{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T18:38:45Z","timestamp":1778179125225,"version":"3.51.4"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,3,14]],"date-time":"2020-03-14T00:00:00Z","timestamp":1584144000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,3,14]],"date-time":"2020-03-14T00:00:00Z","timestamp":1584144000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["51675312"],"award-info":[{"award-number":["51675312"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["51675313"],"award-info":[{"award-number":["51675313"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Intell Manuf"],"published-print":{"date-parts":[[2021,1]]},"DOI":"10.1007\/s10845-020-01559-0","type":"journal-article","created":{"date-parts":[[2020,3,14]],"date-time":"2020-03-14T04:48:12Z","timestamp":1584161292000},"page":"77-90","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":111,"title":["Estimation of tool wear and optimization of cutting parameters based on novel ANFIS-PSO method toward intelligent machining"],"prefix":"10.1007","volume":"32","author":[{"given":"Longhua","family":"Xu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5400-8580","authenticated-orcid":false,"given":"Chuanzhen","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Chengwu","family":"Li","sequence":"additional","affiliation":[]},{"given":"Jun","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Hanlian","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Xiaodan","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,3,14]]},"reference":[{"key":"1559_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-012-4540-2","author":"M Ayd\u0131n","year":"2013","unstructured":"Ayd\u0131n, M., Karakuzu, C., U\u00e7ar, M., Cengiz, A., & \u00c7avu\u015flu, M. A. (2013). Prediction of surface roughness and cutting zone temperature in dry turning processes of AISI304 stainless steel using ANFIS with PSO learning. International Journal of Advanced Manufacturing Technology. https:\/\/doi.org\/10.1007\/s00170-012-4540-2.","journal-title":"International Journal of Advanced Manufacturing Technology"},{"key":"1559_CR2","doi-asserted-by":"publisher","DOI":"10.1007\/s10845-019-01526-4","author":"WL Cai","year":"2020","unstructured":"Cai, W. L., Zhang, W. J., Hu, X. F., & Liu, Y. C. (2020). A hybrid information model based on long short-term memory network for tool condition monitoring. Journal of Intelligent Manufacturing. https:\/\/doi.org\/10.1007\/s10845-019-01526-4.","journal-title":"Journal of Intelligent Manufacturing"},{"key":"1559_CR3","doi-asserted-by":"publisher","first-page":"697","DOI":"10.1016\/j.asoc.2018.09.019","volume":"73","author":"SN Chegini","year":"2018","unstructured":"Chegini, S. N., Bagheri, A., & Najafi, F. (2018). PSOSCALF: A new hybrid PSO based on Sine Cosine Algorithm and Levy flight for solving optimization problems. Applied Soft Computing, 73, 697\u2013726. https:\/\/doi.org\/10.1016\/j.asoc.2018.09.019.","journal-title":"Applied Soft Computing"},{"key":"1559_CR4","doi-asserted-by":"publisher","first-page":"1024","DOI":"10.1016\/j.apm.2010.07.048","volume":"35","author":"MG Dong","year":"2011","unstructured":"Dong, M. G., & Wang, N. (2011). Adaptive network-based fuzzy inference system with leave-one-out cross-validation approach for prediction of surface roughness. Applied Mathematical Modelling, 35, 1024\u20131035. https:\/\/doi.org\/10.1016\/j.apm.2010.07.048.","journal-title":"Applied Mathematical Modelling"},{"key":"1559_CR5","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1109\/CEC.2000.870279","volume":"1","author":"RC Eberhart","year":"2000","unstructured":"Eberhart, R. C., & Shi, Y. (2000). Comparing inertia weights and constriction factors in particle swarm optimization. Proceedings Congress on in Evolutionary Computation, 1, 84\u201388. https:\/\/doi.org\/10.1109\/CEC.2000.870279.","journal-title":"Proceedings Congress on in Evolutionary Computation"},{"key":"1559_CR6","doi-asserted-by":"publisher","first-page":"466","DOI":"10.1016\/j.ymssp.2005.10.010","volume":"21","author":"N Ghosh","year":"2007","unstructured":"Ghosh, N., Ravi, Y. B., Patra, A., et al. (2007). Estimation of tool wear during CNC milling using neural network-based sensor fusion. Mechanical Systems and Signal Processing, 21, 466\u2013479. https:\/\/doi.org\/10.1016\/j.ymssp.2005.10.010.","journal-title":"Mechanical Systems and Signal Processing"},{"key":"1559_CR7","doi-asserted-by":"publisher","first-page":"4171","DOI":"10.1016\/j.eswa.2011.09.117","volume":"39","author":"SS Gill","year":"2012","unstructured":"Gill, S. S., Singh, R., Singh, J., & Singh, H. (2012). Adaptive neuro-fuzzy inference system modeling of cryogenically treated AISI M2 HSS turning tool for estimation of flank wear. Expert Systems with Applications, 39, 4171\u20134180. https:\/\/doi.org\/10.1016\/j.eswa.2011.09.117.","journal-title":"Expert Systems with Applications"},{"key":"1559_CR8","doi-asserted-by":"publisher","first-page":"889","DOI":"10.1016\/j.asoc.2008.11.005","volume":"9","author":"RE Haber","year":"2009","unstructured":"Haber, R. E., Haber, R. H., Jimenez, A., & Galan, R. (2009). An optimal fuzzy control system in a network environment based on simulated annealing. An application to a drilling process. Applied Soft Computing, 9, 889\u2013895. https:\/\/doi.org\/10.1016\/j.asoc.2008.11.005.","journal-title":"Applied Soft Computing"},{"key":"1559_CR9","doi-asserted-by":"publisher","first-page":"1015","DOI":"10.1007\/s00521-016-2746-1","volume":"30","author":"M Hasanipanah","year":"2018","unstructured":"Hasanipanah, M., Amnieh, H. B., Arab, H., & Zamzam, M. S. (2018). Feasibility of PSO-ANFIS model to estimate rock fragmentation produced by mine blasting. Neural Computing and Applications, 30, 1015\u20131024. https:\/\/doi.org\/10.1007\/s00521-016-2746-1.","journal-title":"Neural Computing and Applications"},{"key":"1559_CR10","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1016\/j.swevo.2017.07.006","volume":"38","author":"TT Hoanga","year":"2018","unstructured":"Hoanga, T. T., Cho, M. Y., Alamb, M. N., & Vu, Q. T. (2018). A novel differential particle swarm optimization for parameter selection of support vector machines for monitoring metal-oxide surge arrester conditions. Swarm and Evolutionary Computation, 38, 120\u2013126. https:\/\/doi.org\/10.1016\/j.swevo.2017.07.006.","journal-title":"Swarm and Evolutionary Computation"},{"key":"1559_CR11","doi-asserted-by":"publisher","first-page":"14148","DOI":"10.1016\/j.eswa.2011.04.225","volume":"38","author":"M Hosoz","year":"2011","unstructured":"Hosoz, M., Ertunc, H. M., & Bulgurcu, H. (2011). An adaptive neuro-fuzzy inference system model for predicting the performance of a refrigeration system with a cooling tower. Expert Systems with Applications, 38, 14148\u201314155. https:\/\/doi.org\/10.1016\/j.eswa.2011.04.225.","journal-title":"Expert Systems with Applications"},{"key":"1559_CR12","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1016\/j.advengsoft.2010.12.002","volume":"42","author":"B Kaya","year":"2011","unstructured":"Kaya, B., Oysu, C., & Ertunc, H. M. (2011). Force-torque based on-line tool wear estimation system for CNC milling of Inconel 718 using neural networks. Advances in Engineering Software, 42, 76\u201384. https:\/\/doi.org\/10.1016\/j.advengsoft.2010.12.002.","journal-title":"Advances in Engineering Software"},{"issue":"1-4","key":"1559_CR13","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1007\/s00170-016-9070-x","volume":"89","author":"DD Kong","year":"2016","unstructured":"Kong, D. D., Chen, Y. J., Li, N., & Tan, S. L. (2016). Tool wear monitoring based on kernel principal component analysis and v-support vector regression. International Journal of Advanced Manufacturing Technology, 89(1-4), 175\u2013190. https:\/\/doi.org\/10.1007\/s00170-016-9070-x.","journal-title":"International Journal of Advanced Manufacturing Technology"},{"key":"1559_CR14","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1016\/j.jclepro.2016.09.125","volume":"141","author":"NT Mathew","year":"2017","unstructured":"Mathew, N. T., & Vijayaraghavan, L. (2017). Environmentally friendly drilling of intermet allictitanium aluminide at different aspect ratio. Journal of Cleaner Production, 141, 439\u2013452. https:\/\/doi.org\/10.1016\/j.jclepro.2016.09.125.","journal-title":"Journal of Cleaner Production"},{"key":"1559_CR15","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1016\/j.applthermaleng.2015.11.081","volume":"96","author":"K Mohammadi","year":"2016","unstructured":"Mohammadi, K., Shamshirband, S., Petkovic, D., Yee, P. L., & Mansor, Z. (2016). Using ANFIS for selection of more relevant parameters to predict dew point temperature. Applied Thermal Engineering, 96, 311\u2013319. https:\/\/doi.org\/10.1016\/j.applthermaleng.2015.11.081.","journal-title":"Applied Thermal Engineering"},{"key":"1559_CR16","doi-asserted-by":"publisher","first-page":"428","DOI":"10.1016\/j.jclepro.2015.04.057","volume":"102","author":"JS Nam","year":"2015","unstructured":"Nam, J. S., Kim, D. H., Chung, H., & Lee, S. W. (2015). Optimization of environmentally benign micro-drilling process with nano fluid minimum quantity lubrication using response surface methodology and genetic algorithm. Journal of Cleaner Production, 102, 428\u2013436. https:\/\/doi.org\/10.1016\/j.jclepro.2015.04.057.","journal-title":"Journal of Cleaner Production"},{"issue":"4\u20135","key":"1559_CR17","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1016\/j.ijmachtools.2004.09.007","volume":"45","author":"T \u00d6zel","year":"2005","unstructured":"\u00d6zel, T., & Karpat, Y. (2005). Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks. International Journal of Machine Tools and Manufacture, 45(4\u20135), 467\u2013479. https:\/\/doi.org\/10.1016\/j.ijmachtools.2004.09.007.","journal-title":"International Journal of Machine Tools and Manufacture"},{"key":"1559_CR18","doi-asserted-by":"publisher","first-page":"1960","DOI":"10.1016\/j.asoc.2012.11.043","volume":"13","author":"M Rizal","year":"2013","unstructured":"Rizal, M., Ghani, J. A., Nuawi, M. Z., & Haron, C. H. C. (2013). Online tool wear prediction system in the turning process using an adaptive neuro-fuzzy inference system. Applied Soft Computing, 13, 1960\u20131968. https:\/\/doi.org\/10.1016\/j.asoc.2012.11.043.","journal-title":"Applied Soft Computing"},{"issue":"9","key":"1559_CR19","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1088\/0960-1317\/15\/9\/S11","volume":"64","author":"SS Roy","year":"2005","unstructured":"Roy, S. S. (2005). Design of adaptive neuro-fuzzy inference system for predicting surface roughness in turning operation. Journal of Scientific & Industrial Research, 64(9), 653\u2013659. https:\/\/doi.org\/10.1088\/0960-1317\/15\/9\/S11.","journal-title":"Journal of Scientific & Industrial Research"},{"issue":"14","key":"1559_CR20","doi-asserted-by":"publisher","first-page":"2140","DOI":"10.1016\/j.ijmachtools.2007.04.013","volume":"47","author":"DR Salgado","year":"2007","unstructured":"Salgado, D. R., & Alonso, F. J. (2007). An approach based on current and sound signals for in-process tool wear monitoring. International Journal of Advanced Manufacturing Technology, 47(14), 2140\u20132152. https:\/\/doi.org\/10.1016\/j.ijmachtools.2007.04.013.","journal-title":"International Journal of Advanced Manufacturing Technology"},{"key":"1559_CR21","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1016\/j.neucom.2015.03.060","volume":"166","author":"A Sarkheyli","year":"2015","unstructured":"Sarkheyli, A., Zain, A. M., & Sharif, S. (2015). Robust optimization of ANFIS based on a new modified GA. Neurocomputing, 166, 357\u2013366. https:\/\/doi.org\/10.1016\/j.neucom.2015.03.060.","journal-title":"Neurocomputing"},{"key":"1559_CR22","doi-asserted-by":"publisher","first-page":"3289","DOI":"10.1016\/j.jclepro.2017.10.303","volume":"172","author":"LB Saw","year":"2018","unstructured":"Saw, L. B., Ho, L. W., Yew, M. C., & Yusof, F. (2018). Sensitivity analysis of drill wear and optimization using Adaptive Neuro fuzzy genetic algorithm technique toward sustainable machining. Journal of Cleaner Production, 172, 3289\u20133298. https:\/\/doi.org\/10.1016\/j.jclepro.2017.10.303.","journal-title":"Journal of Cleaner Production"},{"key":"1559_CR23","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1016\/j.jocs.2012.12.002","volume":"5","author":"H Sharma","year":"2014","unstructured":"Sharma, H., Bansal, J. C., & Arya, K. V. (2014). Self balanced differential evolution. Journal of Computational Science, 5, 312\u2013323. https:\/\/doi.org\/10.1016\/j.jocs.2012.12.002.","journal-title":"Journal of Computational Science"},{"key":"1559_CR24","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1007\/s10845-007-0048-2","volume":"19","author":"VS Sharma","year":"2008","unstructured":"Sharma, V. S., Sharma, S. K., & Sharma, A. K. (2008). Cutting tool wear estimation for turning. Journal of Intelligent Manufacturing, 19, 99\u2013108. https:\/\/doi.org\/10.1007\/s10845-007-0048-2.","journal-title":"Journal of Intelligent Manufacturing"},{"key":"1559_CR25","doi-asserted-by":"crossref","unstructured":"Shoorehdeli, M. A., et al. (2006). A novel training algorithm in ANFIS structure. In Proceedings of the 2006 American control conference (Vol. 6, pp. 5059\u20135064).","DOI":"10.1109\/ACC.2006.1657525"},{"issue":"5","key":"1559_CR26","doi-asserted-by":"publisher","first-page":"901","DOI":"10.1080\/00207540310001626652","volume":"42","author":"J Sun","year":"2007","unstructured":"Sun, J., Hong, G. S., Rahman, M., & Wong, Y. S. (2007). Identification of feature set for effective tool condition monitoring by acoustic emission sensing. International Journal of Production Research, 42(5), 901\u2013918. https:\/\/doi.org\/10.1080\/00207540310001626652.","journal-title":"International Journal of Production Research"},{"issue":"13","key":"1559_CR27","doi-asserted-by":"publisher","first-page":"162","DOI":"10.3969\/j.issn.1001-3881.2014.13.041","volume":"42","author":"H Yuan","year":"2014","unstructured":"Yuan, H., Wang, C. Y., & Zheng, L. J. (2014). Research development of compacted graphite iron machining. Machine Tool & Hydraulics, 42(13), 162\u2013167. https:\/\/doi.org\/10.3969\/j.issn.1001-3881.2014.13.041.","journal-title":"Machine Tool & Hydraulics"},{"key":"1559_CR28","doi-asserted-by":"publisher","unstructured":"Zhang, Z., et al. (2012). Study on the PSA-ANFIS approach for inverse design in slope engineering. In Proceedings of the 9th international conference on fuzzy systems and knowledge discovery (FSKD) (pp. 170\u2013174). https:\/\/doi.org\/10.1109\/FSKD.2012.6234017.","DOI":"10.1109\/FSKD.2012.6234017"},{"key":"1559_CR29","doi-asserted-by":"publisher","first-page":"606","DOI":"10.1016\/j.msea.2018.01.025","volume":"724","author":"CL Zou","year":"2015","unstructured":"Zou, C. L., Pang, J. C., Zhang, M. X., Qiu, Y., Li, S. X., et al. (2015). The high cycle fatigue, deformation and fracture of compacted graphite iron: Influence of temperature. Materials Science and Engineering A, 724, 606\u2013615. https:\/\/doi.org\/10.1016\/j.msea.2018.01.025.","journal-title":"Materials Science and Engineering A"}],"container-title":["Journal of Intelligent Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-020-01559-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10845-020-01559-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-020-01559-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,14]],"date-time":"2021-03-14T00:53:00Z","timestamp":1615683180000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10845-020-01559-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,14]]},"references-count":29,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["1559"],"URL":"https:\/\/doi.org\/10.1007\/s10845-020-01559-0","relation":{},"ISSN":["0956-5515","1572-8145"],"issn-type":[{"value":"0956-5515","type":"print"},{"value":"1572-8145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,14]]},"assertion":[{"value":"12 May 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 March 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 March 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}