{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T18:51:56Z","timestamp":1781290316969,"version":"3.54.1"},"reference-count":70,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,15]],"date-time":"2024-08-15T00:00:00Z","timestamp":1723680000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Tool condition monitoring technology is an indispensable part of intelligent manufacturing. Most current research focuses on complex signal processing techniques or advanced deep learning algorithms to improve prediction performance without fully leveraging the end-to-end advantages of deep learning. The challenge lies in transforming multi-sensor raw data into input data suitable for direct model feeding, all while minimizing data scale and preserving sufficient temporal interpretation of tool wear. However, there is no clear reference standard for this so far. In light of this, this paper innovatively explores the processing methods that transform raw data into input data for deep learning models, a process known as an input paradigm. This paper introduces three new input paradigms: the downsampling paradigm, the periodic paradigm, and the subsequence paradigm. Then an improved hybrid model that combines a convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) was employed to validate the model\u2019s performance. The subsequence paradigm demonstrated considerable superiority in prediction results based on the PHM2010 dataset, as the newly generated time series maintained the integrity of the raw data. Further investigation revealed that, with 120 subsequences and the temporal indicator being the maximum value, the model\u2019s mean absolute error (MAE) and root mean square error (RMSE) were the lowest after threefold cross-validation, outperforming several classical and contemporary methods. The methods explored in this paper provide references for designing input data for deep learning models, helping to enhance the end-to-end potential of deep learning models, and promoting the industrial deployment and practical application of tool condition monitoring systems.<\/jats:p>","DOI":"10.3390\/s24165300","type":"journal-article","created":{"date-parts":[[2024,8,16]],"date-time":"2024-08-16T04:29:57Z","timestamp":1723782597000},"page":"5300","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Exploring the Processing Paradigm of Input Data for End-to-End Deep Learning in Tool Condition Monitoring"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5787-7001","authenticated-orcid":false,"given":"Chengguan","family":"Wang","sequence":"first","affiliation":[{"name":"Institute of Intelligent Manufacturing Technology, Shenzhen Polytechnic University, Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangping","family":"Wang","sequence":"additional","affiliation":[{"name":"AVIC Changhe Aircraft Industry (Group) Corporation Ltd., Jingdezhen 333002, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Ultrasonic Technology, Institute of Intelligent Manufacturing Technology, Shenzhen Polytechnic University, Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiyao","family":"Xiong","sequence":"additional","affiliation":[{"name":"AVIC Changhe Aircraft Industry (Group) Corporation Ltd., Jingdezhen 333002, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongchuan","family":"Ouyang","sequence":"additional","affiliation":[{"name":"AVIC Changhe Aircraft Industry (Group) Corporation Ltd., Jingdezhen 333002, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Gong","sequence":"additional","affiliation":[{"name":"Institute of Intelligent Manufacturing Technology, Shenzhen Polytechnic University, Shenzhen 518055, China"},{"name":"Institute of Ultrasonic Technology, Institute of Intelligent Manufacturing Technology, Shenzhen Polytechnic University, Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,15]]},"reference":[{"key":"ref_1","first-page":"012039","article-title":"Deep learning algorithms for tool condition monitoring in milling: A review","volume":"1969","author":"Patil","year":"2021","journal-title":"Int. J. Phys."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Munaro, R., Attanasio, A., and Del Prete, A. (2023). Tool Wear Monitoring with Artificial Intelligence Methods: A Review. J. Manuf. Mater. Proc., 7.","DOI":"10.3390\/jmmp7040129"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"118548","DOI":"10.1016\/j.eswa.2022.118548","article-title":"A hybrid attention-based paralleled deep learning model for tool wear prediction","volume":"211","author":"Duan","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"953","DOI":"10.1007\/s00170-020-05449-w","article-title":"Review of tool condition monitoring in machining and opportunities for deep learning","volume":"109","author":"Serin","year":"2020","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1497","DOI":"10.1007\/s10845-019-01526-4","article-title":"A hybrid information model based on long short-term memory network for tool condition monitoring","volume":"31","author":"Cai","year":"2020","journal-title":"J. Intell. Manuf."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Chen, Q., Xie, Q., Yuan, Q., Huang, H., and Li, Y. (2019). Research on a real-time monitoring method for the wear state of a tool based on a convolutional bidirectional LSTM model. Symmetry, 11.","DOI":"10.3390\/sym11101233"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1687814020919209","DOI":"10.1177\/1687814020919209","article-title":"Indirect online tool wear monitoring and model-based identification of process-related signal","volume":"12","author":"Stavropoulos","year":"2020","journal-title":"Adv. Mech. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"102368","DOI":"10.1016\/j.rcim.2022.102368","article-title":"Intelligent tool wear prediction based on Informer encoder and stacked bidirectional gated recurrent unit","volume":"77","author":"Li","year":"2022","journal-title":"Robot. Comput. Integr. Manuf."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/j.cirpj.2024.05.001","article-title":"Sensors for in-process and on-machine monitoring of machining operations","volume":"51","author":"Shokrani","year":"2024","journal-title":"CIRP J. Manuf. Sci. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"117758","DOI":"10.1016\/j.eswa.2022.117758","article-title":"Modeling and tagging of time sequence signals in the milling process based on an improved hidden semi-Markov model","volume":"205","author":"Li","year":"2022","journal-title":"Expert. Syst. Appl."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"738","DOI":"10.1016\/j.precisioneng.2021.07.019","article-title":"Identification of tool wear using acoustic emission signal and machine learning methods","volume":"72","author":"Twardowski","year":"2021","journal-title":"Precis. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"105601","DOI":"10.1088\/1361-6501\/ac750f","article-title":"A dual compensation strategy based on multi-model support vector regression for tool wear monitoring","volume":"33","author":"Zhang","year":"2022","journal-title":"Meas. Sci. Technol."},{"key":"ref_13","first-page":"1","article-title":"Identification of tool life stages and redressing criterion for PCD micro-grinding tools using a machine learning approach","volume":"145","author":"Pratap","year":"2022","journal-title":"J. Manuf. Sci. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2209","DOI":"10.1007\/s00170-022-10342-9","article-title":"Tool wear state recognition based on WOA-SVM with statistical feature fusion of multi-signal singularity","volume":"123","author":"Gai","year":"2022","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"111501","DOI":"10.1016\/j.measurement.2022.111501","article-title":"Multi-sensor heterogeneous data-based online tool health monitoring in milling of IN718 superalloy using OGM (1, N) model and SVM","volume":"199","author":"Babu","year":"2022","journal-title":"Measurement"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Cheng, Y.N., Jin, Y.B., Gai, X.Y., Guan, R., and Lu, M.D. (2023). Prediction of tool wear in milling process based on BP neural network optimized by firefly algorithm. Proc. Inst. Mech. Eng. E J. Process Mech. Eng.","DOI":"10.1177\/09544089231160492"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.precisioneng.2020.09.025","article-title":"Tool wear monitoring in micromilling using support vector machine with vibration and sound sensors","volume":"67","author":"Gomes","year":"2021","journal-title":"Precis. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1007\/s00170-022-10701-6","article-title":"Tool wear classification in milling for varied cutting conditions: With emphasis on data pre-processing","volume":"125","author":"Li","year":"2023","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1721","DOI":"10.1007\/s42417-022-00478-z","article-title":"Na\u00efve Bayes and Bayes net classifier for fault diagnosis of end mill tool using wavelet analysis: A comparative study","volume":"10","author":"Dhobale","year":"2022","journal-title":"J. Vib. Eng. Technol."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Papacharalampopoulos, A., Alexopoulos, K., Catti, P., Stavropoulos, P., and Chryssolouris, G. (2024). Learning More with Less Data in Manufacturing: The Case of Turning Tool Wear Assessment through Active and Transfer Learning. Processes, 12.","DOI":"10.3390\/pr12061262"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhao, R., Yan, R., Wang, J., and Mao, K. (2017). Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks. Sensors, 17.","DOI":"10.3390\/s17020273"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"106092","DOI":"10.1016\/j.engappai.2023.106092","article-title":"Performance evaluation of LSTM and Bi-LSTM using non-convolutional features for blockage detection in centrifugal pump","volume":"122","author":"Ranawat","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the dimensionality of data with neural networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"106164","DOI":"10.1016\/j.engfailanal.2022.106164","article-title":"Measurement and prediction of wear volume of the tool in nonlinear degradation process based on multi-sensor information fusion","volume":"136","author":"Gao","year":"2022","journal-title":"Eng. Fail. Anal."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3217","DOI":"10.1007\/s00170-018-2420-0","article-title":"Tool condition monitoring using spectral subtraction and convolutional neural networks in milling process","volume":"98","author":"Aghazadeh","year":"2018","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.ymssp.2018.05.050","article-title":"Deep learning and its applications to machine health monitoring","volume":"115","author":"Zhao","year":"2019","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1071","DOI":"10.1016\/j.procir.2022.05.110","article-title":"ConvLSTM deep learning signal prediction for forecasting bending moment for tool condition monitoring","volume":"107","author":"Hall","year":"2022","journal-title":"Procedia CIRP"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2532","DOI":"10.1177\/09544089221142161","article-title":"An industrially viable wavelet long-short term memory-deep multilayer perceptron-based approach to tool condition monitoring considering operational variability","volume":"237","author":"Gudelek","year":"2023","journal-title":"Proc. Inst. Mech. Eng. Part E J. Process Mech. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"4023","DOI":"10.1007\/s00170-022-08861-6","article-title":"Tool wear monitoring for cavity milling based on vibration singularity analysis and stacked LSTM","volume":"120","author":"Ma","year":"2022","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"109434","DOI":"10.1016\/j.ymssp.2022.109434","article-title":"Online damage detection of cutting tools using Dirichlet process mixture models","volume":"180","author":"Wickramarachchi","year":"2022","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ahmed, M., Kamal, K., Ratlamwala, T.A., Hussain, G., Alqahtani, M., Alkahtani, M., Alatefi, M., and Alzabidi, A. (2023). Tool health monitoring of a milling process using acoustic emissions and a ResNet deep learning model. Sensors, 23.","DOI":"10.3390\/s23063084"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Hassan, M., Sadek, A., and Attia, H. (2023). A Real-Time Deep Machine Learning Approach for Sudden Tool Failure Prediction and Prevention in Machining Processes. Sensors, 23.","DOI":"10.3390\/s23083894"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1007\/s00170-022-09356-0","article-title":"Tool wear prediction in face milling of stainless steel using singular generative adversarial network and LSTM deep learning models","volume":"121","author":"Shah","year":"2022","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"108029","DOI":"10.1016\/j.measurement.2020.108029","article-title":"A two-stage approach for predicting the remaining useful life of tools using bidirectional long short-term memory","volume":"164","author":"Liu","year":"2020","journal-title":"Measurement"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"4027","DOI":"10.1007\/s00170-023-10811-9","article-title":"Tool remaining useful life prediction using bidirectional recurrent neural networks (BRNN)","volume":"125","author":"Ferrando","year":"2023","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"953","DOI":"10.1007\/s10845-019-01488-7","article-title":"Tool wear predicting based on multi-domain feature fusion by deep convolutional neural network in milling operations","volume":"31","author":"Huang","year":"2020","journal-title":"J. Intell. Manuf."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3803","DOI":"10.1007\/s00170-021-08448-7","article-title":"A hybrid CNN-BiLSTM approach-based variational mode decomposition for tool wear monitoring","volume":"119","author":"Bazi","year":"2022","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2277","DOI":"10.1007\/s00170-020-06447-8","article-title":"Prediction of the remaining useful life of cutting tool using the Hurst exponent and CNN-LSTM","volume":"112","author":"Zhang","year":"2021","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"6611","DOI":"10.1007\/s00170-022-09784-y","article-title":"Tool wear prediction using long short-term memory variants and hybrid feature selection techniques","volume":"121","author":"Sayyad","year":"2022","journal-title":"Int. J. Adv. Manuf. Tech."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Huang, C.Y., and Chiang, Y.H. (2022). Solutions of feature and Hyperparameter model selection in the intelligent manufacturing. Processes, 10.","DOI":"10.3390\/pr10050862"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.jmsy.2024.01.008","article-title":"Data-model linkage prediction of tool remaining useful life based on deep feature fusion and Wiener process","volume":"73","author":"Li","year":"2024","journal-title":"J. Manuf. Syst."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"608","DOI":"10.1016\/j.jmsy.2021.06.006","article-title":"Intelligent tool wear monitoring based on parallel residual and stacked bidirectional long short-term memory network","volume":"60","author":"Liu","year":"2021","journal-title":"J. Manuf. Syst."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Arias, V.A., Vargas-Machuca, J., Zegarra, F.C., and Coronado, A.M. (2021, January 17\u201319). Convolutional Neural Network Classification for Machine Tool Wear Based on Unsupervised Gaussian Mixture Model. Proceedings of the 2021 IEEE Sciences and Humanities International Research Conference (SHIRCON), Lima, Peru.","DOI":"10.1109\/SHIRCON53068.2021.9652266"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"6707","DOI":"10.1007\/s00170-022-09198-w","article-title":"A novel hybrid model integrating residual structure and bi-directional long short-term memory network for tool wear monitoring","volume":"120","author":"Zhang","year":"2022","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Suawa, P.F., and H\u00fcbner, M. (2022, January 14\u201323). Health monitoring of milling tools under distinct operating conditions by a deep convolutional neural network model. Proceedings of the 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE), Antwerp, Belgium.","DOI":"10.23919\/DATE54114.2022.9774570"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"10879","DOI":"10.1109\/JSEN.2023.3263634","article-title":"A novel sensor-based label-smoothing technique for machine state degradation","volume":"23","author":"Chao","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1007\/s00170-023-11832-0","article-title":"Tool wear prediction based on parallel dual-channel adaptive feature fusion","volume":"128","author":"Yang","year":"2023","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Caggiano, A., Mattera, G., and Nele, L. (2023). Smart tool wear monitoring of CFRP\/CFRP stack drilling using autoencoders and memory-based neural networks. Appl. Sci., 13.","DOI":"10.3390\/app13053307"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Zhao, R., Wang, J., Yan, R., and Mao, K. (2016, January 11\u201313). Machine health monitoring with LSTM networks. Proceedings of the 10th International Conference on Sensing Technology (ICST), Nanjing, China.","DOI":"10.1109\/ICSensT.2016.7796266"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"109329","DOI":"10.1016\/j.measurement.2021.109329","article-title":"Tool wear prediction in high-speed turning of a steel alloy using long short-term memory modelling","volume":"177","author":"Marani","year":"2021","journal-title":"Measurement"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"110922","DOI":"10.1016\/j.asoc.2023.110922","article-title":"Bayesian-based uncertainty-aware tool-wear prediction model in end-milling process of titanium alloy","volume":"148","author":"Kim","year":"2023","journal-title":"Appl. Soft Comput."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Jeon, W.S., and Rhee, S.Y. (2024). Tool Wear Monitoring System Using Seq2Seq. Machines, 12.","DOI":"10.3390\/machines12030169"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"4448","DOI":"10.1007\/s10489-022-03773-0","article-title":"Multisensor-based tool wear diagnosis using 1D-CNN and DGCCA","volume":"53","author":"Yin","year":"2023","journal-title":"Appl. Intell."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"810","DOI":"10.1007\/s11227-021-03903-4","article-title":"Tool wear prediction using convolutional bidirectional LSTM networks","volume":"78","author":"Chan","year":"2022","journal-title":"J. Supercomput."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Nie, L., Zhang, L., Xu, S., Cai, W., and Yang, H. (2022). Remaining useful life prediction of milling cutters based on CNN-BiLSTM and attention mechanism. Symmetry, 14.","DOI":"10.3390\/sym14112243"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2907","DOI":"10.1007\/s00170-024-13909-w","article-title":"An end-to-end deep learning approach for tool wear condition monitoring","volume":"133","author":"Ma","year":"2024","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"80448","DOI":"10.1109\/ACCESS.2023.3299849","article-title":"Research on tool remaining life prediction method based on CNN-LSTM-PSO","volume":"11","author":"Wang","year":"2023","journal-title":"IEEE Access"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"7349","DOI":"10.1109\/TIE.2021.3102443","article-title":"Deep spatial-temporal feature extraction and lightweight feature fusion for tool condition monitoring","volume":"69","author":"Li","year":"2021","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Zegarra, F.C., Vargas-Machuca, J., and Coronado, A.M. (2021, January 27\u201329). Comparison of CNN and CNN-LSTM architectures for tool wear estimation. Proceedings of the 2021 IEEE Engineering International Research Conference (EIRCON), Lima, Peru.","DOI":"10.1109\/EIRCON52903.2021.9613659"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Li, R., Ye, X., Yang, F., and Du, K.L. (2023). ConvLSTM-Att: An attention-based composite deep neural network for tool wear prediction. Machines, 11.","DOI":"10.3390\/machines11020297"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1007\/s13198-022-01777-0","article-title":"Machine learning based fault-oriented predictive maintenance in industry 4.0","volume":"15","author":"Justus","year":"2024","journal-title":"Int. J. Syst. Assur. Eng. Manag."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"2547","DOI":"10.1007\/s10845-023-02164-7","article-title":"Tool wear prediction in milling CFRP with different fiber orientations based on multi-channel 1DCNN-LSTM","volume":"35","author":"Li","year":"2023","journal-title":"J. Intell. Manuf."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"885","DOI":"10.1007\/s10845-022-01954-9","article-title":"An approach for tool wear prediction using customized DenseNet and GRU integrated model based on multi-sensor feature fusion","volume":"34","author":"Liu","year":"2023","journal-title":"J. Intell. Manuf."},{"key":"ref_64","unstructured":"PHM Society (2024, May 30). PHM Society Conference Data Challenge. Available online: https:\/\/data.phmsociety.org\/2021-phm-conference-data-challenge\/."},{"key":"ref_65","unstructured":"(1989). Tool Life Testing in Milling\u2014Part 2: End milling (Standard No. ISO 8688-2:1989(E))."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"4025","DOI":"10.1007\/s00170-022-10455-1","article-title":"Tool wear prediction based on convolutional bidirectional LSTM model with improved particle swarm optimization","volume":"123","author":"Li","year":"2022","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.jmsy.2023.02.006","article-title":"Tool wear identification and prediction method based on stack sparse self-coding network","volume":"68","author":"Qin","year":"2023","journal-title":"J. Manuf. Syst."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compind.2019.06.001","article-title":"Deep heterogeneous GRU model for predictive analytics in smart manufacturing: Application to tool wear prediction","volume":"111","author":"Wang","year":"2019","journal-title":"Comput. Ind."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Qiao, H., Wang, T., Wang, P., Qiao, S., and Zhang, L. (2018). A time-distributed spatiotemporal feature learning method for machine health monitoring with multi-sensor time series. Sensors, 18.","DOI":"10.3390\/s18092932"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/16\/5300\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:37:22Z","timestamp":1760110642000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/16\/5300"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,15]]},"references-count":70,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["s24165300"],"URL":"https:\/\/doi.org\/10.3390\/s24165300","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,15]]}}}