{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,30]],"date-time":"2025-11-30T13:50:59Z","timestamp":1764510659923,"version":"build-2065373602"},"reference-count":26,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2019,6,19]],"date-time":"2019-06-19T00:00:00Z","timestamp":1560902400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 51865004"],"award-info":[{"award-number":["No. 51865004"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018555","name":"Science and Technology Project of Guizhou Province","doi-asserted-by":"publisher","award":["No. [2018]5781","No. [2017]3004"],"award-info":[{"award-number":["No. [2018]5781","No. [2017]3004"]}],"id":[{"id":"10.13039\/501100018555","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>To accurately and efficiently detect tool wear values during production and processing activities, a new online detection model is proposed called the Residual Dense Network (RDN). The model is created with two main steps: Firstly, the time-domain signals for a cutting tool are obtained (e.g., using acceleration sensors); these signals are processed to denoise and segmented to provide a larger number of uniform samples. This processing helps to improve the robustness of the model. Secondly, a new deep convolutional neural network is proposed to extract features adaptively, by combining the idea of a recursive residual network and a dense network. Notably, this method is specifically tailored to the tool wear value detection problem. In this way, the limitations of traditional manual feature extraction steps can be avoided. The experimental results demonstrate that the proposed method is promising in terms of detection accuracy and speed; it provides a new way to detect tool wear values in practical industrial scenarios.<\/jats:p>","DOI":"10.3390\/sym11060809","type":"journal-article","created":{"date-parts":[[2019,6,19]],"date-time":"2019-06-19T10:43:32Z","timestamp":1560941012000},"page":"809","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Research on a Tool Wear Monitoring Algorithm Based on Residual Dense Network"],"prefix":"10.3390","volume":"11","author":[{"given":"Yiting","family":"Li","sequence":"first","affiliation":[{"name":"Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang 550025, China"}]},{"given":"Qingsheng","family":"Xie","sequence":"additional","affiliation":[{"name":"Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang 550025, China"}]},{"given":"Haisong","family":"Huang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang 550025, China"}]},{"given":"Qipeng","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang 550025, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,6,19]]},"reference":[{"key":"ref_1","first-page":"1007","article-title":"A review: Prognostics and health management","volume":"24","author":"Yu","year":"2010","journal-title":"J. Electron. Meas. Instrum."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2509","DOI":"10.1007\/s00170-018-1768-5","article-title":"Review of tool condition monitoring methods in milling processes","volume":"96","author":"Zhou","year":"2018","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.cirpj.2013.02.005","article-title":"Application of digital image processing in tool condition monitoring: A review","volume":"6","author":"Dutta","year":"2013","journal-title":"CIRP J. Manuf. Sci. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.precisioneng.2016.12.011","article-title":"Artificial neural network based tool condition monitoring in micro mechanical peck drilling using thrust force signals","volume":"48","author":"Patra","year":"2017","journal-title":"Precis. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"667","DOI":"10.1109\/TIM.2013.2281576","article-title":"Tool Condition Monitoring of Single-Point Dresser Using Acoustic Emission and Neural Networks Models","volume":"63","author":"Martins","year":"2014","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_6","unstructured":"Li, X., Lim, B.S., Zhou, J.H., Huang, S., Phua, S.J., Shaw, K.C., and Er, M.J. (October, January 27). Fuzzy neural network modelling for tool wear estimation in dry milling operation. Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM, Montreal, QC, Canada."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"446","DOI":"10.4028\/www.scientific.net\/MSF.800-801.446","article-title":"Tool Wear Identification in Turning Titanium Alloy Based on SVM","volume":"800","author":"Liao","year":"2014","journal-title":"Mater. Sci. Forum"},{"key":"ref_8","first-page":"975","article-title":"Tool wear state recognition based on LS-SVM with the PSO algorithm","volume":"57","author":"Liu","year":"2017","journal-title":"J. Tsinghua Univ."},{"key":"ref_9","first-page":"187","article-title":"Application of Fractal Dimensions and Fuzzy Clustering to Tool Wear Monitoring","volume":"11","author":"Li","year":"2013","journal-title":"Telkomnika"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2437","DOI":"10.1007\/s00170-015-7895-3","article-title":"Multi-scale hybrid HMM for tool wear condition monitoring","volume":"84","author":"Liao","year":"2016","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Li, X., Er, M.J., Ge, H., Gan, O.P., Huang, S., Zhai, L.Y., and Torabi, A.J. (2012, January 25\u201328). Adaptive Network Fuzzy Inference System and support vector machine learning for tool wear estimation in high speed milling processes. Proceedings of the Conference of the IEEE Industrial Electronics Society, Montreal, QC, Canada.","DOI":"10.1109\/IECON.2012.6389448"},{"key":"ref_12","first-page":"1292","article-title":"Prediction of tool wear based on generalized dimensions and optimized BP neural network","volume":"34","author":"Zhang","year":"2013","journal-title":"J. Northeast. Univ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-level classification of skin cancer with deep neural networks","volume":"542","author":"Esteva","year":"2017","journal-title":"Nature"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"Fast learning algorithm for deep belief nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"6103","DOI":"10.1109\/ACCESS.2017.2717492","article-title":"Bearing Fault Diagnosis Using Fully-Connected Winner-Take-All Autoencoder","volume":"6","author":"Li","year":"2017","journal-title":"IEEE Access"},{"key":"ref_17","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_18","doi-asserted-by":"crossref","unstructured":"Zhang, A., Wang, H., Li, S., Cui, Y., Liu, Z., Yang, G., and Hu, J. (2018). Transfer Learning with Deep Recurrent Neural Networks for Remaining Useful Life Estimation. Appl. Sci., 8.","DOI":"10.3390\/app8122416"},{"key":"ref_19","first-page":"2146","article-title":"Research on tool wear monitoring based on deep learning","volume":"10","author":"Zhang","year":"2017","journal-title":"Comput. Integr. Manuf. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 1\u201326). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Tai, Y., Yang, J., and Liu, X. (2017, January 21\u201326). Image Super-Resolution via Deep Recursive Residual Network. Proceedings of the IEEE Computer Vision and Pattern Recognition (CVPR 2017), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.298"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., and Laurens, V.D.M. (2016, January 27\u201330). Densely Connected Convolutional Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_23","unstructured":"Ioffe, S., and Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv."},{"key":"ref_24","unstructured":"Simonyan, I.K., and Zisserman, I.A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_25","unstructured":"Zaremba, W., Sutskever, I., and Vinyals, O. (2014). Recurrent Neural Network Regularization. arXiv."},{"key":"ref_26","unstructured":"Xingjian, S.H.I., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., and Woo, W.C. (2015, January 7\u201312). Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting. Proceedings of the 28th International Conference on Neural Information Processing Systems, Montreal, QC, Canada."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/11\/6\/809\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:59:36Z","timestamp":1760187576000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/11\/6\/809"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,19]]},"references-count":26,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2019,6]]}},"alternative-id":["sym11060809"],"URL":"https:\/\/doi.org\/10.3390\/sym11060809","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2019,6,19]]}}}