{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T08:16:49Z","timestamp":1783671409825,"version":"3.55.0"},"reference-count":41,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U24B6006"],"award-info":[{"award-number":["U24B6006"]}],"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":["U22B2085"],"award-info":[{"award-number":["U22B2085"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Applied Soft Computing"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.asoc.2026.115330","type":"journal-article","created":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T04:21:31Z","timestamp":1778818891000},"page":"115330","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PA","title":["Multi-sensor information fusion with dual-space aligned GAN for piston lever surface roughness prediction under imbalanced data"],"prefix":"10.1016","volume":"201","author":[{"given":"Mengmeng","family":"Niu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kuo","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiadong","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongqing","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.asoc.2026.115330_bib1","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2022.111912","article-title":"Acceleration of surface roughness evaluation using RANSAC and least squares method for Running-in wear process analysis of plateau surface","volume":"203","author":"Nagai","year":"2022","journal-title":"Measurement"},{"key":"10.1016\/j.asoc.2026.115330_bib2","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2020.106770","article-title":"Bayesian linear regression for surface roughness prediction","volume":"142","author":"Kong","year":"2020","journal-title":"Mech. Syst. Sig. Process"},{"key":"10.1016\/j.asoc.2026.115330_bib3","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1016\/j.jmsy.2022.07.012","article-title":"Prediction of surface roughness using fuzzy broad learning system based on feature selection","volume":"64","author":"Tian","year":"2022","journal-title":"J. Manuf. Syst."},{"issue":"2","key":"10.1016\/j.asoc.2026.115330_bib4","doi-asserted-by":"crossref","first-page":"1755","DOI":"10.1016\/j.eswa.2009.07.033","article-title":"Prediction of surface roughness in the end milling machining using artificial neural network","volume":"37","author":"Zain","year":"2010","journal-title":"Expert Syst. Appl."},{"issue":"3","key":"10.1016\/j.asoc.2026.115330_bib5","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1007\/s10845-010-0415-2","article-title":"Support vector machines models for surface roughness prediction in CNC turning of AISI 304 austenitic stainless steel","volume":"23","author":"\u00c7ayda\u00b8s","year":"2012","journal-title":"J. Intell. Manuf."},{"key":"10.1016\/j.asoc.2026.115330_bib6","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.advengsoft.2017.07.008","article-title":"Prediction of machining accuracy and surface quality for CNC machine tools using data driven approach","volume":"114","author":"Chiu","year":"2017","journal-title":"Adv. Eng. Softw."},{"key":"10.1016\/j.asoc.2026.115330_bib7","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1016\/j.asoc.2016.10.010","article-title":"Evolutionary neuro-fuzzy system for surface roughness evaluation","volume":"52","author":"Svalina","year":"2017","journal-title":"Appl. Soft Comput."},{"issue":"10","key":"10.1016\/j.asoc.2026.115330_bib8","doi-asserted-by":"crossref","first-page":"7683","DOI":"10.1109\/TIM.2020.2980599","article-title":"IntelligentANFIS model for predicting measurement of surface roughness and geometric tolerances in three-axis CNC milling","volume":"69","author":"Kannadasan","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.asoc.2026.115330_bib9","first-page":"1","article-title":"Broad learning system based on binary grey wolf optimization for surface roughness prediction in slot milling","volume":"71","author":"Tian","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.asoc.2026.115330_bib10","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2024.116351","article-title":"Surface roughness prediction based on fusion of dynamic-static data","volume":"243","author":"Wang","year":"2025","journal-title":"Measurement"},{"issue":"4","key":"10.1016\/j.asoc.2026.115330_bib11","doi-asserted-by":"crossref","first-page":"981","DOI":"10.3390\/electronics12040981","article-title":"Applying a neural network to predict surface roughness and machining accuracy in the milling of SUS304","volume":"12","author":"Tsai","year":"2023","journal-title":"Electronics"},{"issue":"9","key":"10.1016\/j.asoc.2026.115330_bib12","doi-asserted-by":"crossref","first-page":"2853","DOI":"10.1007\/s00170-020-06523-z","article-title":"Prediction of surface roughness based on a hybrid feature selection method and long short-term memory network in grinding","volume":"112","author":"Guo","year":"2021","journal-title":"Int. J. Adv. Manuf. Technol."},{"issue":"5","key":"10.1016\/j.asoc.2026.115330_bib13","doi-asserted-by":"crossref","DOI":"10.3390\/s22051991","article-title":"Online surface roughness prediction for assembly interfaces of vertical tail integrating tool wear under variable cutting parameters","volume":"22","author":"Wang","year":"2022","journal-title":"Sensors"},{"issue":"13","key":"10.1016\/j.asoc.2026.115330_bib14","doi-asserted-by":"crossref","first-page":"4943","DOI":"10.3390\/s22134943","article-title":"A novel multi-task learning model with PSAE network for simultaneous estimation of surface quality and tool wear in milling of nickel-based superalloy haynes 230","volume":"22","author":"Cheng","year":"2022","journal-title":"Sensors"},{"issue":"3","key":"10.1016\/j.asoc.2026.115330_bib15","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1007\/s10845-020-01669-9","article-title":"On-line prediction of ultrasonic elliptical vibration cutting surface roughness of tungsten heavy alloy based on deep learning","volume":"33","author":"Pan","year":"2022","journal-title":"J. Intell. Manuf."},{"issue":"1","key":"10.1016\/j.asoc.2026.115330_bib16","doi-asserted-by":"crossref","first-page":"1609","DOI":"10.1007\/s00170-019-04378-7","article-title":"Prediction of Inconel 718 roughness with acoustic emission using convolutional neural network based regression","volume":"105","author":"Ibarra-Zarate","year":"2019","journal-title":"Int J. Adv. Manuf. Technol."},{"key":"10.1016\/j.asoc.2026.115330_bib17","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/j.jmsy.2023.09.001","article-title":"Model-based real-time prediction of surface roughness in fused deposition modeling with graph convolutional network-based error correction","volume":"71","author":"Wei","year":"2023","journal-title":"J. Manuf. Syst."},{"key":"10.1016\/j.asoc.2026.115330_bib18","doi-asserted-by":"crossref","first-page":"2019","DOI":"10.1007\/s00170-024-14631-3","article-title":"A self\u2011adaptive machining parameters adjustment method for stabilizing the machining\u2011induced surface roughness","volume":"135","author":"Lin","year":"2024","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"10.1016\/j.asoc.2026.115330_bib19","doi-asserted-by":"crossref","first-page":"4925","DOI":"10.1007\/s00170-023-11454-6","article-title":"Surface roughness prediction of large shaft grinding via attentional CNN\u2011LSTM fusing multiple process signals","volume":"126","author":"Wang","year":"2023","journal-title":"Int. J. Adv. Manuf. Technol."},{"issue":"16","key":"10.1016\/j.asoc.2026.115330_bib20","article-title":"Estimation of Tool Wear and Surface Roughness Development Using Deep Learning and Sensors Fusion","volume":"21","author":"Huang","year":"2021","journal-title":"Sens. (Basel)"},{"key":"10.1016\/j.asoc.2026.115330_bib21","doi-asserted-by":"crossref","first-page":"446","DOI":"10.1016\/j.jmapro.2023.04.038","article-title":"Surface roughness prediction using multi-source heterogeneous data and Bayesian quantile regression in milling process","volume":"95","author":"Liu","year":"2023","journal-title":"J. Manuf. Process."},{"issue":"18","key":"10.1016\/j.asoc.2026.115330_bib22","doi-asserted-by":"crossref","first-page":"13275","DOI":"10.1007\/s00521-023-08425-z","article-title":"Implementation of transformer-based deep learning architecture for the development of surface roughness classifier using sound and cutting force signals","volume":"35","author":"Bhandari","year":"2023","journal-title":"Neural Comput. & Applic"},{"key":"10.1016\/j.asoc.2026.115330_bib23","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2024.111633","article-title":"Extreme learning machine oriented surface roughness prediction at continuous cutting positions based on monitored acceleration","volume":"219","author":"Yao","year":"2024","journal-title":"Mech. Syst. Sig. Process"},{"key":"10.1016\/j.asoc.2026.115330_bib24","article-title":"Deep learning based multi-source heterogeneous information fusion framework for online monitoring of surface quality in milling process","volume":"133","author":"Wang","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.asoc.2026.115330_bib25","doi-asserted-by":"crossref","DOI":"10.1016\/j.compind.2024.104199","article-title":"A deep transfer learning model for online monitoring of surface roughness in milling with variable parameters","volume":"164","author":"Zhou","year":"2025","journal-title":"Comput. Ind."},{"key":"10.1016\/j.asoc.2026.115330_bib26","doi-asserted-by":"crossref","first-page":"4969","DOI":"10.3390\/s23104969","article-title":"Milling Surface roughness prediction based on physics-informed machine learning","volume":"23","author":"Zeng","year":"2023","journal-title":"Sensors"},{"key":"10.1016\/j.asoc.2026.115330_bib27","doi-asserted-by":"crossref","first-page":"4065","DOI":"10.1007\/s00170-022-10470-2","article-title":"A physics\u2011informed machine learning model for surface roughness prediction in milling operations","volume":"123","author":"Wu","year":"2022","journal-title":"Int. J. Adv. Manuf. Technol."},{"issue":"9","key":"10.1016\/j.asoc.2026.115330_bib28","doi-asserted-by":"crossref","first-page":"2523","DOI":"10.1007\/s00170-015-7884-6","article-title":"Surface roughness prediction in end milling by using predicted point oriented local linear estimation method","volume":"84","author":"Wen","year":"2016","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"10.1016\/j.asoc.2026.115330_bib29","doi-asserted-by":"crossref","first-page":"2989","DOI":"10.1007\/s00170-023-12453-3","article-title":"A monitoring method for surface roughness of \u03b3\u2011TiAl alloy based on deep learning of time\u2013frequency diagram","volume":"129","author":"Wu","year":"2023","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"10.1016\/j.asoc.2026.115330_bib30","doi-asserted-by":"crossref","first-page":"2016","DOI":"10.3390\/mi14112016","article-title":"Surface Roughness Prediction in Ultra-Precision Milling: An Extreme Learning Machine Method with Data Fusion","volume":"14","author":"Shang","year":"2023","journal-title":"Micromachines"},{"issue":"1","key":"10.1016\/j.asoc.2026.115330_bib31","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1007\/s10845-022-02054-4","article-title":"Interpolation-based virtual sample generation for surface roughness prediction","volume":"35","author":"Tian","year":"2024","journal-title":"J. Intell. Manuf."},{"key":"10.1016\/j.asoc.2026.115330_bib32","series-title":"Proc. Adv. Neural Inf. Process. Syst","first-page":"2672","article-title":"Generative adversarial nets","author":"Goodfellow","year":"2014"},{"key":"10.1016\/j.asoc.2026.115330_bib33","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1016\/j.jmsy.2023.05.016","article-title":"Surface roughness prediction through GAN synthesized power signal as a process signature","volume":"68","author":"Cooper","year":"2023","journal-title":"J. Manuf. Syst."},{"key":"10.1016\/j.asoc.2026.115330_bib34","unstructured":"Mroueh, Y., Sercu, T., Goel, V., McGan: Mean and Covariance Feature Matching GAN, 34th International Conference on Machine Learning, vol 70."},{"key":"10.1016\/j.asoc.2026.115330_bib35","unstructured":"Salimans, T.; Goodfellow, I.; Zaremba, W.; Cheung, V.; Radford, A., Chen, X., Improved Techniques for Training GANs, advances in neural information processing systems 29 (NIPS 2016) 29."},{"key":"10.1016\/j.asoc.2026.115330_bib36","doi-asserted-by":"crossref","unstructured":"Y.B. Zhou, Y.T. Ye, P.Y. Zhang, X. Wei, M.S. Chen, Exact Fusion via Feature Distribution Matching for Few-shot Image Generation, 2024 IEEE\/CVF conference on computer vision and pattern recognition, CVPR 2024, pp.8383-8392.","DOI":"10.1109\/CVPR52733.2024.00801"},{"issue":"10","key":"10.1016\/j.asoc.2026.115330_bib37","doi-asserted-by":"crossref","first-page":"7024","DOI":"10.1109\/TNNLS.2021.3137172","article-title":"A novel data augmentation method based on coralgan for prediction of part surface roughness","volume":"34","author":"Wang","year":"2023","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"4","key":"10.1016\/j.asoc.2026.115330_bib38","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1109\/TASSP.1980.1163420","article-title":"Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences","volume":"28","author":"Davis","year":"1980","journal-title":"IEEE Trans. Acoust. Speech Signal Process."},{"key":"10.1016\/j.asoc.2026.115330_bib39","doi-asserted-by":"crossref","unstructured":"J. Hu, L. Shen, G. Sun, Squeeze-and-excitation networks[C], Proc. IEEE Conf. Comput. Vision Pattern Recognit. (2018) 7132\u20137141.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"10.1016\/j.asoc.2026.115330_bib40","first-page":"1322","article-title":"ADASYN Adaptive synthetic sampling approach for imbalanced learning","author":"Haibo","year":"2008","journal-title":"Proc. IEEE Int. Jt. Conf. Neural Netw."},{"key":"10.1016\/j.asoc.2026.115330_bib41","first-page":"1","article-title":"Conditional generative adversarial nets","author":"Mirza","year":"2014","journal-title":"Comput. Sci."}],"container-title":["Applied Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1568494626007787?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1568494626007787?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T07:48:26Z","timestamp":1783669706000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1568494626007787"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":41,"alternative-id":["S1568494626007787"],"URL":"https:\/\/doi.org\/10.1016\/j.asoc.2026.115330","relation":{},"ISSN":["1568-4946"],"issn-type":[{"value":"1568-4946","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Multi-sensor information fusion with dual-space aligned GAN for piston lever surface roughness prediction under imbalanced data","name":"articletitle","label":"Article Title"},{"value":"Applied Soft Computing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.asoc.2026.115330","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"115330"}}