{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T16:35:46Z","timestamp":1792514146921,"version":"4.5.0"},"reference-count":30,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2024,4,21]],"date-time":"2024-04-21T00:00:00Z","timestamp":1713657600000},"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>The intelligent monitoring of cutting tools used in the manufacturing industry is steadily becoming more convenient. To accurately predict the state of tools and tool breakages, this study proposes a tool wear prediction technique based on multi-sensor information fusion. First, the vibrational, current, and cutting force signals transmitted during the machining process were collected, and the features were extracted. Next, the Kalman filtering algorithm was used for feature fusion, and a predictive model for tool wear was constructed by combining the ResNet and long short-term memory (LSTM) models (called ResNet-LSTM). Experimental data for thin-walled parts obtained under various machining conditions were utilized to monitor the changes in tool conditions. A comparison between the ResNet and LSTM tool wear prediction models indicated that the proposed ResNet-LSTM model significantly improved the prediction accuracy compared to the individual LSTM and ResNet models. Moreover, ResNet-LSTM exhibited adaptive noise reduction capabilities at the front end of the network for signal feature extraction, thereby enhancing the signal feature extraction capability. The ResNet-LSTM model yielded an average prediction error of 0.0085 mm and a tool wear prediction accuracy of 98.25%. These results validate the feasibility of the tool wear prediction method proposed in this study.<\/jats:p>","DOI":"10.3390\/s24082652","type":"journal-article","created":{"date-parts":[[2024,4,22]],"date-time":"2024-04-22T06:10:18Z","timestamp":1713766218000},"page":"2652","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["Machine Tool Wear Prediction Technology Based on Multi-Sensor Information Fusion"],"prefix":"10.3390","volume":"24","author":[{"given":"Kang","family":"Wang","sequence":"first","affiliation":[{"name":"Digital Manufacturing Institute, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aimin","family":"Wang","sequence":"additional","affiliation":[{"name":"Digital Manufacturing Institute, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Long","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangjun","family":"Xie","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1016\/0888-3270(92)90049-O","article-title":"A comparative study of non-parametric spectral estimators for application in machine vibration analysis","volume":"6","author":"Najafi","year":"1992","journal-title":"Mech. Syst. Signal. Process."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1032","DOI":"10.1016\/j.jmrt.2019.10.031","article-title":"Tool condition monitoring techniques in milling process\u2014A review","volume":"9","author":"Mohanraj","year":"2020","journal-title":"J. Mater. Res. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"791","DOI":"10.1016\/j.precisioneng.2014.04.007","article-title":"Effect of different features to drill-wear prediction with back propagation neural network","volume":"38","author":"Xu","year":"2014","journal-title":"Precis. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.rcim.2009.11.002","article-title":"On-line monitoring of boring tools for control of boring operations","volume":"26","author":"Liu","year":"2010","journal-title":"Robot. Comput. Integr. Manuf."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1759","DOI":"10.1016\/j.wear.2017.02.017","article-title":"Cutting tool wear classification and detection using multi-sensor signals and Mahalanobis-Taguchi System","volume":"376","author":"Rizal","year":"2017","journal-title":"Wear"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2619","DOI":"10.1016\/j.wear.2011.01.038","article-title":"Monitoring online cutting tool wear using low-cost technique and user-friendly GUI","volume":"271","author":"Ghani","year":"2011","journal-title":"Wear"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1704","DOI":"10.1016\/j.ymssp.2009.01.003","article-title":"Development of an intelligent multisensor chatter detection system in milling","volume":"23","author":"Kuljanic","year":"2009","journal-title":"Mech. Syst. Signal. Process."},{"key":"ref_8","first-page":"924","article-title":"Vibration and acoustic emission signal monitoring for detection of induction motor bearing fault","volume":"4","author":"Othman","year":"2015","journal-title":"Int. J. Eng. Res. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1016\/j.ymssp.2015.04.019","article-title":"Multi-sensor data fusion framework for CNC machining monitoring","volume":"66","author":"Duro","year":"2016","journal-title":"Mech. Syst. Signal. Process."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.procir.2013.09.016","article-title":"Multiple sensor monitoring in nickel alloy turning for tool wear assessment via sensor fusion","volume":"12","author":"Segreto","year":"2013","journal-title":"Procedia CIRP"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.matpr.2021.12.131","article-title":"Indirect tool wear measurement and prediction using multi-sensor data fusion and neural network during machining","volume":"56","author":"Bagga","year":"2022","journal-title":"Mater. Today Proc."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.rcim.2016.05.010","article-title":"Multisensory fusion based virtual tool wear sensing for ubiquitous manufacturing","volume":"45","author":"Wang","year":"2017","journal-title":"Robot. Comput. Integr. Manuf."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.mechatronics.2015.04.017","article-title":"Compressive sensing and sparse decomposition in precision machining process monitoring: From theory to applications","volume":"31","author":"Zhu","year":"2015","journal-title":"Mechatronics"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.ymssp.2015.10.025","article-title":"Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data","volume":"72","author":"Jia","year":"2016","journal-title":"Mech. Syst. Signal. Process."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"33362","DOI":"10.1109\/ACCESS.2018.2846251","article-title":"Dynamic Bayesian network-based approach by integrating sensor deployment for machining process monitoring","volume":"6","author":"He","year":"2018","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"47102","DOI":"10.1109\/ACCESS.2019.2908852","article-title":"Multisensory data-driven health degradation monitoring of machining tools by generalized multiclass support vector machine","volume":"7","author":"Cheng","year":"2019","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1016\/j.measurement.2016.07.054","article-title":"Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis","volume":"93","author":"Guo","year":"2016","journal-title":"Measurement"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.aei.2017.02.005","article-title":"Intelligent fault diagnosis of rolling bearing using hierarchical convolutional network based health state classification","volume":"32","author":"Lu","year":"2017","journal-title":"Adv. Eng. Inform."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1016\/j.ymssp.2019.03.023","article-title":"Relevance vector machine for tool wear prediction","volume":"127","author":"Kong","year":"2019","journal-title":"Mech. Syst. Signal. Process."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"108233","DOI":"10.1016\/j.ymssp.2021.108233","article-title":"In-process stochastic tool wear identification and its application to the improved cutting force modeling of micro milling","volume":"164","author":"Zhang","year":"2022","journal-title":"Mech. Syst. Signal. Process."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.jmapro.2022.06.027","article-title":"Physics-based modeling and information-theoretic sensor and settings selection for tool wear detection in precision machining","volume":"81","author":"Awasthi","year":"2022","journal-title":"J. Manuf. Process."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.jmsy.2021.10.013","article-title":"Physics-informed meta learning for machining tool wear prediction","volume":"62","author":"Li","year":"2022","journal-title":"J. Manuf. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1007\/s11740-014-0527-1","article-title":"Tool wear model based on least squares support vector machines and Kalman filter","volume":"8","author":"Zhang","year":"2014","journal-title":"Prod. Eng. Res. Devel."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"951","DOI":"10.1016\/j.jsv.2007.01.001","article-title":"Online tracking of bearing wear using wavelet packet decomposition and probabilistic modeling: A method for bearing prognostics","volume":"302","author":"Ocak","year":"2007","journal-title":"J. Sound Vib."},{"key":"ref_25","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. E"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1006\/mssp.1998.0174","article-title":"Time series analysis in metal cutting: Chatter versus chatter-free cutting","volume":"12","author":"Gradisek","year":"1998","journal-title":"Mech. Syst. Signal. Process."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"106435","DOI":"10.1016\/j.cie.2020.106435","article-title":"An optimized model using LSTM network for demand forecasting","volume":"143","author":"Abbasimehr","year":"2020","journal-title":"Comput. Ind. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Yu, H., Miao, X.D., and Wang, H. (2022). Bearing fault reconstruction diagnosis method based on ResNet-152 with multi-scale stacked receptive field. Sensors., 22.","DOI":"10.3390\/s22051705"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1016\/S0924-0136(02)00281-9","article-title":"Finite element simulation of the orthogonal metal cutting process for qualitative understanding of the effects of crater wear on the chip formation process","volume":"127","author":"Li","year":"2002","journal-title":"J. Mater. Process. Technol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"4121","DOI":"10.1007\/s00170-022-09058-7","article-title":"The wear detection of mill-grinding tool based on acoustic emission sensor","volume":"124","author":"Huang","year":"2023","journal-title":"Int. J. Adv. Manuf. Technol."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/8\/2652\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:31:56Z","timestamp":1760106716000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/8\/2652"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,21]]},"references-count":30,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["s24082652"],"URL":"https:\/\/doi.org\/10.3390\/s24082652","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,21]]}}}