{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T08:35:21Z","timestamp":1782376521424,"version":"3.54.5"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2023,2,26]],"date-time":"2023-02-26T00:00:00Z","timestamp":1677369600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,2,26]],"date-time":"2023-02-26T00:00:00Z","timestamp":1677369600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51875329"],"award-info":[{"award-number":["51875329"]}],"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":["51905322"],"award-info":[{"award-number":["51905322"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010029","name":"Taishan Scholar Foundation of Shandong Province","doi-asserted-by":"publisher","award":["tsqn201812064"],"award-info":[{"award-number":["tsqn201812064"]}],"id":[{"id":"10.13039\/501100010029","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shandong Provincial Key Research & Development Project","award":["2018GGX103008"],"award-info":[{"award-number":["2018GGX103008"]}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2017MEE050"],"award-info":[{"award-number":["ZR2017MEE050"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Scientific Innovation Project for Young Scientists in Shandong Provincial Universities","award":["2019KJB030"],"award-info":[{"award-number":["2019KJB030"]}]},{"name":"Key Research & Development Project of Zibo","award":["2019ZBXC070"],"award-info":[{"award-number":["2019ZBXC070"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Intell Manuf"],"published-print":{"date-parts":[[2024,3]]},"DOI":"10.1007\/s10845-023-02089-1","type":"journal-article","created":{"date-parts":[[2023,2,26]],"date-time":"2023-02-26T06:02:11Z","timestamp":1677391331000},"page":"1013-1035","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Development of grinding intelligent monitoring and big data-driven decision making expert system towards high efficiency and low energy consumption: experimental approach"],"prefix":"10.1007","volume":"35","author":[{"given":"Jinling","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6141-9097","authenticated-orcid":false,"given":"Yebing","family":"Tian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xintao","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zenghua","family":"Fan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinguo","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanhou","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,2,26]]},"reference":[{"key":"2089_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.pecs.2021.100904","volume":"85","author":"M Aghbashlo","year":"2021","unstructured":"Aghbashlo, M., Peng, W. X., Tabatabaei, M., Kalogirou, S. A., Soltanian, S., Hosseinzadeh-Bandbafha, H., Mahian, O., & Lam, S. S. (2021). Machine learning technology in biodiesel research: A review. Progress in Energy and Combustion Science, 85, 1\u2013112. https:\/\/doi.org\/10.1016\/j.pecs.2021.100904","journal-title":"Progress in Energy and Combustion Science"},{"key":"2089_CR2","doi-asserted-by":"publisher","first-page":"1469","DOI":"10.1007\/s10845-017-1326-2","volume":"30","author":"MS Alajmi","year":"2019","unstructured":"Alajmi, M. S., Alfares, F. S., & Alfares, M. S. (2019). Selection of optimal conditions in the surface grinding process using the quantum based optimisation method. Journal of Intelligent Manufacturing, 30, 1469\u20131481. https:\/\/doi.org\/10.1007\/s10845-017-1326-2","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2089_CR3","doi-asserted-by":"publisher","first-page":"11888","DOI":"10.1016\/j.precisioneng.2018.09.018","volume":"5","author":"A Arun","year":"2018","unstructured":"Arun, A., Rameshkumar, K., Unnikrishnan, D., & Sumesh, A. (2018). Tool condition monitoring of cylindrical grinding process using acoustic emission sensor. Materials Today: Proceedings, 5, 11888\u201311899. https:\/\/doi.org\/10.1016\/j.precisioneng.2018.09.018","journal-title":"Materials Today: Proceedings"},{"key":"2089_CR4","doi-asserted-by":"publisher","first-page":"292","DOI":"10.1016\/j.procir.2019.02.069","volume":"79","author":"S Bracke","year":"2019","unstructured":"Bracke, S., Radetzky, M., Rosebrock, C., & Ulutas, B. (2019). Efficiency and effectivity of high precision grinding manufacturing processes: An approach based on combined DEA and cluster analyses. Procedia CIRP, 79, 292\u2013297. https:\/\/doi.org\/10.1016\/j.procir.2019.02.069","journal-title":"Procedia CIRP"},{"key":"2089_CR5","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1016\/j.procir.2014.04.073","volume":"13","author":"E Brinksmeier","year":"2014","unstructured":"Brinksmeier, E., Klocke, F., Lucca, D. A., Solter, J., & Meyer, D. (2014). Process signatures\u2014A new approach to solve the inverse surface integrity problem in machining processes. Procedia CIRP, 13, 429\u2013434. https:\/\/doi.org\/10.1016\/j.procir.2014.04.073","journal-title":"Procedia CIRP"},{"key":"2089_CR6","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.cirpj.2022.11.022","volume":"41","author":"SJ Cai","year":"2023","unstructured":"Cai, S. J., Cai, Z. Q., & Lin, C. (2023). Modeling of the generating face gear grinding force and the prediction of the tooth surface topography based on the abrasive differential element method. CIRP Journal of Manufacturing Science and Technology, 41, 80\u201393. https:\/\/doi.org\/10.1016\/j.cirpj.2022.11.022","journal-title":"CIRP Journal of Manufacturing Science and Technology"},{"key":"2089_CR7","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1007\/s10845-015-1100-2","volume":"29","author":"S Chaki","year":"2018","unstructured":"Chaki, S., Bathe, R. N., Ghosal, S., & Padmanabham, G. (2018). Multi-objective optimisation of pulsed Nd:YAG laser cutting process using integrated ANN\u2013NSGAII model. Journal of Intelligent Manufacturing, 29, 175\u2013190. https:\/\/doi.org\/10.1007\/s10845-015-1100-2","journal-title":"Journal of Intelligent Manufacturing"},{"issue":"8","key":"2089_CR8","doi-asserted-by":"publisher","first-page":"1899","DOI":"10.1080\/00207540600562025","volume":"45","author":"T Choi","year":"2007","unstructured":"Choi, T., & Shin, Y. C. (2007). Generalized intelligent grinding advisory system. International Journal of Production Research, 45(8), 1899\u20131932. https:\/\/doi.org\/10.1080\/00207540600562025","journal-title":"International Journal of Production Research"},{"key":"2089_CR9","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1016\/j.precisioneng.2017.12.005","volume":"52","author":"CW Dai","year":"2018","unstructured":"Dai, C. W., Ding, W. F., Zhu, Y. J., Xu, J. H., & Yu, H. W. (2018). Grinding temperature and power consumption in high speed grinding of Inconel 718 nickel-based superalloy with a vitrified CBN wheel. Precision Engineering, 52, 192\u2013200. https:\/\/doi.org\/10.1016\/j.precisioneng.2017.12.005","journal-title":"Precision Engineering"},{"key":"2089_CR10","doi-asserted-by":"publisher","first-page":"904","DOI":"10.1016\/j.jclepro.2018.02.246","volume":"185","author":"ZH Deng","year":"2018","unstructured":"Deng, Z. H., Zhang, H., Fu, Y. H., Wan, L. L., & Lv, L. S. (2018). Research on intelligent expert system of green cutting process and its application. Journal of Cleaner Production, 185, 904\u2013911. https:\/\/doi.org\/10.1016\/j.jclepro.2018.02.246","journal-title":"Journal of Cleaner Production"},{"key":"2089_CR11","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1016\/j.precisioneng.2017.07.019","volume":"51","author":"Y Fukuhara","year":"2018","unstructured":"Fukuhara, Y., Suzuki, S., & Sasahara, H. (2018). Real-time grinding state discrimination strategy by use of monitor-embedded grinding wheels. Precision Engineering, 51, 128\u2013136. https:\/\/doi.org\/10.1016\/j.precisioneng.2017.07.019","journal-title":"Precision Engineering"},{"key":"2089_CR12","doi-asserted-by":"publisher","first-page":"1783","DOI":"10.1007\/s10845-010-0481-5","volume":"23","author":"VN Gaitonde","year":"2012","unstructured":"Gaitonde, V. N., & Karnik, S. R. (2012). Minimizing burr size in drilling using artificial neural network (ANN)-particle swarm optimization (PSO) approach. Journal of Intelligent Manufacturing, 23, 1783\u20131793. https:\/\/doi.org\/10.1007\/s10845-010-0481-5","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2089_CR13","doi-asserted-by":"publisher","first-page":"3175","DOI":"10.1007\/s00170-019-03735-w","volume":"103","author":"YD Gong","year":"2019","unstructured":"Gong, Y. D., Qu, S. S., Yang, Y. Y., Liang, C. Y., Li, P. F., & She, Y. B. (2019). Some observations in grinding SiC and silicon carbide ceramic matrix composite material. The International Journal of Advanced Manufacturing Technology, 103, 3175\u20133186. https:\/\/doi.org\/10.1007\/s00170-019-03735-w","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"key":"2089_CR14","doi-asserted-by":"publisher","first-page":"2837","DOI":"10.1007\/s00170-019-03748-5","volume":"103","author":"WC Guo","year":"2019","unstructured":"Guo, W. C., Li, B. Z., Shen, S. G., & Zhou, Q. Z. (2019). An intelligent grinding burn detection system based on two-stage feature selection and stacked sparse autoencoder. The International Journal of Advanced Manufacturing Technology, 103, 2837\u20132847. https:\/\/doi.org\/10.1007\/s00170-019-03748-5","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"key":"2089_CR15","doi-asserted-by":"publisher","first-page":"1947","DOI":"10.1016\/j.matpr.2021.11.277","volume":"56","author":"AW Hashmi","year":"2022","unstructured":"Hashmi, A. W., Mali, H. S., Meena, A., Khilji, I. A., Hashmi, M. F., & Saffe, S. N. B. M. (2022). Artificial intelligence techniques for implementation of intelligent machining. Materials Today: Proceedings, 56, 1947\u20131955. https:\/\/doi.org\/10.1016\/j.matpr.2021.11.277","journal-title":"Materials Today: Proceedings"},{"issue":"1","key":"2089_CR16","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1007\/s11465-007-0018-6","volume":"2","author":"Y He","year":"2007","unstructured":"He, Y., Liu, F., Cao, H. J., & Zhang, H. (2007). Process planning support system for green manufacturing and its application. Frontiers of Mechanical Engineering, 2(1), 104\u2013109. https:\/\/doi.org\/10.1007\/s11465-007-0018-6","journal-title":"Frontiers of Mechanical Engineering"},{"issue":"1","key":"2089_CR17","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1016\/j.cirp.2020.04.105","volume":"69","author":"T Kizaki","year":"2020","unstructured":"Kizaki, T., Hao, Y., Ohashi, T., Kokubo, T., & Nishijima, T. (2020). Capability of a grinding wheel reinforced in hoop direction with carbon fiber. CIRP Annals: Manufacturing Technology, 69(1), 285\u2013288. https:\/\/doi.org\/10.1016\/j.cirp.2020.04.105","journal-title":"CIRP Annals: Manufacturing Technology"},{"key":"2089_CR18","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1016\/j.jmapro.2020.09.075","volume":"61","author":"T Kizaki","year":"2021","unstructured":"Kizaki, T., Takahashi, K., Katsuma, T., Shu, L. M., & Sugita, N. (2021). Prospects of dry continuous generating grinding based on specific energy requirement. Journal of Manufacturing Processes, 61, 190\u2013207. https:\/\/doi.org\/10.1016\/j.jmapro.2020.09.075","journal-title":"Journal of Manufacturing Processes"},{"key":"2089_CR19","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1016\/j.arcontrol.2019.02.001","volume":"47","author":"A Kusiak","year":"2019","unstructured":"Kusiak, A. (2019). Fundamentals of smart manufacturing: A multi-thread perspective. Annual Reviews in Control, 47, 214\u2013220. https:\/\/doi.org\/10.1016\/j.arcontrol.2019.02.001","journal-title":"Annual Reviews in Control"},{"key":"2089_CR20","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1016\/j.promfg.2020.07.009","volume":"49","author":"ET Lee","year":"2020","unstructured":"Lee, E. T., Fan, Z. Y., & Sencer, B. (2020). Real-time grinding wheel condition monitoring using linear imaging sensor. Procedia Manufacturing, 49, 139\u2013143. https:\/\/doi.org\/10.1016\/j.promfg.2020.07.009","journal-title":"Procedia Manufacturing"},{"key":"2089_CR21","doi-asserted-by":"publisher","first-page":"124","DOI":"10.13462\/j.cnki.Mmtamt.2021.10.028","volume":"10","author":"Y Li","year":"2021","unstructured":"Li, Y., Liu, Y. H., Zhang, K., Tian, Y. B., & Tian, C. J. (2021). Prediction of grinding energy consumption and optimization of process parameters based on improved genetic algorithm. Modular Machine Tool and Automatic Manufacturing Technique, 10, 124\u2013128. https:\/\/doi.org\/10.13462\/j.cnki.Mmtamt.2021.10.028","journal-title":"Modular Machine Tool and Automatic Manufacturing Technique"},{"issue":"2","key":"2089_CR22","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/MASSP.1987.1165576","volume":"4","author":"RP Lippmann","year":"1987","unstructured":"Lippmann, R. P. (1987). An introduction to computing with neural nets. IEEE ASSP Magazine, 4(2), 4\u201322. https:\/\/doi.org\/10.1109\/MASSP.1987.1165576","journal-title":"IEEE ASSP Magazine"},{"key":"2089_CR23","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1016\/j.jmsy.2020.06.010","volume":"56","author":"YQ Lu","year":"2020","unstructured":"Lu, Y. Q., Xu, X., & Wang, L. H. (2020). Smart manufacturing process and system automation\u2014A critical review of the standards and envisioned scenarios. Journal of Manufacturing Systems, 56, 312\u2013325. https:\/\/doi.org\/10.1016\/j.jmsy.2020.06.010","journal-title":"Journal of Manufacturing Systems"},{"key":"2089_CR24","doi-asserted-by":"publisher","first-page":"1599","DOI":"10.1007\/s10845-016-1193-2","volume":"29","author":"K Maity","year":"2018","unstructured":"Maity, K., & Mishra, H. (2018). ANN modelling and Elitist teaching learning approach for multi-objective optimization of \u03bc-EDM. Journal of Intelligent Manufacturing, 29, 1599\u20131616. https:\/\/doi.org\/10.1007\/s10845-016-1193-2","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2089_CR25","unstructured":"Malkin, S., & Guo, C. (2008). Grinding technology: Theory and applications of machining with abrasives. Industrial Press."},{"key":"2089_CR26","doi-asserted-by":"crossref","unstructured":"Marinescu, I. D., Hitchiner, M. P., Uhlmann, E., Rowe, W. B., & Inasaki, I. (2016). Handbook of machining with grinding wheels (2nd ed.). CRC Press.","DOI":"10.1201\/b19462"},{"issue":"1","key":"2089_CR27","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1504\/IJAT.2007.013853","volume":"1","author":"MN Morgan","year":"2007","unstructured":"Morgan, M. N., Cai, R., Guidotti, A., Allanson, D. R., Moruzzi, J. L., & Rowe, W. B. (2007). Design and implementation of an intelligent grinding assistant system. International Journal of Abrasive Technology, 1(1), 106\u2013135. https:\/\/doi.org\/10.1504\/IJAT.2007.013853","journal-title":"International Journal of Abrasive Technology"},{"issue":"56","key":"2089_CR28","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1016\/j.jmapro.2017.11.014","volume":"31","author":"V Pandiyan","year":"2018","unstructured":"Pandiyan, V., Caesarendra, W., Tjahjowidodo, T., & Tan, H. H. (2018). In-process tool condition monitoring in compliant abrasive belt grinding process using support vector machine and genetic algorithm. Journal of Manufacturing Processes, 31(56), 199\u2013213. https:\/\/doi.org\/10.1016\/j.jmapro.2017.11.014","journal-title":"Journal of Manufacturing Processes"},{"key":"2089_CR29","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1007\/s10845-014-1019-z","volume":"28","author":"J Peng","year":"2017","unstructured":"Peng, J., & Gao, J. (2017). Foreword to the special issue of journal of intelligent manufacturing on uncertain models in intelligent manufacturing systems: Dedicated to Professor Mistuo Gen for his 70th birthday. Journal of Intelligent Manufacturing, 28, 501\u2013502. https:\/\/doi.org\/10.1007\/s10845-014-1019-z","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2089_CR30","doi-asserted-by":"publisher","first-page":"2023","DOI":"10.1007\/s10845-021-01759-2","volume":"32","author":"M Seitz","year":"2021","unstructured":"Seitz, M., Gehlhoff, F., Salazar, L. A. C., Fay, A., & Vogel-Heuser, B. (2021). Automation platform independent multi-agent system for robust networks of production resources in industry 4.0. Journal of Intelligent Manufacturing, 32, 2023\u20132041. https:\/\/doi.org\/10.1007\/s10845-021-01759-2","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2089_CR31","doi-asserted-by":"publisher","first-page":"604","DOI":"10.1631\/FITEE.1601660","volume":"19","author":"DP Tan","year":"2018","unstructured":"Tan, D. P., Chen, S. T., Bao, G. J., & Zhang, L. B. (2018). An embedded lightweight GUI component library and ergonomics optimization method for industry process monitoring. Frontiers of Information Technology and Electronic Engineering, 19, 604\u2013625. https:\/\/doi.org\/10.1631\/FITEE.1601660","journal-title":"Frontiers of Information Technology and Electronic Engineering"},{"key":"2089_CR32","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1007\/s10845-016-1265-3","volume":"30","author":"DP Tan","year":"2019","unstructured":"Tan, D. P., Zhang, L. B., & Ai, Q. L. (2019). An embedded self-adapting network service framework for networked manufacturing system. Journal of Intelligent Manufacturing, 30, 539\u2013556. https:\/\/doi.org\/10.1007\/s10845-016-1265-3","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2089_CR33","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.measurement.2019.05.079","volume":"145","author":"R Thomazella","year":"2019","unstructured":"Thomazella, R., Lopes, W. N., Aguiar, P. R., Alexandre, F. A., Fiochi, A. A., & Bianchi, E. C. (2019). Digital signal processing for self-vibration monitoring in grinding: A new approach based on the time\u2013frequency analysis of vibration signals. Measurement, 145, 71\u201383. https:\/\/doi.org\/10.1016\/j.measurement.2019.05.079","journal-title":"Measurement"},{"key":"2089_CR34","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1016\/j.jmapro.2017.05.002","volume":"27","author":"YB Tian","year":"2017","unstructured":"Tian, Y. B., Liu, F., Wang, Y., & Wu, H. (2017). Development of portable power monitoring system and grinding analytical tool. Journal of Manufacturing Processes, 27, 188\u2013197. https:\/\/doi.org\/10.1016\/j.jmapro.2017.05.002","journal-title":"Journal of Manufacturing Processes"},{"issue":"3","key":"2089_CR35","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1016\/j.jestch.2018.04.014","volume":"21","author":"DR Unune","year":"2018","unstructured":"Unune, D. R., Nirala, C. K., & Mali, H. S. (2018). ANN\u2013NSGA-II dual approach for modeling and optimization in abrasive mixed electro discharge diamond grinding of Monel K-500. Engineering Science and Technology, 21(3), 322\u2013329. https:\/\/doi.org\/10.1016\/j.jestch.2018.04.014","journal-title":"Engineering Science and Technology"},{"key":"2089_CR36","doi-asserted-by":"publisher","first-page":"1533","DOI":"10.1007\/s10845-016-1197-y","volume":"29","author":"K Venkata Rao","year":"2018","unstructured":"Venkata Rao, K., & Murthy, P. B. G. S. N. (2018). Modeling and optimization of tool vibration and surface roughness in boring of steel using RSM, ANN and SVM. Journal of Intelligent Manufacturing, 29, 1533\u20131543. https:\/\/doi.org\/10.1007\/s10845-016-1197-y","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2089_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.ceramint.2022.12.238","author":"LL Wan","year":"2022","unstructured":"Wan, L. L., Zhang, X. Y., Zhou, Q. M., Wen, D. D., & Ran, X. R. (2022). Acoustic emission identification of wheel wear states in engineering ceramic grinding based on parameter-adaptive VMD. Ceramics International. https:\/\/doi.org\/10.1016\/j.ceramint.2022.12.238","journal-title":"Ceramics International"},{"issue":"3","key":"2089_CR39","doi-asserted-by":"publisher","first-page":"356","DOI":"10.13394\/j.cnki.jgszz.2021.0608","volume":"42","author":"JL Wang","year":"2022","unstructured":"Wang, J. L., Li, J. W., Tian, Y. B., Liu, Y. H., & Zhang, K. (2022a). Methods of grinding power signal acquisition and dynamic power monitoring database establishment. Diamond and Abrasives Engineering, 42(3), 356\u2013363. https:\/\/doi.org\/10.13394\/j.cnki.jgszz.2021.0608","journal-title":"Diamond and Abrasives Engineering"},{"key":"2089_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jclepro.2021.129479","volume":"327","author":"JL Wang","year":"2021","unstructured":"Wang, J. L., Tian, Y. B., Hu, X. T., Li, Y., Zhang, K., & Liu, Y. H. (2021). Predictive modelling and Pareto optimization for energy efficient grinding based on aANN-embedded NSGA II algorithm. Journal of Cleaner Production, 327, 1\u201314. https:\/\/doi.org\/10.1016\/j.jclepro.2021.129479","journal-title":"Journal of Cleaner Production"},{"key":"2089_CR40","doi-asserted-by":"publisher","first-page":"945","DOI":"10.1016\/j.jmapro.2022.07.055","volume":"81","author":"S Wang","year":"2022","unstructured":"Wang, S., Zhao, Q. L., & Wu, T. (2022b). An investigation of monitoring the damage mechanism in ultra-precision grinding of monocrystalline silicon based on AE signals processing. Journal of Manufacturing Processes, 81, 945\u2013961. https:\/\/doi.org\/10.1016\/j.jmapro.2022.07.055","journal-title":"Journal of Manufacturing Processes"},{"key":"2089_CR41","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1007\/s10845-020-01559-0","volume":"32","author":"LH Xu","year":"2021","unstructured":"Xu, L. H., Huang, C. Z., Li, C. W., Wang, J., Liu, H. L., & Wang, X. D. (2021). Estimation of tool wear and optimization of cutting parameters based on novel ANFIS\u2013PSO method toward intelligent machining. Journal of Intelligent Manufacturing, 32, 77\u201390. https:\/\/doi.org\/10.1007\/s10845-020-01559-0","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2089_CR42","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/j.jmsy.2022.11.015","volume":"66","author":"C Zhang","year":"2023","unstructured":"Zhang, C., Zhou, G. H., Li, J. J., Chang, F. T., Ding, K., & Ma, D. X. (2023). A multi-access edge computing enabled framework for the construction of a knowledge-sharing intelligent machine tool swarm in Industry 4.0. Journal of Manufacturing Systems, 66, 56\u201370. https:\/\/doi.org\/10.1016\/j.jmsy.2022.11.015","journal-title":"Journal of Manufacturing Systems"},{"issue":"1","key":"2089_CR43","doi-asserted-by":"publisher","first-page":"71","DOI":"10.13394\/j.cnki.jgszz.2021.1.0012","volume":"41","author":"K Zhang","year":"2021","unstructured":"Zhang, K., Tian, Y. B., Cong, J. C., Liu, Y. H., Yan, N., & Lu, T. (2021). Reduction grinding energy consumption by modified particle swarm optimization based on dynamic inertia weight. Diamond and Abrasives Engineering, 41(1), 71\u201375. https:\/\/doi.org\/10.13394\/j.cnki.jgszz.2021.1.0012","journal-title":"Diamond and Abrasives Engineering"},{"key":"2089_CR44","doi-asserted-by":"publisher","first-page":"802","DOI":"10.1016\/j.apsusc.2019.06.019","volume":"489","author":"WX Zhao","year":"2019","unstructured":"Zhao, W. X., Wang, Y. H., Liang, Z. Q., Zhou, T. F., Wang, X. B., Lin, H., Zhong, J., & Luan, X. S. (2019). Research on ground surface characteristics of prism-plane sapphire under the orthogonal grinding direction. Applied Surface Science, 489, 802\u2013814. https:\/\/doi.org\/10.1016\/j.apsusc.2019.06.019","journal-title":"Applied Surface Science"},{"key":"2089_CR45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ijmecsci.2022.107125","volume":"219","author":"X Zhao","year":"2022","unstructured":"Zhao, X., Zheng, L. Y., Wang, Y. H., & Zhang, Y. H. (2022). Services-oriented intelligent milling for thin-walled parts based on time-varying information model of machining system. International Journal of Mechanical Sciences, 219, 1\u201318. https:\/\/doi.org\/10.1016\/j.ijmecsci.2022.107125","journal-title":"International Journal of Mechanical Sciences"},{"issue":"6","key":"2089_CR46","doi-asserted-by":"publisher","first-page":"4007","DOI":"10.1109\/TII.2019.2957107","volume":"16","author":"KP Zhu","year":"2020","unstructured":"Zhu, K. P., Li, G. C., & Zhang, Y. (2020). Big data oriented smart tool condition monitoring system. IEEE Transactions on Industrial Informatics, 16(6), 4007\u20134016. https:\/\/doi.org\/10.1109\/TII.2019.2957107","journal-title":"IEEE Transactions on Industrial Informatics"}],"container-title":["Journal of Intelligent Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-023-02089-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10845-023-02089-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-023-02089-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,28]],"date-time":"2024-02-28T19:04:59Z","timestamp":1709147099000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10845-023-02089-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,26]]},"references-count":46,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["2089"],"URL":"https:\/\/doi.org\/10.1007\/s10845-023-02089-1","relation":{},"ISSN":["0956-5515","1572-8145"],"issn-type":[{"value":"0956-5515","type":"print"},{"value":"1572-8145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,26]]},"assertion":[{"value":"31 May 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 January 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 February 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}