{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T21:16:46Z","timestamp":1775337406366,"version":"3.50.1"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T00:00:00Z","timestamp":1736208000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T00:00:00Z","timestamp":1736208000000},"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":["52188102"],"award-info":[{"award-number":["52188102"]}],"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":["52122512"],"award-info":[{"award-number":["52122512"]}],"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":["52375496"],"award-info":[{"award-number":["52375496"]}],"id":[{"id":"10.13039\/501100001809","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":[[2026,1]]},"DOI":"10.1007\/s10845-024-02555-4","type":"journal-article","created":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T04:37:41Z","timestamp":1736224661000},"page":"481-503","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Mechanism and data hybrid-driven cutting forces prediction model for end milling"],"prefix":"10.1007","volume":"37","author":[{"given":"Chang","family":"Ni","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9631-2046","authenticated-orcid":false,"given":"Jixiang","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Han","family":"Ding","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,7]]},"reference":[{"key":"2555_CR1","doi-asserted-by":"publisher","first-page":"109756","DOI":"10.1016\/j.asoc.2022.109756","volume":"131","author":"MN Al-Andoli","year":"2022","unstructured":"Al-Andoli, M. N., Tan, S. C., Sim, K. S., Lim, C. P., & Goh, P. Y. (2022). Parallel Deep Learning with a hybrid BP-PSO framework for feature extraction and malware classification. Applied Soft Computing, 131, 109756. https:\/\/doi.org\/10.1016\/j.asoc.2022.109756","journal-title":"Applied Soft Computing"},{"key":"2555_CR2","doi-asserted-by":"publisher","first-page":"993","DOI":"10.1016\/j.ijmachtools.2004.11.028","volume":"45","author":"A Albrecht","year":"2005","unstructured":"Albrecht, A., Park, S. S., Altintas, Y., & Pritschow, G. (2005). High frequency bandwidth cutting force measurement in milling using capacitance displacement sensors. International Journal of Machine Tools and Manufacture, 45, 993\u20131008. https:\/\/doi.org\/10.1016\/j.ijmachtools.2004.11.028","journal-title":"International Journal of Machine Tools and Manufacture"},{"key":"2555_CR3","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1115\/1.2900688","volume":"114","author":"Y Altintas","year":"1992","unstructured":"Altintas, Y. (1992). Prediction of cutting forces and tool breakage in milling from feed drive current measurements. Journal of Engineering for Industry, 114, 386\u2013392. https:\/\/doi.org\/10.1115\/1.2900688","journal-title":"Journal of Engineering for Industry"},{"key":"2555_CR4","volume-title":"Manufacturing automation: Metal cutting mechanics, machine tool vibrations, and CNC design","author":"Y Altintas","year":"2000","unstructured":"Altintas, Y. (2000). Manufacturing automation: Metal cutting mechanics, machine tool vibrations, and CNC design. Cambridge University Press."},{"key":"2555_CR5","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1016\/S0007-8506(07)60703-3","volume":"53","author":"Y Altintas","year":"2004","unstructured":"Altintas, Y., & Park, S. S. (2004). Dynamic compensation of spindle-integrated force sensors. CIRP Annals, 53, 305\u2013308. https:\/\/doi.org\/10.1016\/S0007-8506(07)60703-3","journal-title":"CIRP Annals"},{"key":"2555_CR6","doi-asserted-by":"publisher","first-page":"107461","DOI":"10.1016\/j.measurement.2019.107461","volume":"154","author":"Q An","year":"2020","unstructured":"An, Q., Tao, Z., Xu, X., El Mansori, M., & Chen, M. (2020). A data-driven model for milling tool remaining useful life prediction with convolutional and stacked LSTM network. Measurement, 154, 107461. https:\/\/doi.org\/10.1016\/j.measurement.2019.107461","journal-title":"Measurement"},{"key":"2555_CR7","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.ijmachtools.2018.04.007","volume":"132","author":"D Aslan","year":"2018","unstructured":"Aslan, D., & Altintas, Y. (2018a). On-line chatter detection in milling using drive motor current commands extracted from CNC. International Journal of Machine Tools and Manufacture, 132, 64\u201380. https:\/\/doi.org\/10.1016\/j.ijmachtools.2018.04.007","journal-title":"International Journal of Machine Tools and Manufacture"},{"key":"2555_CR8","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1109\/TMECH.2018.2804859","volume":"23","author":"D Aslan","year":"2018","unstructured":"Aslan, D., & Altintas, Y. (2018b). Prediction of cutting forces in five-axis milling using feed drive current measurements. IEEE\/ASME Transactions on Mechatronics, 23, 833\u2013844. https:\/\/doi.org\/10.1109\/TMECH.2018.2804859","journal-title":"IEEE\/ASME Transactions on Mechatronics"},{"key":"2555_CR9","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1016\/j.jmsy.2017.04.011","volume":"44","author":"P Blaser","year":"2017","unstructured":"Blaser, P., Pavli\u010dek, F., Mori, K., Mayr, J., Weikert, S., & Wegener, K. (2017). Adaptive learning control for thermal error compensation of 5-axis machine tools. Journal of Manufacturing Systems, 44, 302\u2013309. https:\/\/doi.org\/10.1016\/j.jmsy.2017.04.011","journal-title":"Journal of Manufacturing Systems"},{"key":"2555_CR10","doi-asserted-by":"publisher","first-page":"2235","DOI":"10.1109\/TMECH.2021.3100719","volume":"27","author":"Y Cheng","year":"2022","unstructured":"Cheng, Y., Li, Y., Liu, X., & Cai, Y. (2022). Mechanism-based structured deep neural network for cutting force forecasting using CNC inherent monitoring signals. IEEE\/ASME Transactions on Mechatronics, 27, 2235\u20132245. https:\/\/doi.org\/10.1109\/TMECH.2021.3100719","journal-title":"IEEE\/ASME Transactions on Mechatronics"},{"key":"2555_CR11","doi-asserted-by":"publisher","first-page":"986","DOI":"10.1016\/j.ijmachtools.2010.07.004","volume":"50","author":"J-W Dang","year":"2010","unstructured":"Dang, J.-W., Zhang, W.-H., Yang, Y., & Wan, M. (2010). Cutting force modeling for flat end milling including bottom edge cutting effect. International Journal of Machine Tools and Manufacture, 50, 986\u2013997. https:\/\/doi.org\/10.1016\/j.ijmachtools.2010.07.004","journal-title":"International Journal of Machine Tools and Manufacture"},{"key":"2555_CR12","doi-asserted-by":"publisher","first-page":"2143","DOI":"10.1109\/TMECH.2020.3022983","volume":"25","author":"H Ding","year":"2020","unstructured":"Ding, H., Gao, R. X., Isaksson, A. J., Landers, R. G., Parisini, T., & Yuan, Y. (2020). State of AI-based monitoring in smart manufacturing and introduction to focused section. IEEE\/ASME Transactions on Mechatronics, 25, 2143\u20132154. https:\/\/doi.org\/10.1109\/TMECH.2020.3022983","journal-title":"IEEE\/ASME Transactions on Mechatronics"},{"issue":"10","key":"2555_CR13","doi-asserted-by":"publisher","first-page":"1487","DOI":"10.1016\/S0890-6955(01)00003-7","volume":"41","author":"K Erkorkmaz","year":"2001","unstructured":"Erkorkmaz, K., & Altintas, Y. (2001). High speed CNC system design. Part II: Modeling and identification of feed drives. International Journal of Machine Tools and Manufacture, 41(10), 1487\u20131509.","journal-title":"International Journal of Machine Tools and Manufacture"},{"key":"2555_CR14","doi-asserted-by":"publisher","first-page":"1013","DOI":"10.1016\/j.engappai.2007.10.001","volume":"21","author":"JM Fines","year":"2008","unstructured":"Fines, J. M., & Agah, A. (2008). Machine tool positioning error compensation using artificial neural networks. Engineering Applications of Artificial Intelligence, 21, 1013\u20131026. https:\/\/doi.org\/10.1016\/j.engappai.2007.10.001","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"2555_CR15","doi-asserted-by":"publisher","first-page":"1293","DOI":"10.1007\/s00170-022-09951-1","volume":"122","author":"R Guo","year":"2022","unstructured":"Guo, R., Chen, M., Wang, G., & Zhou, X. (2022). Milling force prediction and optimization of process parameters in micro-milling of glow discharge polymer. International Journal of Advanced Manufacturing Technology, 122, 1293\u20131310. https:\/\/doi.org\/10.1007\/s00170-022-09951-1","journal-title":"International Journal of Advanced Manufacturing Technology"},{"key":"2555_CR16","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9, 1735\u20131780. https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Computation"},{"key":"2555_CR17","doi-asserted-by":"publisher","first-page":"1377","DOI":"10.1109\/TMECH.2020.2975343","volume":"25","author":"C Hu","year":"2020","unstructured":"Hu, C., Ou, T., Chang, H., Yu, Z., & Zhu, L. (2020). Deep GRU neural-network prediction and feedforward compensation for precision multi-axis motion control systems. IEEE\/ASME Transactions on Mechatronics, 25, 1377\u20131388. https:\/\/doi.org\/10.1109\/TMECH.2020.2975343","journal-title":"IEEE\/ASME Transactions on Mechatronics"},{"key":"2555_CR18","doi-asserted-by":"publisher","first-page":"2471","DOI":"10.1007\/s10845-021-01792-1","volume":"33","author":"M Ismail","year":"2022","unstructured":"Ismail, M., Mostafa, N. A., & El-assal, A. (2022). Quality monitoring in multistage manufacturing systems by using machine learning techniques. Journal of Intelligent Manufacturing, 33, 2471\u20132486. https:\/\/doi.org\/10.1007\/s10845-021-01792-1","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2555_CR19","doi-asserted-by":"publisher","first-page":"1813","DOI":"10.1007\/s10845-021-01764-5","volume":"33","author":"Y Kim","year":"2022","unstructured":"Kim, Y., Kim, T., Youn, B. D., & Ahn, S.-H. (2022). Machining quality monitoring (MQM) in laser-assisted micro-milling of glass using cutting force signals: An image-based deep transfer learning. Journal of Intelligent Manufacturing, 33, 1813\u20131828. https:\/\/doi.org\/10.1007\/s10845-021-01764-5","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2555_CR20","doi-asserted-by":"publisher","unstructured":"Kingma DP, Ba J. Adam: A Method for Stochastic Optimization. In: Proceedings of the 3rd international conference on learning representations, arXiv; 2015. https:\/\/doi.org\/10.48550\/arXiv.1412.6980.","DOI":"10.48550\/arXiv.1412.6980"},{"key":"2555_CR21","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1007\/s41871-023-00193-7","volume":"6","author":"H Kwak","year":"2023","unstructured":"Kwak, H., & Kim, J. (2023). Semiconductor multilayer nanometrology with machine learning. Nanomanuf Metrol, 6, 15. https:\/\/doi.org\/10.1007\/s41871-023-00193-7","journal-title":"Nanomanuf Metrol"},{"key":"2555_CR22","doi-asserted-by":"publisher","first-page":"2547","DOI":"10.1007\/s10845-023-02164-7","volume":"35","author":"B Li","year":"2024","unstructured":"Li, B. (2024). Tool wear prediction in milling CFRP with different fiber orientations based on multi-channel 1DCNN-LSTM. Journal of Intelligent Manufacturing, 35, 2547\u20132566.","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2555_CR23","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1007\/s41871-023-00206-5","volume":"6","author":"K Li","year":"2023","unstructured":"Li, K., Zhang, Z., Lin, J., Sato, R., Matsukuma, H., & Gao, W. (2023). Angle measurement based on second harmonic generation using artificial neural network. Nanomanuf Metrol, 6, 28. https:\/\/doi.org\/10.1007\/s41871-023-00206-5","journal-title":"Nanomanuf Metrol"},{"key":"2555_CR24","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1016\/j.amc.2019.01.038","volume":"352","author":"Y Liu","year":"2019","unstructured":"Liu, Y., Wang, X., Wang, L., & Liu, D. (2019). A modified leaky ReLU scheme (MLRS) for topology optimization with multiple materials. Applied Mathematics and Computation, 352, 188\u2013204. https:\/\/doi.org\/10.1016\/j.amc.2019.01.038","journal-title":"Applied Mathematics and Computation"},{"key":"2555_CR25","doi-asserted-by":"publisher","first-page":"2517","DOI":"10.1007\/s10845-023-02172-7","volume":"35","author":"S Liu","year":"2024","unstructured":"Liu, S., Zheng, P., & Bao, J. (2024). Digital twin-based manufacturing system: A survey based on a novel reference model. Journal of Intelligent Manufacturing, 35, 2517\u20132546. https:\/\/doi.org\/10.1007\/s10845-023-02172-7","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2555_CR26","doi-asserted-by":"publisher","first-page":"967","DOI":"10.1007\/s10845-019-01489-6","volume":"31","author":"H Ma","year":"2020","unstructured":"Ma, H., Liu, W., Zhou, X., Niu, Q., & Kong, C. (2020). An effective and automatic approach for parameters optimization of complex end milling process based on virtual machining. Journal of Intelligent Manufacturing, 31, 967\u2013984. https:\/\/doi.org\/10.1007\/s10845-019-01489-6","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2555_CR27","doi-asserted-by":"publisher","first-page":"114693","DOI":"10.1016\/j.eswa.2021.114693","volume":"173","author":"L Mou","year":"2021","unstructured":"Mou, L., Zhou, C., Zhao, P., Nakisa, B., Rastgoo, M. N., Jain, R., et al. (2021). Driver stress detection via multimodal fusion using attention-based CNN-LSTM. Expert Systems with Applications, 173, 114693. https:\/\/doi.org\/10.1016\/j.eswa.2021.114693","journal-title":"Expert Systems with Applications"},{"key":"2555_CR28","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1115\/1.1789531","volume":"126","author":"SS Park","year":"2004","unstructured":"Park, S. S., & Altintas, Y. (2004). Dynamic compensation of spindle integrated force sensors with kalman filter. Journal of Dynamic Systems, Measurement, and Control, 126, 443\u2013452. https:\/\/doi.org\/10.1115\/1.1789531","journal-title":"Journal of Dynamic Systems, Measurement, and Control"},{"key":"2555_CR29","doi-asserted-by":"publisher","first-page":"2079","DOI":"10.1007\/s10845-022-01923-2","volume":"34","author":"DY Pimenov","year":"2023","unstructured":"Pimenov, D. Y., Bustillo, A., Wojciechowski, S., Sharma, V. S., Gupta, M. K., & Kunto\u011flu, M. (2023). Artificial intelligence systems for tool condition monitoring in machining: Analysis and critical review. Journal of Intelligent Manufacturing, 34, 2079\u20132121. https:\/\/doi.org\/10.1007\/s10845-022-01923-2","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2555_CR30","doi-asserted-by":"publisher","first-page":"413","DOI":"10.1016\/j.cirp.2019.03.019","volume":"68","author":"M Postel","year":"2019","unstructured":"Postel, M., Aslan, D., Wegener, K., & Altintas, Y. (2019). Monitoring of vibrations and cutting forces with spindle mounted vibration sensors. CIRP Annals, 68, 413\u2013416. https:\/\/doi.org\/10.1016\/j.cirp.2019.03.019","journal-title":"CIRP Annals"},{"key":"2555_CR31","doi-asserted-by":"publisher","first-page":"686","DOI":"10.1016\/j.jcp.2018.10.045","volume":"378","author":"M Raissi","year":"2019","unstructured":"Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2019). Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics, 378, 686\u2013707. https:\/\/doi.org\/10.1016\/j.jcp.2018.10.045","journal-title":"Journal of Computational Physics"},{"key":"2555_CR32","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1007\/s10845-023-02074-8","volume":"35","author":"NS Ross","year":"2024","unstructured":"Ross, N. S., Sheeba, P. T., Shibi, C. S., Gupta, M. K., Korkmaz, M. E., & Sharma, V. S. (2024). A novel approach of tool condition monitoring in sustainable machining of Ni alloy with transfer learning models. Journal of Intelligent Manufacturing, 35, 757\u2013775. https:\/\/doi.org\/10.1007\/s10845-023-02074-8","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2555_CR33","doi-asserted-by":"publisher","first-page":"1064","DOI":"10.1016\/j.jmapro.2022.10.064","volume":"84","author":"A Schueller","year":"2022","unstructured":"Schueller, A., & Salda\u00f1a, C. (2022). Generalizability analysis of tool condition monitoring ensemble machine learning models. Journal of Manufacturing Processes, 84, 1064\u20131075. https:\/\/doi.org\/10.1016\/j.jmapro.2022.10.064","journal-title":"Journal of Manufacturing Processes"},{"key":"2555_CR34","doi-asserted-by":"publisher","first-page":"1879","DOI":"10.1007\/s10845-022-01963-8","volume":"33","author":"H Tercan","year":"2022","unstructured":"Tercan, H., & Meisen, T. (2022). Machine learning and deep learning based predictive quality in manufacturing: A systematic review. Journal of Intelligent Manufacturing, 33, 1879\u20131905. https:\/\/doi.org\/10.1007\/s10845-022-01963-8","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2555_CR35","doi-asserted-by":"publisher","first-page":"717","DOI":"10.1016\/j.cirp.2010.05.010","volume":"59","author":"R Teti","year":"2010","unstructured":"Teti, R., Jemielniak, K., O\u2019Donnell, G., & Dornfeld, D. (2010). Advanced monitoring of machining operations. CIRP Annals, 59, 717\u2013739. https:\/\/doi.org\/10.1016\/j.cirp.2010.05.010","journal-title":"CIRP Annals"},{"key":"2555_CR36","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/j.neucom.2022.04.127","volume":"497","author":"Y Tian","year":"2022","unstructured":"Tian, Y., Su, D., Lauria, S., & Liu, X. (2022). Recent advances on loss functions in deep learning for computer vision. Neurocomputing, 497, 129\u2013158. https:\/\/doi.org\/10.1016\/j.neucom.2022.04.127","journal-title":"Neurocomputing"},{"key":"2555_CR37","doi-asserted-by":"publisher","first-page":"108412","DOI":"10.1016\/j.ymssp.2021.108412","volume":"166","author":"G Totis","year":"2022","unstructured":"Totis, G., & Sortino, M. (2022). Upgraded regularized deconvolution of complex dynamometer dynamics for an improved correction of cutting forces in milling. Mechanical Systems and Signal Processing, 166, 108412. https:\/\/doi.org\/10.1016\/j.ymssp.2021.108412","journal-title":"Mechanical Systems and Signal Processing"},{"key":"2555_CR38","doi-asserted-by":"publisher","first-page":"1353","DOI":"10.1007\/s10845-019-01514-8","volume":"31","author":"S Vaishnav","year":"2020","unstructured":"Vaishnav, S., Agarwal, A., & Desai, K. A. (2020). Machine learning-based instantaneous cutting force model for end milling operation. Journal of Intelligent Manufacturing, 31, 1353\u20131366. https:\/\/doi.org\/10.1007\/s10845-019-01514-8","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2555_CR39","doi-asserted-by":"publisher","first-page":"947","DOI":"10.1007\/s10845-020-01595-w","volume":"32","author":"J Wang","year":"2021","unstructured":"Wang, J. (2021). Milling force prediction model based on transfer learning and neural network. Journal of Intelligent Manufacturing, 32, 947\u2013956.","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2555_CR40","doi-asserted-by":"publisher","first-page":"101309","DOI":"10.1016\/j.swevo.2023.101309","volume":"79","author":"C Wang","year":"2023","unstructured":"Wang, C., Wang, Z., Zhang, S., & Tan, J. (2023). Adam-assisted quantum particle swarm optimization guided by length of potential well for numerical function optimization. Swarm and Evolutionary Computation, 79, 101309. https:\/\/doi.org\/10.1016\/j.swevo.2023.101309","journal-title":"Swarm and Evolutionary Computation"},{"key":"2555_CR41","doi-asserted-by":"publisher","first-page":"2475","DOI":"10.1007\/s10845-023-02176-3","volume":"35","author":"C Wang","year":"2024","unstructured":"Wang, C., Sun, Y., & Wang, X. (2024). Image deep learning in fault diagnosis of mechanical equipment. Journal of Intelligent Manufacturing, 35, 2475\u20132515. https:\/\/doi.org\/10.1007\/s10845-023-02176-3","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2555_CR42","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1016\/j.tics.2018.12.005","volume":"23","author":"JCR Whittington","year":"2019","unstructured":"Whittington, J. C. R., & Bogacz, R. (2019). Theories of error back-propagation in the brain. Trends in Cognitive Sciences, 23, 235\u2013250. https:\/\/doi.org\/10.1016\/j.tics.2018.12.005","journal-title":"Trends in Cognitive Sciences"},{"key":"2555_CR43","doi-asserted-by":"publisher","first-page":"108153","DOI":"10.1016\/j.ijmecsci.2023.108153","volume":"246","author":"J Xie","year":"2023","unstructured":"Xie, J., Hu, P., Chen, J., Han, W., & Wang, R. (2023). Deep learning-based instantaneous cutting force modeling of three-axis CNC milling. International Journal of Mechanical Sciences, 246, 108153. https:\/\/doi.org\/10.1016\/j.ijmecsci.2023.108153","journal-title":"International Journal of Mechanical Sciences"},{"key":"2555_CR44","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1186\/s10033-020-00459-x","volume":"33","author":"K Xu","year":"2020","unstructured":"Xu, K., Li, Y., Liu, C., Liu, X., Hao, X., Gao, J., et al. (2020). Advanced data collection and analysis in data-driven manufacturing process. Chin J Mech Eng, 33, 43. https:\/\/doi.org\/10.1186\/s10033-020-00459-x","journal-title":"Chin J Mech Eng"},{"key":"2555_CR45","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.patrec.2018.05.018","volume":"118","author":"G Yao","year":"2019","unstructured":"Yao, G., Lei, T., & Zhong, J. (2019). A review of convolutional-neural-network-based action recognition. Pattern Recognition Letters, 118, 14\u201322. https:\/\/doi.org\/10.1016\/j.patrec.2018.05.018","journal-title":"Pattern Recognition Letters"},{"key":"2555_CR46","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1007\/s10845-018-1442-7","volume":"31","author":"O Yousefian","year":"2020","unstructured":"Yousefian, O., Balabokhin, A., & Tarbutton, J. (2020). Point-by-point prediction of cutting force in 3-axis CNC milling machines through voxel framework in digital manufacturing. Journal of Intelligent Manufacturing, 31, 215\u2013226. https:\/\/doi.org\/10.1007\/s10845-018-1442-7","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2555_CR47","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1016\/j.ijmecsci.2017.12.019","volume":"136","author":"X Zhang","year":"2018","unstructured":"Zhang, X., Yu, T., & Wang, W. (2018). Prediction of cutting forces and instantaneous tool deflection in micro end milling by considering tool run-out. International Journal of Mechanical Sciences, 136, 124\u2013133. https:\/\/doi.org\/10.1016\/j.ijmecsci.2017.12.019","journal-title":"International Journal of Mechanical Sciences"},{"key":"2555_CR48","doi-asserted-by":"publisher","first-page":"121692","DOI":"10.1016\/j.eswa.2023.121692","volume":"237","author":"S Zhang","year":"2024","unstructured":"Zhang, S., Yang, Y., Chen, C., Zhang, X., Leng, Q., & Zhao, X. (2024). Deep learning-based multimodal emotion recognition from audio, visual, and text modalities: A systematic review of recent advancements and future prospects. Expert Systems with Applications, 237, 121692. https:\/\/doi.org\/10.1016\/j.eswa.2023.121692","journal-title":"Expert Systems with Applications"},{"key":"2555_CR49","doi-asserted-by":"publisher","first-page":"102796","DOI":"10.1016\/j.rcim.2024.102796","volume":"90","author":"X Zhu","year":"2024","unstructured":"Zhu, X., Chen, G., Ni, C., Lu, X., & Guo, J. (2024). Hybrid CNN-LSTM model driven image segmentation and roughness prediction for tool condition assessment with heterogeneous data. Robotics and Computer-Integrated Manufacturing, 90, 102796. https:\/\/doi.org\/10.1016\/j.rcim.2024.102796","journal-title":"Robotics and Computer-Integrated Manufacturing"}],"container-title":["Journal of Intelligent Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-024-02555-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10845-024-02555-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-024-02555-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T09:35:12Z","timestamp":1769852112000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10845-024-02555-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,7]]},"references-count":49,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["2555"],"URL":"https:\/\/doi.org\/10.1007\/s10845-024-02555-4","relation":{},"ISSN":["0956-5515","1572-8145"],"issn-type":[{"value":"0956-5515","type":"print"},{"value":"1572-8145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,7]]},"assertion":[{"value":"20 July 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 January 2025","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 conflicts of interest to this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}