennedy, J., & Eberhart, R. C. (1997). A discrete binary version of the particle swarm optimization. In: Proceedings of the IEEE International Conference on Computational Cybernetics and Simulation (pp. 4104\u20134108). Piscataway, NJ: IEEE Press."},{"issue":"19","key":"867_CR25","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/S0924-0136(99)00070-9","volume":"80\u201390","author":"J Kosmol","year":"1999","unstructured":"Kosmol, J., Czech, M., Klarecki, K., & Sliwka, J. (1999). The optimization of drills for machining of austenitic steel. Journal of Materials Processing Technology, 80\u201390(19), 117\u2013122.","journal-title":"Journal of Materials Processing Technology"},{"issue":"4","key":"867_CR26","doi-asserted-by":"crossref","first-page":"755","DOI":"10.1007\/s10845-012-0623-z","volume":"24","author":"P Kovac","year":"2013","unstructured":"Kovac, P., Rodic, D., Pucovsky, V., Savkovic, B., & Gostimirovic, M. (2013). Application of fuzzy logic and regression analysis for modeling surface roughness in face milliing. Journal of Intelligent Manufacturing, 24(4), 755\u2013762.","journal-title":"Journal of Intelligent Manufacturing"},{"issue":"6","key":"867_CR27","doi-asserted-by":"crossref","first-page":"1061","DOI":"10.1177\/0954405411435579","volume":"226","author":"SM Krishnan","year":"2012","unstructured":"Krishnan, S. M., & Irusa, G. R. (2012). Prediction and analysis of multiple quality characteristics in drilling under minimum quantity. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 226(6), 1061\u20131070.","journal-title":"Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture"},{"key":"867_CR28","unstructured":"Krogh, A., & Vedelsby, J. (1995). Neural network ensembles cross validation, and active learning. In: Advances in neural information processing systems (Vol. 7, pp. 231\u2013238). Denver, CO, Cambridge, MA: MIT Press."},{"issue":"1\u20133","key":"867_CR29","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/S0924-0136(97)00263-X","volume":"74","author":"BY Lee","year":"1998","unstructured":"Lee, B. Y., Liu, H. S., & Tarng, Y. S. (1998). Modelling and optimization of drilling process. Journal of Materials Processing Technology, 74(1\u20133), 149\u2013157.","journal-title":"Journal of Materials Processing Technology"},{"issue":"2","key":"867_CR30","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/S0043-1648(99)00130-1","volume":"231","author":"XL Li","year":"1999","unstructured":"Li, X. L., & Tso, S. K. (1999). Drill wear monitoring based on current signals. Wear, 231(2), 172\u2013178.","journal-title":"Wear"},{"issue":"3","key":"867_CR31","first-page":"352","volume":"110","author":"PG Li","year":"1998","unstructured":"Li, P. G., & Wu, S. M. (1998). Monitoring of drill wear states by a Fuzzy pattern recognition technique. Journal of Engineering for Industry, 110(3), 352\u2013359.","journal-title":"Journal of Engineering for Industry"},{"issue":"1\u20132","key":"867_CR32","first-page":"53","volume":"180","author":"SC Lin","year":"1995","unstructured":"Lin, S. C., & Ting, C. J. (1995). Tool wear monitoring in drilling using force signals. International Journal of Machine Tools and Manufacture, 180(1\u20132), 53\u201360.","journal-title":"International Journal of Machine Tools and Manufacture"},{"issue":"4","key":"867_CR33","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1016\/0890-6955(95)00059-3","volume":"36","author":"SC Lin","year":"1996","unstructured":"Lin, S. C., & Ting, C. J. (1996). Drill wear monitoring using neural network. International Journal of Machine Tools and Manufacture, 36(4), 465\u2013475.","journal-title":"International Journal of Machine Tools and Manufacture"},{"issue":"8","key":"867_CR34","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1080\/08839510490496888","volume":"18","author":"S-P Lo","year":"2004","unstructured":"Lo, S.-P., & Lin, Y.-Y. (2004). Application of abductive polynomial network and grey theory to drill flank wear. Applied Artificial Intelligence: An International Journal, 18(8), 713\u2013733.","journal-title":"Applied Artificial Intelligence: An International Journal"},{"issue":"4","key":"867_CR35","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1007\/s10845-009-0310-x","volume":"22","author":"S Pal","year":"2011","unstructured":"Pal, S., Heyns, P. S., Freyer, B. H., Theron, N. J., & Pal, S. K. (2011). Tool wear monitoring and selection of optimum cutting conditions with progressive tool wear effect and input uncertainties. J ournal of Intelligent Manufacturing, 22(4), 491\u2013504.","journal-title":"J ournal of Intelligent Manufacturing"},{"issue":"3\u20134","key":"867_CR36","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1007\/s00170-006-0589-0","volume":"34","author":"SS Panda","year":"2007","unstructured":"Panda, S. S., Chakraborty, D., & Pal, S. K. (2007). Monitoring of drill flank wear using fuzzy back-propagation neural network. The International Journal of Advanced Manufacturing Technology, 34(3\u20134), 227\u2013235.","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"issue":"2","key":"867_CR37","doi-asserted-by":"crossref","first-page":"858","DOI":"10.1016\/j.asoc.2007.07.003","volume":"8","author":"SS Panda","year":"2008","unstructured":"Panda, S. S., Chakraborty, D., & Pal, S. K. (2008a). Flank wear prediction in drilling using back propagation neural network and radial basis function network. Applied Soft Computing, 8(2), 858\u2013871.","journal-title":"Applied Soft Computing"},{"issue":"5\u20136","key":"867_CR38","first-page":"327","volume":"12","author":"SS Panda","year":"2008","unstructured":"Panda, S. S., Chakraborty, D., & Pal, S. K. (2008b). Drill wear prediction using different neural network architectures. International Journal of Knowledge-based and Intelligent Engineering Systems, 12(5\u20136), 327\u2013338.","journal-title":"International Journal of Knowledge-based and Intelligent Engineering Systems"},{"issue":"2","key":"867_CR39","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/j.jmatprotec.2005.10.021","volume":"172","author":"SS Panda","year":"2006","unstructured":"Panda, S. S., Singh, A. K., Chakraborty, D., & Pal, S. K. (2006). Drill wear monitoring using back propagation neural network. Journal of Materials Processing Technology, 172(2), 283\u2013290.","journal-title":"Journal of Materials Processing Technology"},{"issue":"11\u201312","key":"867_CR40","doi-asserted-by":"crossref","first-page":"2826","DOI":"10.1016\/j.wear.2011.05.038","volume":"271","author":"K-H Park","year":"2011","unstructured":"Park, K.-H., Beal, A., Kim, D., Kwon, P., & Lantrip, J. (2011). Tool wear in drilling of composite\/titanium stacks using carbide and polycrystalline diamond tools. Wear, 271(11\u201312), 2826\u20132835.","journal-title":"Wear"},{"issue":"3","key":"867_CR41","doi-asserted-by":"crossref","first-page":"929","DOI":"10.1016\/j.asoc.2006.06.001","volume":"7","author":"K Patra","year":"2007","unstructured":"Patra, K., Pal, S. K., & Bhattacharyya, K. (2007a). Artificial neural network based prediction of drill flank wear from motor current signals. Applied Soft Computing, 7(3), 929\u2013935.","journal-title":"Applied Soft Computing"},{"issue":"3","key":"867_CR42","first-page":"413","volume":"11","author":"K Patra","year":"2007","unstructured":"Patra, K., Pal, S. K., & Bhattacharyya, K. (2007b). Application of wavelet packet analysis in drill wear monitoring. Machining Science and Technology, 11(3), 413\u2013432.","journal-title":"Machining Science and Technology"},{"issue":"2","key":"867_CR43","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1080\/10910344.2010.500954","volume":"14","author":"K Patra","year":"2010","unstructured":"Patra, K., Pal, S. K., & Bhattacharyy, K. (2010). Fuzzy radial basis function (FRBF) network based tool condition monitoring system using vibration signals. Machining Science and Technology: An International Journal, 14(2), 280\u2013300.","journal-title":"Machining Science and Technology: An International Journal"},{"issue":"4","key":"867_CR44","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1007\/s10845-009-0323-5","volume":"22","author":"G Quintana","year":"2011","unstructured":"Quintana, G., Garcia-Romeu, M. L., & Ciurana, J. (2011). Surface roughness monitoring application based on artificial neural networks for ball-end milling operations. Journal of Intelligent Manufacturing, 22(4), 607\u2013617.","journal-title":"Journal of Intelligent Manufacturing"},{"issue":"3\u20134","key":"867_CR45","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1515\/IJMSP.2009.10.3-4.169","volume":"10","author":"N Raghavendra","year":"2009","unstructured":"Raghavendra, N., Koranne, R., Patra, K., & Pal, S. K. (2009). A neuro ant colony optimized model for drill flank wear prediction. International Journal for Manufacturing Science and Production, 10(3\u20134), 169\u2013184.","journal-title":"International Journal for Manufacturing Science and Production"},{"key":"867_CR46","unstructured":"Ridgeway, G., Madigan, D., & Richardson, T. (1999). Boosting methodology for regression problems. In: Proceedings of the 7th International Workshop on Artificial Intelligence and Statistics (pp. 152\u2013161), Fort Lauderdale, FL, San Mateo, CA: Morgan Kaufmann."},{"issue":"3","key":"867_CR47","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1016\/j.jmatprotec.2005.04.072","volume":"170","author":"C Sanjay","year":"2005","unstructured":"Sanjay, C., Neema, M. L., & Chin, C. W. (2005). Modeling of tool wear in drilling by statistical analysis and artificial neural network. Journal of Materials Processing Technology, 170(3), 494\u2013500.","journal-title":"Journal of Materials Processing Technology"},{"issue":"2","key":"867_CR48","first-page":"197","volume":"5","author":"RE Schapire","year":"1990","unstructured":"Schapire, R. E. (1990). The strength of weak learnability. Machine Learning, 5(2), 197\u2013227.","journal-title":"Machine Learning"},{"issue":"5\u20136","key":"867_CR49","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1007\/s00170-004-2376-0","volume":"28","author":"AK Singh","year":"2006","unstructured":"Singh, A. K., Panda, S. S., Pal, S. K., & Chakraborty, D. (2006). Predicting drill wear using an artificial neural network. The International Journal of Advanced Manufacturing Technology, 28(5\u20136), 456\u2013462.","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"issue":"7\u20138","key":"867_CR50","first-page":"661","volume":"27","author":"DK Sonar","year":"2005","unstructured":"Sonar, D. K., Dixit, U. S., & Ojha, D. K. (2005). The application of a radial basis function neural network for predicting the surface roughness in a turning process. The International Journal of Advanced Manufacturing Technology, 27(7\u20138), 661\u2013666.","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"issue":"3","key":"867_CR51","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/S0924-0136(01)01257-2","volume":"123","author":"CC Tsao","year":"2002","unstructured":"Tsao, C. C. (2002). Prediction of flank wear of different coated drills for JIS SUS 304 stainless steel using neural network. Journal of Materials Processing Technology, 123(3), 354\u2013360.","journal-title":"Journal of Materials Processing Technology"},{"issue":"6","key":"867_CR52","doi-asserted-by":"crossref","first-page":"1085","DOI":"10.1007\/s10845-012-0636-7","volume":"24","author":"GF Wang","year":"2013","unstructured":"Wang, G. F., & Cui, Y. H. (2013). On line tool wear monitoring based on auto associative neural network. Journal of Intelligent Manufacturing, 24(6), 1085\u20131094.","journal-title":"Journal of Intelligent Manufacturing"},{"key":"867_CR53","doi-asserted-by":"crossref","unstructured":"Wang, X. Y., Wang, W., Huang, Y., Nguyen, N., & Krishnakumar, K. (2008). Design of neural network-based estimator for tool wear modeling in hard turning. Journal of Intelligent Manufacturing, 19(4), 383\u2013396.","DOI":"10.1007\/s10845-008-0090-8"},{"key":"867_CR54","unstructured":"Weston, J. A. E., Stitson, M. O., Gammerman, A., Vovk, V., & Vapnik, V. (1996). Experiments with support vector machines. London: Technical Report: CSD-TR-96-19, Royal Holloway University of London."},{"issue":"1\u20133","key":"867_CR55","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/S0924-0136(96)02625-8","volume":"63","author":"YS Wong","year":"1997","unstructured":"Wong, Y. S., Nee, A. Y. C., Li, X. Q., & Reisdorf, C. (1997). Tool condition monitoring using laser scatter pattern. Journal of Materials Processing Technology, 63(1\u20133), 205\u2013210.","journal-title":"Journal of Materials Processing Technology"},{"issue":"1\u20134","key":"867_CR56","first-page":"45","volume":"54","author":"W-A Yang","year":"2010","unstructured":"Yang, W.-A., Guo, Y., & Liao, W. H. (2010). Optimization of multi-pass face milling using a fuzzy particle swarm optimization algorithm. The International Journal of Advanced Manufacturing Technology, 54(1\u20134), 45\u201357.","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"issue":"5\u20138","key":"867_CR57","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1007\/s00170-011-3187-8","volume":"56","author":"W-A Yang","year":"2011","unstructured":"Yang, W.-A., Guo, Y., & Liao, W. H. (2011). Multi-objective optimization of multi-pass face milling using particle swarm intelligence. The International Journal of Advanced Manufacturing Technology, 56(5\u20138), 429\u2013443.","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"issue":"3","key":"867_CR58","doi-asserted-by":"crossref","first-page":"75","DOI":"10.5539\/cis.v2n3p75","volume":"2","author":"X Yang","year":"2009","unstructured":"Yang, X., Kumehara, H., & Zhang, W. (2009). Back propagation wavelet neural network based prediction of drill wear from thrust force. Computer and information Science, 2(3), 75\u201386.","journal-title":"Computer and information Science"},{"issue":"2","key":"867_CR59","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.rcim.2007.08.002","volume":"25","author":"AR Yildiz","year":"2009","unstructured":"Yildiz, A. R. (2009a). A novel hybrid immune algorithm for global optimization in design and manufacturing. Robotics and Computer-Integrated Manufacturing, 25(2), 261\u2013270.","journal-title":"Robotics and Computer-Integrated Manufacturing"},{"issue":"4","key":"867_CR60","first-page":"224","volume":"50","author":"AR Yildiz","year":"2009","unstructured":"Yildiz, A. R. (2009b). An effective hybrid immune-hill climbing optimization approach for solving design and manufacturing optimization problems in industry. Journal of Materials Processing Technology, 50(4), 224\u2013228.","journal-title":"Journal of Materials Processing Technology"},{"issue":"25","key":"867_CR61","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.ins.2012.03.005","volume":"210","author":"AR Yildiz","year":"2012","unstructured":"Yildiz, A. R. (2012). A comparative study of population-based optimization algorithms for turning operations. Information Sciences, 210(25), 81\u201388.","journal-title":"Information Sciences"},{"issue":"20","key":"867_CR62","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1016\/j.ins.2012.07.012","volume":"220","author":"AR Yildiz","year":"2013","unstructured":"Yildiz, A. R. (2013a). Optimization of cutting parameters in multi-pass turning using artificial bee colony-based approach. Information Sciences, 220(20), 399\u2013407.","journal-title":"Information Sciences"},{"issue":"3","key":"867_CR63","doi-asserted-by":"crossref","first-page":"1561","DOI":"10.1016\/j.asoc.2011.12.016","volume":"13","author":"AR Yildiz","year":"2013","unstructured":"Yildiz, A. R. (2013b). A new hybrid differential evolution algorithm for the selection of optimal machining parameters in milling operations. Applied Soft Computing, 13(3), 1561\u20131566.","journal-title":"Applied Soft Computing"},{"issue":"3","key":"867_CR64","doi-asserted-by":"crossref","first-page":"1433","DOI":"10.1016\/j.asoc.2012.01.012","volume":"13","author":"AR Yildiz","year":"2013","unstructured":"Yildiz, A. R. (2013c). Hybrid Taguchi-differential evolution algorithm for optimization of multi-pass turning operations. Applied Soft Computing, 13(3), 1433\u20131439.","journal-title":"Applied Soft Computing"},{"issue":"1\u20134","key":"867_CR65","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1007\/s00170-012-4013-7","volume":"64","author":"AR Yildiz","year":"2013","unstructured":"Yildiz, A. R. (2013d). Cuckoo search algorithm for the selection of optimal machining parameters in milling operations. International Journal of Advanced Manufacturing Technology, 64(1\u20134), 55\u201361.","journal-title":"International Journal of Advanced Manufacturing Technology"},{"issue":"1\u20132","key":"867_CR66","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/S0004-3702(02)00190-X","volume":"137","author":"ZH Zhou","year":"2002","unstructured":"Zhou, Z. H., Wu, J. X., & Tang, W. (2002). Ensembling neural networks: Many could be better than all. Artificial Intelligence, 137(1\u20132), 239\u2013263.","journal-title":"Artificial Intelligence"},{"issue":"3","key":"867_CR67","doi-asserted-by":"crossref","first-page":"1504","DOI":"10.1016\/j.jmatprotec.2008.04.002","volume":"209","author":"U Zuperl","year":"2009","unstructured":"Zuperl, U., Cus, F., & Kiker, E. (2009). Adaptive network based inference system for estimation of flank wear in end-milling. Journal of Materials Processing Technology, 209(3), 1504\u20131511.","journal-title":"Journal of Materials Processing Technology"}],"container-title":["Journal of Intelligent Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-013-0867-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10845-013-0867-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-013-0867-2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,8,13]],"date-time":"2020-08-13T12:58:08Z","timestamp":1597323488000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10845-013-0867-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,1,8]]},"references-count":67,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2016,4]]}},"alternative-id":["867"],"URL":"https:\/\/doi.org\/10.1007\/s10845-013-0867-2","relation":{},"ISSN":["0956-5515","1572-8145"],"issn-type":[{"value":"0956-5515","type":"print"},{"value":"1572-8145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014,1,8]]}}}