{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T04:51:29Z","timestamp":1777956689081,"version":"3.51.4"},"reference-count":45,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100010094","name":"COMAC","doi-asserted-by":"publisher","award":["COMAC-SFGS-2023-3148"],"award-info":[{"award-number":["COMAC-SFGS-2023-3148"]}],"id":[{"id":"10.13039\/100010094","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003452","name":"Innovation and Technology Commission","doi-asserted-by":"publisher","award":["GHX\/075\/22GD"],"award-info":[{"award-number":["GHX\/075\/22GD"]}],"id":[{"id":"10.13039\/501100003452","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52275111"],"award-info":[{"award-number":["52275111"]}],"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":["U23A20620"],"award-info":[{"award-number":["U23A20620"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006579","name":"Ministry of Industry and Information Technology of the People's Republic of China","doi-asserted-by":"publisher","award":["2024YFB4709200"],"award-info":[{"award-number":["2024YFB4709200"]}],"id":[{"id":"10.13039\/501100006579","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012039","name":"Yancheng Institute of Technology","doi-asserted-by":"publisher","award":["YCGH002"],"award-info":[{"award-number":["YCGH002"]}],"id":[{"id":"10.13039\/100012039","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003453","name":"Guangdong Provincial Natural Science Foundation","doi-asserted-by":"publisher","award":["202201010615"],"award-info":[{"award-number":["202201010615"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003453","name":"Guangdong Provincial Natural Science Foundation","doi-asserted-by":"publisher","award":["2021A1515110708"],"award-info":[{"award-number":["2021A1515110708"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100021171","name":"Basic and Applied Basic Research Foundation of Guangdong Province","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004377","name":"Hong Kong Polytechnic University","doi-asserted-by":"publisher","award":["H-ZG6L"],"award-info":[{"award-number":["H-ZG6L"]}],"id":[{"id":"10.13039\/501100004377","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers & Industrial Engineering"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.cie.2026.111996","type":"journal-article","created":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T15:04:55Z","timestamp":1774883095000},"page":"111996","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["A wavelet-constrained band attention neural network for physics-informed interpretable tool wear prediction"],"prefix":"10.1016","volume":"216","author":[{"given":"Dongpeng","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaxian","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhuyun","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guolin","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Feng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zilong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qian","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pai","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weihua","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.cie.2026.111996_b1","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1016\/j.jmapro.2017.09.016","article-title":"A comprehensive study on the effects of tool wear on surface roughness, dimensional integrity and residual stress in turning IN718 hard-to-machine alloy","volume":"30","author":"Akhavan Niaki","year":"2017","journal-title":"Journal of Manufacturing Processes"},{"key":"10.1016\/j.cie.2026.111996_b2","first-page":"1","article-title":"Interpretable neural network via algorithm unrolling for mechanical fault diagnosis","volume":"71","author":"An","year":"2022","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"issue":"3","key":"10.1016\/j.cie.2026.111996_b3","doi-asserted-by":"crossref","first-page":"2521","DOI":"10.1109\/TIE.2020.2972443","article-title":"Machine remaining useful life prediction via an attention-based deep learning approach","volume":"68","author":"Chen","year":"2021","journal-title":"IEEE Transactions on Industrial Electronics"},{"key":"10.1016\/j.cie.2026.111996_b4","doi-asserted-by":"crossref","DOI":"10.1016\/j.cie.2022.107939","article-title":"A machine learning-based iterative design approach to automate user satisfaction degree prediction in smart product-service system","volume":"165","author":"Cong","year":"2022","journal-title":"Computers & Industrial Engineering"},{"key":"10.1016\/j.cie.2026.111996_b5","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2022.108353","article-title":"Variational encoding approach for interpretable assessment of remaining useful life estimation","volume":"222","author":"Costa","year":"2022","journal-title":"Reliability Engineering & System Safety"},{"key":"10.1016\/j.cie.2026.111996_b6","series-title":"Proceedings of the 36th international conference on neural information processing systems","first-page":"10492","article-title":"Revisiting sparse convolutional model for visual recognition","author":"Dai","year":"2022"},{"issue":"6","key":"10.1016\/j.cie.2026.111996_b7","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6501\/abb7a0","article-title":"Multi-frequency-band deep CNN model for tool wear prediction","volume":"32","author":"Duan","year":"2021","journal-title":"Measurement Science and Technology"},{"issue":"6","key":"10.1016\/j.cie.2026.111996_b8","doi-asserted-by":"crossref","first-page":"2900","DOI":"10.1109\/TIE.2013.2274422","article-title":"Multimodal hidden Markov model-based approach for tool wear monitoring","volume":"61","author":"Geramifard","year":"2013","journal-title":"IEEE Transactions on Industrial Electronics"},{"issue":"21","key":"10.1016\/j.cie.2026.111996_b9","doi-asserted-by":"crossref","first-page":"21101","DOI":"10.1109\/JSEN.2022.3208753","article-title":"Remaining useful life prediction based on multisensor fusion and attention TCN-BiGRU model","volume":"22","author":"Gong","year":"2022","journal-title":"IEEE Sensors Journal"},{"key":"10.1016\/j.cie.2026.111996_b10","unstructured":"Gregor, K., & LeCun, Y. (2010). Learning Fast Approximations of Sparse Coding. In Proceedings of the 27th international conference on machine learning (pp. 399\u2013406)."},{"key":"10.1016\/j.cie.2026.111996_b11","series-title":"Proceedings of the 36th international conference on machine learning","first-page":"2494","article-title":"Exploring interpretable LSTM neural networks over multi-variable data","author":"Guo","year":"2019"},{"key":"10.1016\/j.cie.2026.111996_b12","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770\u2013778).","DOI":"10.1109\/CVPR.2016.90"},{"issue":"7","key":"10.1016\/j.cie.2026.111996_b13","doi-asserted-by":"crossref","first-page":"5219","DOI":"10.1109\/TIM.2019.2952476","article-title":"Tool wear estimation in end milling of titanium alloy using NPE and a novel WOA-SVM model","volume":"69","author":"Kong","year":"2019","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"10.1016\/j.cie.2026.111996_b14","article-title":"Traceable algorithm unrolling network: An interpretable deep sparse representation model for mechanical fault diagnosis","author":"Lan","year":"2025","journal-title":"IEEE Transactions on Cybernetics"},{"issue":"3","key":"10.1016\/j.cie.2026.111996_b15","doi-asserted-by":"crossref","first-page":"1314","DOI":"10.1109\/TR.2016.2570568","article-title":"A model-based method for remaining useful life prediction of machinery","volume":"65","author":"Lei","year":"2016","journal-title":"IEEE Transactions on Reliability"},{"key":"10.1016\/j.cie.2026.111996_b16","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.jmsy.2023.11.009","article-title":"Sensor-aware CapsNet: Towards trustworthy multisensory fusion for remaining useful life prediction","volume":"72","author":"Li","year":"2024","journal-title":"Journal of Manufacturing Systems"},{"issue":"12","key":"10.1016\/j.cie.2026.111996_b17","doi-asserted-by":"crossref","first-page":"1935","DOI":"10.1016\/S0890-6955(99)00021-8","article-title":"Discrete wavelet transform for tool breakage monitoring","volume":"39","author":"Li","year":"1999","journal-title":"International Journal of Machine Tools and Manufacture"},{"key":"10.1016\/j.cie.2026.111996_b18","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2021.108487","article-title":"A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications and challenges","volume":"167","author":"Li","year":"2022","journal-title":"Mechanical Systems and Signal Processing"},{"key":"10.1016\/j.cie.2026.111996_b19","doi-asserted-by":"crossref","DOI":"10.1016\/j.cie.2023.109795","article-title":"A domain adversarial graph convolutional network for intelligent monitoring of tool wear in machine tools","volume":"187","author":"Li","year":"2024","journal-title":"Computers & Industrial Engineering"},{"key":"10.1016\/j.cie.2026.111996_b20","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.jmapro.2022.04.066","article-title":"A novel ensemble deep learning model for cutting tool wear monitoring using audio sensors","volume":"79","author":"Li","year":"2022","journal-title":"Journal of Manufacturing Processes"},{"key":"10.1016\/j.cie.2026.111996_b21","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1016\/j.jmsy.2024.11.020","article-title":"Trustworthy ai for human-centric smart manufacturing: a survey","volume":"78","author":"Li","year":"2025","journal-title":"Journal of Manufacturing Systems"},{"key":"10.1016\/j.cie.2026.111996_b22","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2024.102290","article-title":"Fusing consensus knowledge: A federated learning method for fault diagnosis via privacy-preserving reference under domain shift","volume":"106","author":"Li","year":"2024","journal-title":"Information Fusion"},{"issue":"7","key":"10.1016\/j.cie.2026.111996_b23","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":"2022","journal-title":"IEEE Transactions on Industrial Electronics"},{"key":"10.1016\/j.cie.2026.111996_b24","doi-asserted-by":"crossref","DOI":"10.1016\/j.compstruct.2024.118033","article-title":"Tool wear in cutting carbon fiber reinforced polymer\/ceramic matrix composites: A review","volume":"337","author":"Ma","year":"2024","journal-title":"Composite Structures"},{"issue":"9","key":"10.1016\/j.cie.2026.111996_b25","doi-asserted-by":"crossref","first-page":"2683","DOI":"10.1007\/s00170-021-07325-7","article-title":"A review on deep learning in machining and tool monitoring: methods, opportunities, and challenges","volume":"115","author":"Nasir","year":"2021","journal-title":"International Journal of Advanced Manufacturing Technology"},{"issue":"8","key":"10.1016\/j.cie.2026.111996_b26","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6501\/ad45f4","article-title":"Tool wear monitoring based on scSE-ResNet-50-TSCNN model integrating machine vision and force signals","volume":"35","author":"Nie","year":"2024","journal-title":"Measurement Science and Technology"},{"key":"10.1016\/j.cie.2026.111996_b27","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2022.108907","article-title":"Interpretable deep learning LSTM model for intelligent economic decision-making","volume":"248","author":"Park","year":"2022","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.cie.2026.111996_b28","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2024.102394","article-title":"Physics-inspired multimodal machine learning for adaptive correlation fusion based rotating machinery fault diagnosis","volume":"108","author":"Sun","year":"2024","journal-title":"Information Fusion"},{"key":"10.1016\/j.cie.2026.111996_b29","article-title":"Phyformer: A degradation physics-informed self-data driven approach to machinery prognostics","volume":"62","author":"Wang","year":"2024","journal-title":"Advanced Engineering Informatics"},{"key":"10.1016\/j.cie.2026.111996_b30","doi-asserted-by":"crossref","DOI":"10.1016\/j.cie.2025.111576","article-title":"A feature extraction method for multi-source uncertainty adaptive fusion in complex equipment failure mode recognition","author":"Wang","year":"2025","journal-title":"Computers & Industrial Engineering"},{"issue":"23","key":"10.1016\/j.cie.2026.111996_b31","doi-asserted-by":"crossref","first-page":"15667","DOI":"10.3390\/su142315667","article-title":"Multi-scale remaining useful life prediction using long short-term memory","volume":"14","author":"Wang","year":"2022","journal-title":"Sustainability"},{"key":"10.1016\/j.cie.2026.111996_b32","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2022.109046","article-title":"Explainability-driven model improvement for SOH estimation of lithium-ion battery","volume":"232","author":"Wang","year":"2023","journal-title":"Reliability Engineering & System Safety"},{"key":"10.1016\/j.cie.2026.111996_b33","doi-asserted-by":"crossref","DOI":"10.1016\/j.cie.2025.110964","article-title":"A dual-path architecture based on time series decomposition and degradation correction for remaining useful life prediction of aero-engine","volume":"203","author":"Wu","year":"2025","journal-title":"Computers & Industrial Engineering"},{"key":"10.1016\/j.cie.2026.111996_b34","first-page":"1","article-title":"Degradation-aware remaining useful life prediction with lstm autoencoder","volume":"70","author":"Wu","year":"2021","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"10.1016\/j.cie.2026.111996_b35","doi-asserted-by":"crossref","DOI":"10.1016\/j.cie.2022.108204","article-title":"A novel multi-scale CNN and attention mechanism method with multi-sensor signal for remaining useful life prediction","volume":"169","author":"Xu","year":"2022","journal-title":"Computers & Industrial Engineering"},{"key":"10.1016\/j.cie.2026.111996_b36","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.neucom.2019.09.074","article-title":"Remaining useful life prediction of lithium-ion batteries with adaptive unscented kalman filter and optimized support vector regression","volume":"376","author":"Xue","year":"2020","journal-title":"Neurocomputing"},{"key":"10.1016\/j.cie.2026.111996_b37","doi-asserted-by":"crossref","DOI":"10.1016\/j.cie.2022.108559","article-title":"A deep learning framework for sensor-equipped machine health indicator construction and remaining useful life prediction","volume":"172","author":"Yan","year":"2022","journal-title":"Computers & Industrial Engineering"},{"key":"10.1016\/j.cie.2026.111996_b38","doi-asserted-by":"crossref","DOI":"10.1016\/j.cie.2025.110967","article-title":"An unsupervised subdomain adaptation of cross-domain remaining useful life prediction for sensor-equipped equipments","volume":"203","author":"Yan","year":"2025","journal-title":"Computers & Industrial Engineering"},{"key":"10.1016\/j.cie.2026.111996_b39","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2025.114283","article-title":"State of charge prediction for lithium-ion batteries in electric aircraft based on self-supervised informer","volume":"186","author":"Zhang","year":"2026","journal-title":"Applied Soft Computing"},{"issue":"7","key":"10.1016\/j.cie.2026.111996_b40","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6501\/ade552","article-title":"Federated transfer learning for remaining useful life prediction in prognostics with data privacy","volume":"36","author":"Zhang","year":"2025","journal-title":"Measurement Science and Technology"},{"issue":"4","key":"10.1016\/j.cie.2026.111996_b41","doi-asserted-by":"crossref","first-page":"3334","DOI":"10.1109\/JSEN.2021.3136622","article-title":"Adaptive spatio-temporal graph information fusion for remaining useful life prediction","volume":"22","author":"Zhang","year":"2021","journal-title":"IEEE Sensors Journal"},{"key":"10.1016\/j.cie.2026.111996_b42","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2021.107617","article-title":"Tool wear estimation and life prognostics in milling: Model extension and generalization","volume":"155","author":"Zhang","year":"2021","journal-title":"Mechanical Systems and Signal Processing"},{"key":"10.1016\/j.cie.2026.111996_b43","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2024.102462","article-title":"MST-GAT: A multi-perspective spatial-temporal graph attention network for multi-sensor equipment remaining useful life prediction","volume":"110","author":"Zhou","year":"2024","journal-title":"Information Fusion"},{"issue":"4","key":"10.1016\/j.cie.2026.111996_b44","doi-asserted-by":"crossref","first-page":"3208","DOI":"10.1109\/TIE.2018.2844856","article-title":"Estimation of bearing remaining useful life based on multiscale convolutional neural network","volume":"66","author":"Zhu","year":"2019","journal-title":"IEEE Transactions on Industrial Electronics"},{"key":"10.1016\/j.cie.2026.111996_b45","doi-asserted-by":"crossref","DOI":"10.1016\/j.cie.2020.106889","article-title":"Predictive maintenance in the industry 4.0: A systematic literature review","volume":"150","author":"Zonta","year":"2020","journal-title":"Computers & Industrial Engineering"}],"container-title":["Computers & Industrial Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S036083522600197X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S036083522600197X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T03:30:59Z","timestamp":1777951859000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S036083522600197X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":45,"alternative-id":["S036083522600197X"],"URL":"https:\/\/doi.org\/10.1016\/j.cie.2026.111996","relation":{},"ISSN":["0360-8352"],"issn-type":[{"value":"0360-8352","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A wavelet-constrained band attention neural network for physics-informed interpretable tool wear prediction","name":"articletitle","label":"Article Title"},{"value":"Computers & Industrial Engineering","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.cie.2026.111996","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"111996"}}