{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T06:30:20Z","timestamp":1783751420655,"version":"3.55.0"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,1,27]],"date-time":"2022-01-27T00:00:00Z","timestamp":1643241600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,27]],"date-time":"2022-01-27T00:00:00Z","timestamp":1643241600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100012165","name":"Key Technologies Research and Development Program","doi-asserted-by":"publisher","award":["2018YFB1701204"],"award-info":[{"award-number":["2018YFB1701204"]}],"id":[{"id":"10.13039\/501100012165","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Intell Manuf"],"published-print":{"date-parts":[[2023,4]]},"DOI":"10.1007\/s10845-021-01894-w","type":"journal-article","created":{"date-parts":[[2022,1,27]],"date-time":"2022-01-27T00:03:17Z","timestamp":1643241797000},"page":"2013-2026","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":98,"title":["Robust modeling method for thermal error of CNC machine tools based on random forest algorithm"],"prefix":"10.1007","volume":"34","author":[{"given":"Mengrui","family":"Zhu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yun","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaobing","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8010-7996","authenticated-orcid":false,"given":"Zhengchun","family":"Du","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianguo","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,1,27]]},"reference":[{"issue":"7","key":"1894_CR1","doi-asserted-by":"publisher","first-page":"1837","DOI":"10.1016\/j.apm.2014.10.016","volume":"39","author":"AM Abdulshahed","year":"2015","unstructured":"Abdulshahed, A. M., Longstaff, A. P., Fletcher, S., & Myers, A. (2015). Thermal error modelling of machine tools based on ANFIS with fuzzy c-means clustering using a thermal imaging camera. Applied Mathematical Modelling, 39(7), 1837\u20131852. https:\/\/doi.org\/10.1016\/j.apm.2014.10.016","journal-title":"Applied Mathematical Modelling"},{"key":"1894_CR2","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1016\/j.jmsy.2016.08.006","volume":"41","author":"AM Abdulshahed","year":"2016","unstructured":"Abdulshahed, A. M., Longstaff, A. P., Fletcher, S., & Potdar, A. (2016). Thermal error modelling of a gantry-type 5-axis machine tool using a Grey Neural Network Model. Journal of Manufacturing Systems, 41, 130\u2013142. https:\/\/doi.org\/10.1016\/j.jmsy.2016.08.006","journal-title":"Journal of Manufacturing Systems"},{"key":"1894_CR3","doi-asserted-by":"publisher","unstructured":"Bardak, S., Bardak, T., Peker, H., S\u00f6zen, E., & \u00c7abuk, Y. (2021). Predicting effects of selected impregnation processes on the observed bending strength of wood, with use of data mining models. BioResources, 16(3), 4891\u20134904. https:\/\/doi.org\/10.15376\/biores.16.3.4891-4904","DOI":"10.15376\/biores.16.3.4891-4904"},{"key":"1894_CR4","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.ijadhadh.2016.02.010","volume":"68","author":"S Bardak","year":"2016","unstructured":"Bardak, S., Tiryaki, S., Nemli, G., & Ayd\u0131n, A. (2016). Investigation and neural network prediction of wood bonding quality based on pressing conditions. International Journal of Adhesion and Adhesives, 68, 115\u2013123. https:\/\/doi.org\/10.1016\/j.ijadhadh.2016.02.010","journal-title":"International Journal of Adhesion and Adhesives"},{"issue":"1","key":"1894_CR5","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5\u201332. https:\/\/doi.org\/10.1023\/A:1010933404324","journal-title":"Machine Learning"},{"key":"1894_CR6","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/j.cirpj.2018.04.003","volume":"22","author":"M Fujishima","year":"2018","unstructured":"Fujishima, M., Narimatsu, K., Irino, N., & Ido, Y. (2018). Thermal displacement reduction and compensation of a turning center. CIRP Journal of Manufacturing Science and Technology, 22, 111\u2013115. https:\/\/doi.org\/10.1016\/j.cirpj.2018.04.003","journal-title":"CIRP Journal of Manufacturing Science and Technology"},{"issue":"1","key":"1894_CR7","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","volume":"63","author":"P Geurts","year":"2006","unstructured":"Geurts, P., Ernst, D., & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3\u201342. https:\/\/doi.org\/10.1007\/s10994-006-6226-1","journal-title":"Machine Learning"},{"key":"1894_CR8","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.ijmachtools.2018.04.004","volume":"132","author":"SN Grama","year":"2018","unstructured":"Grama, S. N., Mathur, A., & Badhe, A. N. (2018). A model-based cooling strategy for motorized spindle to reduce thermal errors. International Journal of Machine Tools and Manufacture, 132, 3\u201316. https:\/\/doi.org\/10.1016\/j.ijmachtools.2018.04.004","journal-title":"International Journal of Machine Tools and Manufacture"},{"issue":"1\u20132","key":"1894_CR9","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1016\/j.ymssp.2014.04.012","volume":"49","author":"V Hassani","year":"2014","unstructured":"Hassani, V., Tjahjowidodo, T., & Do, T. N. (2014). A survey on hysteresis modeling, identification and control. Mechanical Systems and Signal Processing, 49(1\u20132), 209\u2013233. https:\/\/doi.org\/10.1016\/j.ymssp.2014.04.012","journal-title":"Mechanical Systems and Signal Processing"},{"issue":"1","key":"1894_CR10","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1007\/s10845-012-0675-0","volume":"25","author":"D Katherasan","year":"2014","unstructured":"Katherasan, D., Elias, J. V., Sathiya, P., & Haq, A. N. (2014). Simulation and parameter optimization of flux cored arc welding using artificial neural network and particle swarm optimization algorithm. Journal of Intelligent Manufacturing, 25(1), 67\u201376. https:\/\/doi.org\/10.1007\/s10845-012-0675-0","journal-title":"Journal of Intelligent Manufacturing"},{"issue":"4","key":"1894_CR11","doi-asserted-by":"publisher","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. https:\/\/doi.org\/10.1007\/s10845-012-0623-z","journal-title":"Journal of Intelligent Manufacturing"},{"issue":"7\u20138","key":"1894_CR12","doi-asserted-by":"publisher","first-page":"745","DOI":"10.1007\/s00170-006-0751-8","volume":"35","author":"YX Li","year":"2006","unstructured":"Li, Y. X., Yang, J. G., Gelvis, T., & Li, Y. Y. (2006). Optimization of measuring points for machine tool thermal error based on grey system theory. The International Journal of Advanced Manufacturing Technology, 35(7\u20138), 745\u2013750. https:\/\/doi.org\/10.1007\/s00170-006-0751-8","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"key":"1894_CR13","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.ijmachtools.2015.04.008","volume":"95","author":"Y Li","year":"2015","unstructured":"Li, Y., Zhao, W. H., Lan, S. H., Ni, J., Wu, W. W., & Lu, B. H. (2015). A review on spindle thermal error compensation in machine tools. International Journal of Machine Tools and Manufacture, 95, 20\u201338. https:\/\/doi.org\/10.1016\/j.ijmachtools.2015.04.008","journal-title":"International Journal of Machine Tools and Manufacture"},{"key":"1894_CR14","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.ijmachtools.2016.11.001","volume":"113","author":"H Liu","year":"2017","unstructured":"Liu, H., Miao, E. M., Wei, X. Y., & Zhuang, X. D. (2017). Robust modeling method for thermal error of CNC machine tools based on ridge regression algorithm. International Journal of Machine Tools and Manufacture, 113, 35\u201348. https:\/\/doi.org\/10.1016\/j.ijmachtools.2016.11.001","journal-title":"International Journal of Machine Tools and Manufacture"},{"key":"1894_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2019.106538","volume":"138","author":"JL Liu","year":"2020","unstructured":"Liu, J. L., Ma, C., & Wang, S. L. (2020). Data-driven thermally-induced error compensation method of high-speed and precision five-axis machine tools. Mechanical Systems and Signal Processing, 138, 106538. https:\/\/doi.org\/10.1016\/j.ymssp.2019.106538","journal-title":"Mechanical Systems and Signal Processing"},{"key":"1894_CR16","doi-asserted-by":"publisher","first-page":"471","DOI":"10.1016\/j.ymssp.2018.11.042","volume":"121","author":"JL Liu","year":"2019","unstructured":"Liu, J. L., Ma, C., Wang, S. L., Wang, S. B., Yang, B., & Shi, H. (2019). Thermal boundary condition optimization of ball screw feed drive system based on response surface analysis. Mechanical Systems and Signal Processing, 121, 471\u2013495. https:\/\/doi.org\/10.1016\/j.ymssp.2018.11.042","journal-title":"Mechanical Systems and Signal Processing"},{"key":"1894_CR17","doi-asserted-by":"publisher","first-page":"107020","DOI":"10.1016\/j.ymssp.2020.107020","volume":"146","author":"K Liu","year":"2021","unstructured":"Liu, K., Wu, J., Liu, H., Sun, M., & Wang, Y. (2021). Reliability analysis of thermal error model based on DBN and Monte Carlo method. Mechanical Systems and Signal Processing, 146, 107020. https:\/\/doi.org\/10.1016\/j.ymssp.2020.107020","journal-title":"Mechanical Systems and Signal Processing"},{"issue":"9","key":"1894_CR18","doi-asserted-by":"publisher","first-page":"1383","DOI":"10.1016\/S0890-6955(99)00009-7","volume":"39","author":"CH Lo","year":"1999","unstructured":"Lo, C. H., Yuan, J. X., & Ni, J. (1999). Optimal temperature variable selection by grouping approach for thermal error modeling and compensation. International Journal of Machine Tools &amp; Manufacture, 39(9), 1383\u20131396. https:\/\/doi.org\/10.1016\/S0890-6955(99)00009-7","journal-title":"International Journal of Machine Tools & Manufacture"},{"issue":"2","key":"1894_CR19","doi-asserted-by":"publisher","first-page":"771","DOI":"10.1016\/j.cirp.2012.05.008","volume":"61","author":"J Mayr","year":"2012","unstructured":"Mayr, J., Jedrzejewski, J., Uhlmann, E., Alkan Donmez, M., Knapp, W., H\u00e4rtig, F., Wendt, K., Moriwaki, T., Shore, P., Schmitt, R., Brecher, C., W\u00fcrz, T., & Wegener, K. (2012). Thermal issues in machine tools. CIRP Annals, 61(2), 771\u2013791. https:\/\/doi.org\/10.1016\/j.cirp.2012.05.008","journal-title":"CIRP Annals"},{"issue":"9\u201312","key":"1894_CR20","doi-asserted-by":"publisher","first-page":"2593","DOI":"10.1007\/s00170-013-5229-x","volume":"69","author":"E Miao","year":"2013","unstructured":"Miao, E., Gong, Y., Niu, P., Ji, C., & Chen, H. (2013). Robustness of thermal error compensation modeling models of CNC machine tools. The International Journal of Advanced Manufacturing Technology, 69(9\u201312), 2593\u20132603. https:\/\/doi.org\/10.1007\/s00170-013-5229-x","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"key":"1894_CR21","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.ijmachtools.2015.07.004","volume":"97","author":"E Miao","year":"2015","unstructured":"Miao, E., Liu, Y., Liu, H., Gao, Z., & Li, W. (2015). Study on the effects of changes in temperature-sensitive points on thermal error compensation model for CNC machine tool. International Journal of Machine Tools and Manufacture, 97, 50\u201359. https:\/\/doi.org\/10.1016\/j.ijmachtools.2015.07.004","journal-title":"International Journal of Machine Tools and Manufacture"},{"issue":"5","key":"1894_CR22","doi-asserted-by":"publisher","first-page":"1037","DOI":"10.1007\/s10845-014-0933-4","volume":"27","author":"A Mosallam","year":"2016","unstructured":"Mosallam, A., Medjaher, K., & Zerhouni, N. (2016). Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction. Journal of Intelligent Manufacturing, 27(5), 1037\u20131048. https:\/\/doi.org\/10.1007\/s10845-014-0933-4","journal-title":"Journal of Intelligent Manufacturing"},{"key":"1894_CR23","doi-asserted-by":"publisher","DOI":"10.1007\/s10845-021-01771-6","author":"IK Nti","year":"2021","unstructured":"Nti, I. K., Adekoya, A. F., Weyori, B. A., & Nyarko-Boateng, O. (2021). Applications of artificial intelligence in engineering and manufacturing: A systematic review. Journal of Intelligent Manufacturing. https:\/\/doi.org\/10.1007\/s10845-021-01771-6","journal-title":"Journal of Intelligent Manufacturing"},{"issue":"2","key":"1894_CR24","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1007\/s001700200132","volume":"20","author":"R Ramesh","year":"2002","unstructured":"Ramesh, R., Mannan, M. A., & Poo, A. N. (2002). Support vector machines model for classification of thermal error in machine tools. International Journal of Advanced Manufacturing Technology, 20(2), 114\u2013120. https:\/\/doi.org\/10.1007\/s001700200132","journal-title":"International Journal of Advanced Manufacturing Technology"},{"issue":"7","key":"1894_CR25","doi-asserted-by":"publisher","first-page":"1939","DOI":"10.1007\/s10845-020-01733-4","volume":"32","author":"A \u015awi\u0107","year":"2021","unstructured":"\u015awi\u0107, A., Gola, A., Sobaszek, \u0141, & \u0160midov\u00e1, N. (2021). A thermo-mechanical machining method for improving the accuracy and stability of the geometric shape of long low-rigidity shafts. Journal of Intelligent Manufacturing, 32(7), 1939\u20131951. https:\/\/doi.org\/10.1007\/s10845-020-01733-4","journal-title":"Journal of Intelligent Manufacturing"},{"key":"1894_CR26","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.ijmachtools.2014.03.002","volume":"82\u201383","author":"B Tan","year":"2014","unstructured":"Tan, B., Mao, X., Liu, H., Li, B., He, S., Peng, F., & Yin, L. (2014). A thermal error model for large machine tools that considers environmental thermal hysteresis effects. International Journal of Machine Tools and Manufacture, 82\u201383, 11\u201320. https:\/\/doi.org\/10.1016\/j.ijmachtools.2014.03.002","journal-title":"International Journal of Machine Tools and Manufacture"},{"issue":"5\u20138","key":"1894_CR27","doi-asserted-by":"publisher","first-page":"2861","DOI":"10.1007\/s00170-017-1096-1","volume":"94","author":"F Tan","year":"2017","unstructured":"Tan, F., Yin, M., Wang, L., & Yin, G. (2017). Spindle thermal error robust modeling using LASSO and LS-SVM. The International Journal of Advanced Manufacturing Technology, 94(5\u20138), 2861\u20132874. https:\/\/doi.org\/10.1007\/s00170-017-1096-1","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"issue":"3","key":"1894_CR28","doi-asserted-by":"publisher","first-page":"575","DOI":"10.1007\/s10845-019-01469-w","volume":"31","author":"L Tian","year":"2020","unstructured":"Tian, L., & Luo, Y. (2020). A study on the prediction of inherent deformation in fillet-welded joint using support vector machine and genetic optimization algorithm. Journal of Intelligent Manufacturing, 31(3), 575\u2013596. https:\/\/doi.org\/10.1007\/s10845-019-01469-w","journal-title":"Journal of Intelligent Manufacturing"},{"key":"1894_CR29","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1016\/j.mechatronics.2018.06.018","volume":"53","author":"S Xiang","year":"2018","unstructured":"Xiang, S., Yao, X., Du, Z., & Yang, J. (2018). Dynamic linearization modeling approach for spindle thermal errors of machine tools. Mechatronics, 53, 215\u2013228. https:\/\/doi.org\/10.1016\/j.mechatronics.2018.06.018","journal-title":"Mechatronics"},{"issue":"2","key":"1894_CR30","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1115\/1.1557296","volume":"125","author":"H Yang","year":"2003","unstructured":"Yang, H., & Ni, J. (2003). Dynamic modeling for machine tool thermal error compensation. Transactions of the ASME Journal of Manufacturing Science and Engineering, 125(2), 245\u2013254. https:\/\/doi.org\/10.1115\/1.1557296","journal-title":"Transactions of the ASME Journal of Manufacturing Science and Engineering"},{"issue":"4\u20135","key":"1894_CR31","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1016\/j.ijmachtools.2004.09.004","volume":"45","author":"H Yang","year":"2005","unstructured":"Yang, H., & Ni, J. (2005). Dynamic neural network modeling for nonlinear, nonstationary machine tool thermally induced error. International Journal of Machine Tools and Manufacture, 45(4\u20135), 455\u2013465. https:\/\/doi.org\/10.1016\/j.ijmachtools.2004.09.004","journal-title":"International Journal of Machine Tools and Manufacture"},{"issue":"5\u20138","key":"1894_CR32","doi-asserted-by":"publisher","first-page":"1699","DOI":"10.1007\/s00170-018-2994-6","volume":"101","author":"Q Yin","year":"2018","unstructured":"Yin, Q., Tan, F., Chen, H., & Yin, G. (2018). Spindle thermal error modeling based on selective ensemble BP neural networks. The International Journal of Advanced Manufacturing Technology, 101(5\u20138), 1699\u20131713. https:\/\/doi.org\/10.1007\/s00170-018-2994-6","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"issue":"3","key":"1894_CR33","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1007\/s10845-014-1023-3","volume":"29","author":"S Zhang","year":"2018","unstructured":"Zhang, S., & Wong, T. N. (2018). Integrated process planning and scheduling: An enhanced ant colony optimization heuristic with parameter tuning. Journal of Intelligent Manufacturing, 29(3), 585\u2013601. https:\/\/doi.org\/10.1007\/s10845-014-1023-3","journal-title":"Journal of Intelligent Manufacturing"}],"container-title":["Journal of Intelligent Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-021-01894-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10845-021-01894-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-021-01894-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,15]],"date-time":"2023-03-15T17:08:24Z","timestamp":1678900104000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10845-021-01894-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,27]]},"references-count":33,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,4]]}},"alternative-id":["1894"],"URL":"https:\/\/doi.org\/10.1007\/s10845-021-01894-w","relation":{},"ISSN":["0956-5515","1572-8145"],"issn-type":[{"value":"0956-5515","type":"print"},{"value":"1572-8145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,27]]},"assertion":[{"value":"20 April 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 December 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 January 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}