{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T18:45:34Z","timestamp":1778265934467,"version":"3.51.4"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2014,3,30]],"date-time":"2014-03-30T00:00:00Z","timestamp":1396137600000},"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":[[2016,6]]},"DOI":"10.1007\/s10845-014-0907-6","type":"journal-article","created":{"date-parts":[[2014,3,29]],"date-time":"2014-03-29T19:47:08Z","timestamp":1396122428000},"page":"689-700","source":"Crossref","is-referenced-by-count":33,"title":["An intelligent neural-fuzzy model for an in-process surface roughness monitoring system in end milling operations"],"prefix":"10.1007","volume":"27","author":[{"given":"PoTsang B.","family":"Huang","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2014,3,30]]},"reference":[{"key":"907_CR1","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1016\/j.apsusc.2013.08.137","volume":"285","author":"H Akhiani","year":"2013","unstructured":"Akhiani, H., & Szpunar, J. A. (2013). Effect of surface roughness on the texture and oxidation behavior of Zircaloy-4 cladding tube. Applied Surface Science, 285, 832\u2013839.","journal-title":"Applied Surface Science"},{"key":"907_CR2","volume-title":"DeGarmo\u2019s Materials and processes in manufacturing","author":"JT Black","year":"2011","unstructured":"Black, J. T., & Kohser, R. A. (2011). DeGarmo\u2019s Materials and processes in manufacturing (11th ed.). NJ: Prentice Hall.","edition":"11"},{"issue":"6","key":"907_CR3","doi-asserted-by":"crossref","first-page":"1021","DOI":"10.1016\/j.ijmachtools.2006.07.004","volume":"47","author":"H Chang","year":"2007","unstructured":"Chang, H., Kim, J., Kim, I., Jang, D. Y., & Han, D. C. (2007). In-process surface roughness prediction using displacement signals from spindle motion. International Journal of Machine Tools and Manufacture, 47(6), 1021\u20131026.","journal-title":"International Journal of Machine Tools and Manufacture"},{"issue":"4","key":"907_CR4","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1080\/095119200407714","volume":"13","author":"JC Chen","year":"2000","unstructured":"Chen, J. C., & Lou, M. S. (2000). Fuzzy-nets based approach to using an accelerometer for an in-process surface roughness prediction system in milling operation. International Journal of Computer Integrated Manufacturing, 13(4), 358\u2013368.","journal-title":"International Journal of Computer Integrated Manufacturing"},{"issue":"6","key":"907_CR5","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1007\/s10845-005-4371-1","volume":"16","author":"KY Chen","year":"2005","unstructured":"Chen, K. Y., Lim, C. P., & Lai, W. K. (2005). Application of a neural fuzzy system with rule extraction to fault detection and diagnosis. Journal of Intelligent Manufacturing, 16(6), 679\u2013691.","journal-title":"Journal of Intelligent Manufacturing"},{"issue":"3","key":"907_CR6","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1016\/0890-6955(95)00057-7","volume":"36","author":"SA Coker","year":"1996","unstructured":"Coker, S. A., & Shin, Y. C. (1996). In-process control of surface roughness due to tool wear using a new ultrasonic system. International Journal of Machine Tools and Manufacture, 36(3), 411\u2013422.","journal-title":"International Journal of Machine Tools and Manufacture"},{"key":"907_CR7","volume-title":"Fundamentals of modern manufacturing","author":"MP Groover","year":"2007","unstructured":"Groover, M. P. (2007). Fundamentals of modern manufacturing (3rd ed.). Asia: Wiley.","edition":"3"},{"issue":"5","key":"907_CR8","doi-asserted-by":"crossref","first-page":"1671","DOI":"10.1016\/j.measurement.2012.12.016","volume":"46","author":"Z Hessainia","year":"2013","unstructured":"Hessainia, Z., Belbah, A., Yallese, M. A., Mabrouki, T., & Rigal, J. F. (2013). On the prediction of surface roughness in the hard turning based on cutting parameters and tool vibrations. Measurement, 46(5), 1671\u20131681.","journal-title":"Measurement"},{"key":"907_CR9","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1007\/s001700300039","volume":"21","author":"B Huang","year":"2003","unstructured":"Huang, B., & Chen, J. C. (2003). An in-process neural network-based surface roughness prediction system using a dynamometer in end milling operations. International Journal of Advanced Manufacturing Technology, 21, 339\u2013347.","journal-title":"International Journal of Advanced Manufacturing Technology"},{"key":"907_CR10","doi-asserted-by":"crossref","first-page":"544","DOI":"10.1016\/j.neucom.2007.07.029","volume":"71","author":"B Huang","year":"2008","unstructured":"Huang, B., Chen, J. C., & Li, Y. (2008). Artificial-neural-networks based surface roughness Pokayoke system for end-milling operations. Journal of Neurocomputing, 71, 544\u2013549.","journal-title":"Journal of Neurocomputing"},{"key":"907_CR11","doi-asserted-by":"crossref","unstructured":"Huang, P.T., & Chen, J.C. (1998). Fuzzy logic based tool breakage system in end milling operation. In Proceedings of the 23rd international conference on computers and industrial engineering (pp. 31\u201334).","DOI":"10.1016\/S0360-8352(98)00014-X"},{"key":"907_CR12","volume-title":"Neuro-fuzzy and soft computing","author":"JS Jang","year":"1997","unstructured":"Jang, J. S., Sun, C. T., & Mizutani, E. (1997). Neuro-fuzzy and soft computing. NJ: Prentice Hall."},{"issue":"15","key":"907_CR13","doi-asserted-by":"crossref","first-page":"1643","DOI":"10.1016\/j.ijmachtools.2004.06.004","volume":"44","author":"Y Jiao","year":"2004","unstructured":"Jiao, Y., Lei, S., Pei, Z. J., & Lee, E. S. (2004). Fuzzy adaptive networks in machining process modeling: Surface roughness prediction for turning operations. International Journal of Machine Tools and Manufacture, 44(15), 1643\u20131651.","journal-title":"International Journal of Machine Tools and Manufacture"},{"issue":"4","key":"907_CR14","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 milling. Journal of Intelligent Manufacturing, 24(4), 755\u2013762.","journal-title":"Journal of Intelligent Manufacturing"},{"issue":"3","key":"907_CR15","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1007\/s10845-010-0418-z","volume":"23","author":"HL Lin","year":"2012","unstructured":"Lin, H. L. (2012). Optimizing the auto-brazing process quality of aluminum pipe and flange via a Taguchi\u2013Neural\u2013Genetic approach. Journal of Intelligent Manufacturing, 23(3), 679\u2013686.","journal-title":"Journal of Intelligent Manufacturing"},{"key":"907_CR16","volume-title":"Computer numerical control: from programming to networking","author":"SC Lin","year":"1994","unstructured":"Lin, S. C. (1994). Computer numerical control: from programming to networking. NY: Delmar."},{"key":"907_CR17","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1115\/1.2901927","volume":"116","author":"SN Melkote","year":"1994","unstructured":"Melkote, S. N., & Thangaraj, A. R. (1994). An enhanced end milling surface texture model including the effects of radial rake and primary relief angles. Journal of Engineering for Industry, 116, 166\u2013 174.","journal-title":"Journal of Engineering for Industry"},{"issue":"9\u201312","key":"907_CR18","doi-asserted-by":"crossref","first-page":"1033","DOI":"10.1007\/s00170-010-3018-3","volume":"54","author":"S Palani","year":"2011","unstructured":"Palani, S., & Natarajan, U. (2011). Prediction of surface roughness in CNC end milling by machine vision system using artificial neural network based on 2D Fourier transform. International Journal of Advanced Manufacturing Technology, 54(9\u201312), 1033\u20131042.","journal-title":"International Journal of Advanced Manufacturing Technology"},{"issue":"9","key":"907_CR19","doi-asserted-by":"crossref","first-page":"7776","DOI":"10.1016\/j.eswa.2012.01.058","volume":"39","author":"FJ Pontes","year":"2012","unstructured":"Pontes, F. J., Paiva, A. P., Balestrassi, P. P., Ferreira, J. R., & Silva, M. B. (2012). Optimization of radial basis function neural network employed for prediction of surface roughness in hard turning process using Taguchi\u2019s orthogonal arrays. Expert Systems with Applications, 39(9), 7776\u20137787.","journal-title":"Expert Systems with Applications"},{"issue":"4","key":"907_CR20","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":"1","key":"907_CR21","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1007\/s10845-005-4826-4","volume":"16","author":"T Radhakrishnan","year":"2005","unstructured":"Radhakrishnan, T., & Nandan, U. (2005). Milling force prediction using regression and neural networks. Journal of Intelligent Manufacturing, 16(1), 93\u2013102.","journal-title":"Journal of Intelligent Manufacturing"},{"issue":"2","key":"907_CR22","first-page":"107","volume":"19","author":"PP Raj","year":"2012","unstructured":"Raj, P. P., Perumal, A. E., & Ramu, P. (2012). Prediction of surface roughness and delamination in end milling of GFRP using mathematical model and ANN. Indian Journal of Engineering and Materials Science, 19(2), 107\u2013120.","journal-title":"Indian Journal of Engineering and Materials Science"},{"key":"907_CR23","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.measurement.2014.01.024","volume":"51","author":"KV Rao","year":"2014","unstructured":"Rao, K. V., Murthy, B. S. N., & Rao, N. M. (2014). Prediction of cutting tool wear, surface roughness and vibration of work piece in boring of AISI 316 steel with Artificial Neural Network. Measurement, 51, 63\u201370.","journal-title":"Measurement"},{"key":"907_CR24","doi-asserted-by":"crossref","unstructured":"Risbood, K. A., Dixit, U. S., & Sahasrabudhe, A. D. (2003). Prediction of surface roughness and dimensional deviation by measuring cutting forces and vibrations in turning. Journal of Materials Processing Technology, 132(1\u20133), 203\u2013214.","DOI":"10.1016\/S0924-0136(02)00920-2"},{"issue":"1\u20132","key":"907_CR25","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/S0924-0136(01)01146-3","volume":"117","author":"KM Tsai","year":"2001","unstructured":"Tsai, K. M., & Wang, P. J. (2001). Comparisons of neural network models on material removal rate in electrical discharge machining. Journal of Materials Processing Technology, 117(1\u20132), 111\u2013124.","journal-title":"Journal of Materials Processing Technology"},{"issue":"1","key":"907_CR26","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.measurement.2012.06.002","volume":"46","author":"V Upadhyay","year":"2013","unstructured":"Upadhyay, V., Jain, P. K., & Mehta, N. K. (2013). In-process prediction of surface roughness in turning of Ti-6Al-4V alloy using cutting parameters and vibration signals. Measurement, 46(1), 154\u2013160.","journal-title":"Measurement"},{"issue":"3","key":"907_CR27","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1007\/s10845-007-0024-x","volume":"18","author":"JZ Zhang","year":"2007","unstructured":"Zhang, J. Z., Chen, J. C., & Kirby, E. D. (2007). The development of an in-process surface roughness adaptive control system in turning operations. Journal of Intelligent Manufacturing, 18(3), 301\u2013311.","journal-title":"Journal of Intelligent Manufacturing"}],"container-title":["Journal of Intelligent Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-014-0907-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10845-014-0907-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-014-0907-6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,9]],"date-time":"2019-08-09T02:06:09Z","timestamp":1565316369000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10845-014-0907-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,3,30]]},"references-count":27,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2016,6]]}},"alternative-id":["907"],"URL":"https:\/\/doi.org\/10.1007\/s10845-014-0907-6","relation":{},"ISSN":["0956-5515","1572-8145"],"issn-type":[{"value":"0956-5515","type":"print"},{"value":"1572-8145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014,3,30]]}}}