{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T13:38:30Z","timestamp":1775741910737,"version":"3.50.1"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,9,22]],"date-time":"2020-09-22T00:00:00Z","timestamp":1600732800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,9,22]],"date-time":"2020-09-22T00:00:00Z","timestamp":1600732800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51405346"],"award-info":[{"award-number":["51405346"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["71471139"],"award-info":[{"award-number":["71471139"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["LY17E050005"],"award-info":[{"award-number":["LY17E050005"]}]},{"name":"Wenzhou City Public Industrial Science and Technology Project of China","award":["2018G0116"],"award-info":[{"award-number":["2018G0116"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Intell Manuf"],"published-print":{"date-parts":[[2022,1]]},"DOI":"10.1007\/s10845-020-01663-1","type":"journal-article","created":{"date-parts":[[2020,9,22]],"date-time":"2020-09-22T21:34:45Z","timestamp":1600810485000},"page":"247-258","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":80,"title":["Tool wear condition monitoring based on a two-layer angle kernel extreme learning machine using sound sensor for milling process"],"prefix":"10.1007","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8580-5427","authenticated-orcid":false,"given":"Yuqing","family":"Zhou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bintao","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4181-1606","authenticated-orcid":false,"given":"Weifang","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhi","family":"Lei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,9,22]]},"reference":[{"issue":"5\u20138","key":"1663_CR1","first-page":"457","volume":"61","author":"CS Ai","year":"2012","unstructured":"Ai, C. S., Sun, Y. J., He, G. W., Ze, X. B., Li, W., & Mao, K. (2012). The milling tool wear monitoring using the acoustic spectrum. The International Journal of Advanced Manufacturing Technology, 61(5\u20138), 457\u2013463.","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"issue":"2","key":"1663_CR2","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":"1\u20134","key":"1663_CR3","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1007\/s00170-014-5679-9","volume":"72","author":"A Ammouri","year":"2014","unstructured":"Ammouri, A., & Hamade, R. (2014). Current rise criterion: A process-independent method for tool-condition monitoring and prognostics. The International Journal of Advanced Manufacturing Technology, 72(1\u20134), 509\u2013519.","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"key":"1663_CR4","doi-asserted-by":"crossref","first-page":"1497","DOI":"10.1007\/s10845-019-01526-4","volume":"31","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, 31, 1497\u20131510.","journal-title":"Journal of Intelligent Manufacturing"},{"issue":"7","key":"1663_CR5","doi-asserted-by":"crossref","first-page":"2526","DOI":"10.1016\/j.ymssp.2011.03.001","volume":"25","author":"BJ Chen","year":"2011","unstructured":"Chen, B. J., Chen, X. F., Li, B., He, Z. J., Cao, H. R., & Cai, G. (2011). Reliability estimation for cutting tools based on logistic regression model using vibration signals. Mechanical Systems and Signal Processing, 25(7), 2526\u20132537.","journal-title":"Mechanical Systems and Signal Processing"},{"key":"1663_CR6","unstructured":"Cho, Y., Saul, L. K. (2009). Kernel methods for deep learning, advances in neural information processing systems. In Conference on neural information processing systems 2009, Vancouver, British Columbia, Canada (pp. 342\u2013350)."},{"issue":"2","key":"1663_CR7","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1115\/1.2899768","volume":"114","author":"G Chryssolouris","year":"1992","unstructured":"Chryssolouris, G., Domroese, M., & Beaulieu, P. (1992). Sensor synthesis for control of manufacturing processes. Journal of Engineering for Industry Transactions of the ASME, 114(2), 158\u2013174.","journal-title":"Journal of Engineering for Industry Transactions of the ASME"},{"issue":"10","key":"1663_CR8","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.rcim.2016.12.009","volume":"47","author":"B Cuka","year":"2017","unstructured":"Cuka, B., & Kim, D. (2017). Fuzzy logic based tool condition monitoring for end-milling. Robotics and Computer-Integrated Manufacturing, 47(10), 22\u201336.","journal-title":"Robotics and Computer-Integrated Manufacturing"},{"issue":"4","key":"1663_CR9","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.jmapro.2016.03.010","volume":"22","author":"C Drouillet","year":"2016","unstructured":"Drouillet, C., Karandikar, J., Nath, C., Journeaux, A. C., Mansori, M. E., & Kurfess, T. (2016). Tool life predictions in milling using spindle power with the neural network technique. Journal of Manufacturing Processes, 22(4), 161\u2013168.","journal-title":"Journal of Manufacturing Processes"},{"issue":"2","key":"1663_CR10","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1016\/j.cirp.2015.05.011","volume":"64","author":"R Gao","year":"2015","unstructured":"Gao, R., Wang, L., Teti, R., Dornfeld, D., Kumara, S., Mori, M., et al. (2015). Cloud-enabled prognosis for manufacturing. CIRP Annals, 64(2), 749\u2013772.","journal-title":"CIRP Annals"},{"issue":"4","key":"1663_CR11","doi-asserted-by":"crossref","first-page":"346","DOI":"10.3390\/app7040346","volume":"7","author":"C Gao","year":"2017","unstructured":"Gao, C., Xue, W., Ren, Y., & Zhou, Y. Q. (2017). Numerical control machine tool fault diagnosis using hybrid stationary subspace analysis and least squares support vector machine with a single sensor. Applied Sciences, 7(4), 346.","journal-title":"Applied Sciences"},{"issue":"9\u201310","key":"1663_CR12","doi-asserted-by":"crossref","first-page":"2619","DOI":"10.1016\/j.wear.2011.01.038","volume":"271","author":"J Ghani","year":"2011","unstructured":"Ghani, J., Rizal, M., Nuawi, M., Ghazali, M., & Haron, C. (2011). Monitoring online cutting tool wear using low-cost technique and user-friendly GUI. Wear, 271(9\u201310), 2619\u20132624.","journal-title":"Wear"},{"issue":"1","key":"1663_CR13","doi-asserted-by":"crossref","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., Mukhopadhyay, S., Paul, S., Mohanty, A. R., et al. (2007). Estimation of tool wear during CNC milling using neural network-based sensor fusion. Mechanical Systems and Signal Processing, 21(1), 466\u2013479.","journal-title":"Mechanical Systems and Signal Processing"},{"issue":"1\u20134","key":"1663_CR14","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1007\/s00170-011-3703-x","volume":"61","author":"W Hsieh","year":"2012","unstructured":"Hsieh, W., Lu, M., & Chiou, S. (2012). Application of backpropagation neural network for spindle vibration-based tool wear monitoring in micro-milling. The International Journal of Advanced Manufacturing Technology, 61(1\u20134), 53\u201361.","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"issue":"3","key":"1663_CR15","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1007\/s12559-015-9333-0","volume":"7","author":"GB Huang","year":"2015","unstructured":"Huang, G. B. (2015). What are extreme learning machines? Filling the gap between Frank Rosenblatt\u2019s dream and John von Neumann\u2019s puzzle. Cognitive Computation, 7(3), 263\u2013278.","journal-title":"Cognitive Computation"},{"key":"1663_CR16","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.neunet.2014.10.001","volume":"61","author":"G Huang","year":"2015","unstructured":"Huang, G., Huang, G. B., & Song, S. (2015a). Trends in extreme learning machines: A review. Neural Networks, 61, 32\u201348.","journal-title":"Neural Networks"},{"key":"1663_CR17","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.asoc.2015.08.019","volume":"37","author":"P Huang","year":"2015","unstructured":"Huang, P., Ma, C., & Kuo, C. (2015b). A PNN self-learning tool breakage detection system in end milling operations. Applied Soft Computing, 37, 114\u2013124.","journal-title":"Applied Soft Computing"},{"key":"1663_CR18","doi-asserted-by":"crossref","first-page":"953","DOI":"10.1007\/s10845-019-01488-7","volume":"31","author":"ZW Huang","year":"2020","unstructured":"Huang, Z. W., Zhu, J. M., Lei, J. T., Li, X., & Tian, F. Q. (2020). Tool wear predicting based on multi-domain feature fusion by deep convolutional neural network in milling operations. Journal of Intelligent Manufacturing, 31, 953\u2013966.","journal-title":"Journal of Intelligent Manufacturing"},{"issue":"8","key":"1663_CR19","doi-asserted-by":"crossref","first-page":"1873","DOI":"10.1007\/s10845-016-1221-2","volume":"29","author":"K Javed","year":"2018","unstructured":"Javed, K., Gouriveau, R., Li, X., & Zerhouni, N. (2018). Tool wear monitoring and prognostics challenges: A comparison of connectionist methods toward an adaptive ensemble model. Journal of Intelligent Manufacturing, 29(8), 1873\u20131890.","journal-title":"Journal of Intelligent Manufacturing"},{"key":"1663_CR20","doi-asserted-by":"crossref","first-page":"1613","DOI":"10.1007\/s00170-014-6560-6","volume":"77","author":"J Karandikar","year":"2015","unstructured":"Karandikar, J., Mcleay, T., Turner, S., & Schmitz, T. (2015). Tool wear monitoring using Na\u00efve Bayes classifiers. The International Journal of Advanced Manufacturing Technology, 77, 1613\u20131626.","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"issue":"1","key":"1663_CR21","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/j.cirp.2016.04.101","volume":"65","author":"R Koike","year":"2016","unstructured":"Koike, R., Ohnishi, K., & Aoyama, T. (2016). A sensorless approach for tool fracture detection in milling by integrating multi-axial servo information. CIRP Annals, 65(1), 385\u2013388.","journal-title":"CIRP Annals"},{"issue":"5","key":"1663_CR22","first-page":"1197","volume":"71","author":"S Konstantinos","year":"2014","unstructured":"Konstantinos, S., & Athanasios, K. (2014). Reliability assessment of cutting tool life based on surrogate approximation methods. The International Journal of Advanced Manufacturing Technology, 71(5), 1197\u20131208.","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"key":"1663_CR23","doi-asserted-by":"crossref","first-page":"995","DOI":"10.1016\/j.promfg.2019.06.096","volume":"34","author":"A Kothuru","year":"2019","unstructured":"Kothuru, A., Nooka, S. P., & Liu, R. (2019). Application of deep visualization in CNN-based tool condition monitoring for end milling. Procedia Manufacturing, 34, 995\u20131004.","journal-title":"Procedia Manufacturing"},{"issue":"4","key":"1663_CR24","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1007\/s001700050062","volume":"15","author":"B Lee","year":"1999","unstructured":"Lee, B. (1999). Application of the discrete wavelet transform to the monitoring of tool failure in end milling using the spindle motor current. The International Journal of Advanced Manufacturing Technology, 15(4), 238\u2013243.","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"issue":"3\u20134","key":"1663_CR25","first-page":"1203","volume":"106","author":"Z Lei","year":"2019","unstructured":"Lei, Z., Zhou, Y. Q., Sun, B. T., & Sun, W. F. (2019). An intrinsic timescale decomposition-based kernel extreme learning machine method to detect tool wear conditions in the milling process. The International Journal of Advanced Manufacturing Technology, 106(3\u20134), 1203\u20131212.","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"key":"1663_CR26","doi-asserted-by":"publisher","DOI":"10.1007\/s10845-020-01588-9","author":"L Li","year":"2020","unstructured":"Li, L., Wang, Y., & Lin, K. (2020). Preventive maintenance scheduling optimization based on opportunistic production-maintenance synchronization. Journal of Intelligent Manufacturing. https:\/\/doi.org\/10.1007\/s10845-020-01588-9.","journal-title":"Journal of Intelligent Manufacturing"},{"key":"1663_CR28","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.asoc.2015.06.023","volume":"35","author":"C Liu","year":"2015","unstructured":"Liu, C., Wang, G. F., & Li, Z. M. (2015). Incremental learning for online tool condition monitoring using ellipsoid artmap network model. Applied Soft Computing, 35, 186\u2013198.","journal-title":"Applied Soft Computing"},{"issue":"1\u20134","key":"1663_CR27","first-page":"3361","volume":"103","author":"MK Liu","year":"2019","unstructured":"Liu, M. K., Tseng, Y. H., & Tran, M. Q. (2019). Tool wear monitoring and prediction based on sound signal. The International Journal of Advanced Manufacturing Technology, 103(1\u20134), 3361\u20133373.","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"issue":"S2","key":"1663_CR29","first-page":"1643","volume":"8","author":"CK Madhusudana","year":"2017","unstructured":"Madhusudana, C. K., Kumar, H., & Narendranath, S. (2017). Face milling tool condition monitoring using sound signal. International Journal of System Assurance Engineering and Management, 8(S2), 1643\u20131653.","journal-title":"International Journal of System Assurance Engineering and Management"},{"issue":"3","key":"1663_CR30","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1007\/s12046-008-0016-3","volume":"33","author":"M Mathew","year":"2008","unstructured":"Mathew, M., Pai, P., & Rocha, L. (2008). An effective sensor for tool wear monitoring in face milling: acoustic emission. Sadhana, 33(3), 227\u2013233.","journal-title":"Sadhana"},{"key":"1663_CR31","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.proeng.2017.02.294","volume":"176","author":"V Pechenin","year":"2017","unstructured":"Pechenin, V., Khaimovich, A., Kondratiev, A., & Bolotov, M. (2017). Method of controlling cutting tool wear based on signal analysis of acoustic emission for milling. Procedia Engineering, 176, 246\u2013252.","journal-title":"Procedia Engineering"},{"issue":"1","key":"1663_CR32","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/S0890-6955(98)00020-0","volume":"39","author":"P Prickett","year":"1999","unstructured":"Prickett, P., & Johns, C. (1999). An overview of approaches to end milling tool monitoring. International Journal of Machine Tools and Manufacture, 39(1), 105\u2013122.","journal-title":"International Journal of Machine Tools and Manufacture"},{"issue":"1\u20132","key":"1663_CR33","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.ymssp.2013.02.004","volume":"44","author":"M Ritou","year":"2014","unstructured":"Ritou, M., Garnier, S., Furet, B., & Hascoet, J. (2014). Angular approach combined to mechanical model for tool breakage detection by eddy current sensors. Mechanical Systems and Signal Processing, 44(1\u20132), 211\u2013220.","journal-title":"Mechanical Systems and Signal Processing"},{"issue":"10","key":"1663_CR34","doi-asserted-by":"crossref","first-page":"2083","DOI":"10.19026\/rjaset.7.502","volume":"7","author":"M Rizal","year":"2014","unstructured":"Rizal, M., Ghani, J., Nuawi, M., & Che, H. (2014). A review of sensor system and application in milling process for tool condition monitoring. Research Journal of Applied Engineering & Technology, 7(10), 2083\u20132097.","journal-title":"Research Journal of Applied Engineering & Technology"},{"issue":"1\u20134","key":"1663_CR35","first-page":"1","volume":"87","author":"A Salimiasl","year":"2016","unstructured":"Salimiasl, A., & \u00d6zdemir, A. (2016). Analyzing the performance of artificial neural network (ANN)-, fuzzy logic (FL)-, and least square (LS)-based models for online tool condition monitoring. The International Journal of Advanced Manufacturing Technology, 87(1\u20134), 1\u201314.","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"issue":"9\u201312","key":"1663_CR36","doi-asserted-by":"crossref","first-page":"1141","DOI":"10.1007\/s00170-010-2907-9","volume":"53","author":"P Sevilla","year":"2011","unstructured":"Sevilla, P., Herrera, G., Robles, J., & J\u00e1uregui, J. (2011). Tool breakage detection in CNC high-speed milling based in feed-motor current signals. The International Journal of Advanced Manufacturing Technology, 53(9\u201312), 1141\u20131148.","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"issue":"4","key":"1663_CR37","first-page":"473","volume":"227","author":"P Sevilla","year":"2013","unstructured":"Sevilla, P., Jauregui, J., Herrera, G., & Robles, J. (2013). Efficient method for detecting tool failures in high-speed machining process. Journal of Engineering Manufacturing, 227(4), 473\u2013482.","journal-title":"Journal of Engineering Manufacturing"},{"key":"1663_CR38","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.measurement.2014.12.037","volume":"64","author":"P Sevilla","year":"2015","unstructured":"Sevilla, P., Robles, J., Jauregui, J., & Jimenez, D. (2015a). FPGA-based reconfigurable system for tool condition monitoring in high-speed machining process. Measurement, 64, 81\u201388.","journal-title":"Measurement"},{"issue":"5\u20138","key":"1663_CR39","first-page":"1","volume":"81","author":"P Sevilla","year":"2015","unstructured":"Sevilla, P., Robles, J., Jauregui, J., & Lee, F. (2015b). Tool failure detection method for high-speed milling using vibration signal and reconfigurable bandpass digital filtering. The International Journal of Advanced Manufacturing Technology, 81(5\u20138), 1\u20138.","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"key":"1663_CR40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.knosys.2017.10.024","volume":"140","author":"HD Shao","year":"2018","unstructured":"Shao, H. D., Jiang, H. K., Li, X. Q., & Wu, S. P. (2018). Intelligent fault diagnosis of rolling bearing using deep wavelet auto-encoder with extreme learning machine. Knowledge- Based Systems, 140, 1\u201314.","journal-title":"Knowledge- Based Systems"},{"issue":"17","key":"1663_CR41","doi-asserted-by":"crossref","first-page":"3609","DOI":"10.1016\/j.neucom.2011.06.026","volume":"74","author":"T Shawe","year":"2011","unstructured":"Shawe, T., & Sun, S. (2011). A review of optimization methodologies in support vector machines. Neurocomputing, 74(17), 3609\u20133618.","journal-title":"Neurocomputing"},{"issue":"1\u20134","key":"1663_CR42","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1007\/s00170-012-4177-1","volume":"65","author":"A Siddhpura","year":"2013","unstructured":"Siddhpura, A., & Paurobally, R. (2013). A review of flank wear prediction methods for tool condition monitoring in a turning process. The International Journal of Advanced Manufacturing Technology, 65(1\u20134), 371\u2013393.","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"key":"1663_CR43","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.knosys.2017.07.014","volume":"134","author":"G Song","year":"2017","unstructured":"Song, G., & Dai, Q. (2017). A novel double deep ELMs ensemble system for time series forecasting. Knowledge- Based Systems, 134, 31\u201349.","journal-title":"Knowledge- Based Systems"},{"issue":"1\u20134","key":"1663_CR44","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1007\/s00170-015-7317-6","volume":"82","author":"P Stavropoulos","year":"2016","unstructured":"Stavropoulos, P., Papacharalampopoulos, A., Vasiliadis, E., & Chryssolouris, G. (2016). Tool wear predictability estimation in milling based on multi-sensorial data. The International Journal of Advanced Manufacturing Technology, 82(1\u20134), 509\u2013521.","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"issue":"2","key":"1663_CR45","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1109\/JSYST.2015.2425793","volume":"10","author":"AJ Torabi","year":"2016","unstructured":"Torabi, A. J., Meng, J. E., Li, X., Lim, B. S., & Peen, G. (2016). Application of clustering methods for online tool condition monitoring and fault diagnosis in high-speed milling processes. IEEE Systems Journal, 10(2), 721\u2013732.","journal-title":"IEEE Systems Journal"},{"key":"1663_CR46","doi-asserted-by":"crossref","first-page":"3061","DOI":"10.1177\/1077546314520835","volume":"21","author":"G Vetrichelvan","year":"2014","unstructured":"Vetrichelvan, G., Sundaram, S., Kumaran, S. S., & Velmurugan, P. (2014). An investigation of tool wear using acoustic emission and genetic algorithm. Journal of Vibration and Control, 21, 3061\u20133066.","journal-title":"Journal of Vibration and Control"},{"key":"1663_CR50","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.sna.2014.01.004","volume":"209","author":"G Wang","year":"2014","unstructured":"Wang, G., Yang, Y., Zhang, Y., & Xie, Q. (2014). Vibration sensor based tool condition monitoring using \u03bd, support vector machine and locality preserving projection. Sensors and Actuators, A: Physical, 209, 24\u201332.","journal-title":"Sensors and Actuators, A: Physical"},{"key":"1663_CR51","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1007\/s10845-016-1235-9","volume":"30","author":"GF Wang","year":"2019","unstructured":"Wang, G. F., Zhang, Y. C., Liu, C., Xie, Q. L., & Xu, Y. G. (2019). A new tool wear monitoring method based on multi-scale PCA. Journal of Intelligent Manufacturing, 30, 113\u2013122.","journal-title":"Journal of Intelligent Manufacturing"},{"key":"1663_CR49","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.rcim.2016.05.010","volume":"45","author":"J Wang","year":"2017","unstructured":"Wang, J., Xie, J., Zhao, R., Zhang, L., & Duan, L. (2017). Multisensory fusion based virtual tool wear sensing for ubiquitous manufacturing. Robotics & Computer Integrated Manufacturing, 45, 47\u201358.","journal-title":"Robotics & Computer Integrated Manufacturing"},{"issue":"5\u20138","key":"1663_CR48","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1007\/s00170-011-3536-7","volume":"59","author":"M Wang","year":"2012","unstructured":"Wang, M., & Wang, J. (2012). CHMM for tool condition monitoring and remaining useful life prediction. The International Journal of Advanced Manufacturing Technology, 59(5\u20138), 463\u2013471.","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"key":"1663_CR47","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.procir.2016.03.101","volume":"48","author":"P Wang","year":"2016","unstructured":"Wang, P., & Gao, R. X. (2016). Stochastic tool wear prediction for sustainable manufacturing. Procedia CIRP, 48, 236\u2013241.","journal-title":"Procedia CIRP"},{"issue":"8","key":"1663_CR52","first-page":"1","volume":"25","author":"XF Wu","year":"2019","unstructured":"Wu, X. F., Liu, Y. H., & Bi, S. Z. (2019). Intelligent recognition of tool wear type based on convolutional neural networks. Computer Integrated Manufacturing Systems, 25(8), 1\u201316. (in China).","journal-title":"Computer Integrated Manufacturing Systems"},{"issue":"1\u20132","key":"1663_CR53","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1016\/j.ymssp.2012.05.001","volume":"34","author":"C Yen","year":"2013","unstructured":"Yen, C., Lu, M., & Chen, J. (2013). Applying the self-organization feature map (som) algorithm to ae-based tool wear monitoring in micro-cutting. Mechanical Systems and Signal Processing, 34(1\u20132), 353\u2013366.","journal-title":"Mechanical Systems and Signal Processing"},{"issue":"795","key":"1663_CR54","first-page":"1","volume":"16","author":"C Zhang","year":"2016","unstructured":"Zhang, C., Yao, X., Zhang, J., & Jin, H. (2016). Tool condition monitoring and remaining useful life prognostic based on a wireless sensor in dry milling operations. Sensors, 16(795), 1\u201320.","journal-title":"Sensors"},{"issue":"1","key":"1663_CR55","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1177\/0954405414526578","volume":"229","author":"H Zhang","year":"2015","unstructured":"Zhang, H., Zhao, J., Wang, F., & Li, A. (2015). Cutting forces and tool failure in high-speed milling of titanium alloy TC21 with coated carbide tools. Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture, 229(1), 20\u201327.","journal-title":"Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture"},{"issue":"2","key":"1663_CR56","doi-asserted-by":"crossref","first-page":"1539","DOI":"10.1109\/TIE.2017.2733438","volume":"65","author":"R Zhao","year":"2018","unstructured":"Zhao, R., Wang, D. Z., Yan, R. Q., Mao, K. Z., Shen, F., & Wang, J. J. (2018). Machine health monitoring using local feature-based gated recurrent unit networks. IEEE Transactions on Industrial Electronics, 65(2), 1539\u20131548.","journal-title":"IEEE Transactions on Industrial Electronics"},{"issue":"15","key":"1663_CR57","doi-asserted-by":"crossref","first-page":"2925","DOI":"10.1177\/1077546314545097","volume":"21","author":"YQ Zhou","year":"2015","unstructured":"Zhou, Y. Q., Liu, X. F., Li, F. P., Sun, B. T., & Xue, W. (2015). An online damage identification approach for numerical control machine tools based on data fusion using vibration signals. Journal of Vibration and Control, 21(15), 2925\u20132936.","journal-title":"Journal of Vibration and Control"},{"key":"1663_CR58","doi-asserted-by":"crossref","first-page":"2509","DOI":"10.1007\/s00170-018-1768-5","volume":"96","author":"YQ Zhou","year":"2018","unstructured":"Zhou, Y. Q., & Xue, W. (2018). Review of tool condition monitoring methods in milling processes. The International Journal of Advanced Manufacturing Technology, 96, 2509\u20132523.","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"issue":"1\u20134","key":"1663_CR59","first-page":"185","volume":"70","author":"KP Zhu","year":"2014","unstructured":"Zhu, K. P., & Vogel, B. (2014). Sparse representation and its applications in micro-milling condition monitoring: noise separation and tool condition monitoring. The International Journal of Advanced Manufacturing Technology, 70(1\u20134), 185\u2013199.","journal-title":"The International Journal of Advanced Manufacturing Technology"}],"container-title":["Journal of Intelligent Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-020-01663-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10845-020-01663-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-020-01663-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,5]],"date-time":"2022-01-05T19:37:10Z","timestamp":1641411430000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10845-020-01663-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,22]]},"references-count":59,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,1]]}},"alternative-id":["1663"],"URL":"https:\/\/doi.org\/10.1007\/s10845-020-01663-1","relation":{},"ISSN":["0956-5515","1572-8145"],"issn-type":[{"value":"0956-5515","type":"print"},{"value":"1572-8145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,22]]},"assertion":[{"value":"18 November 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 September 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 September 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}