{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T23:08:40Z","timestamp":1778627320196,"version":"3.51.4"},"reference-count":55,"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\/501100004543","name":"China Scholarship Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Advanced Engineering Informatics"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.aei.2026.104784","type":"journal-article","created":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T11:33:00Z","timestamp":1778585580000},"page":"104784","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PC","title":["Machining error prediction of five-axis milling thin-walled components based on finite element and machine learning"],"prefix":"10.1016","volume":"74","author":[{"given":"Shaoqing","family":"Qin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2777-202X","authenticated-orcid":false,"given":"Lida","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanzhe","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanpeng","family":"Hao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiming","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingxi","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shangqi","family":"Jian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.aei.2026.104784_b0005","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.cirp.2021.04.013","article-title":"Prediction of plastic surface defects for 5-axis ball end milling of Ti-6Al-4\u00a0V with rounded cutting edges using a material removal simulation","volume":"70","author":"Denkena","year":"2021","journal-title":"CIRP Ann."},{"issue":"10","key":"10.1016\/j.aei.2026.104784_b0010","doi-asserted-by":"crossref","first-page":"4175","DOI":"10.1007\/s00170-024-14917-6","article-title":"Machining-induced geometric errors in thin-walled parts\u2014a review of mitigation strategies and development of application guidelines","volume":"136","author":"Draz","year":"2025","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"10.1016\/j.aei.2026.104784_b0015","doi-asserted-by":"crossref","first-page":"919","DOI":"10.1016\/j.jmsy.2024.11.002","article-title":"A new cause-mechanism independence estimation based cross-domain learning method for machining deformation prediction","volume":"77","author":"Ni","year":"2024","journal-title":"J. Manuf. Syst."},{"issue":"7\u20138","key":"10.1016\/j.aei.2026.104784_b0020","doi-asserted-by":"crossref","first-page":"3775","DOI":"10.1007\/s00170-025-15053-5","article-title":"Error analysis of blade milling considering surface features and deformation","volume":"136","author":"Wu","year":"2025","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"10.1016\/j.aei.2026.104784_b0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.tws.2024.112049","article-title":"Prediction of machining deformation for circular metallic plates under residual stress and clamping force in turning","volume":"202","author":"Ju","year":"2024","journal-title":"Thin-Walled Struct."},{"issue":"5\u20136","key":"10.1016\/j.aei.2026.104784_b0030","doi-asserted-by":"crossref","first-page":"2653","DOI":"10.1007\/s00170-024-14073-x","article-title":"A unified geometric error model applicable to all configurations of three-axis machine tools","volume":"134","author":"Nguyen","year":"2024","journal-title":"Int. J. Adv. Manuf. Technol."},{"issue":"7","key":"10.1016\/j.aei.2026.104784_b0035","doi-asserted-by":"crossref","first-page":"3377","DOI":"10.1007\/s10845-023-02220-2","article-title":"A new effective decoupling method to identify the tracking errors of the motion axes of the five-axis machine tools","volume":"35","author":"Osei","year":"2023","journal-title":"J. Intell. Manuf."},{"key":"10.1016\/j.aei.2026.104784_b0040","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2025.112852","article-title":"Model-based motion error prediction and surface topography simulation for the 3-axis milling machine with nonlinear support under drive constraints","volume":"234","author":"Hao","year":"2025","journal-title":"Mech. Syst. Sig. Process."},{"issue":"6","key":"10.1016\/j.aei.2026.104784_b0045","doi-asserted-by":"crossref","DOI":"10.1088\/2631-7990\/ad6de3","article-title":"On-machine inspection and compensation for thin-walled parts with sculptured surface considering cutting vibration and probe posture","volume":"6","author":"Hao","year":"2024","journal-title":"International Journal of Extreme Manufacturing"},{"key":"10.1016\/j.aei.2026.104784_b0050","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.ijmachtools.2018.08.003","article-title":"Predictive modeling of chatter stability considering force-induced deformation effect in milling thin-walled parts","volume":"135","author":"Sun","year":"2018","journal-title":"Int. J. Mach. Tool Manuf."},{"key":"10.1016\/j.aei.2026.104784_b0055","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2025.112474","article-title":"A coupling analysis model for chatter prediction of thin-walled workpieces considering the effects of force-induced deflection and material removal","volume":"229","author":"Lou","year":"2025","journal-title":"Mech. Syst. Sig. Process."},{"issue":"6","key":"10.1016\/j.aei.2026.104784_b0060","doi-asserted-by":"crossref","DOI":"10.1088\/2631-7990\/adec24","article-title":"A review of chatter suppression in thin-wall milling: strategies, mechanisms, and applications","volume":"7","author":"Sun","year":"2025","journal-title":"International Journal of Extreme Manufacturing"},{"key":"10.1016\/j.aei.2026.104784_b0065","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/j.jmapro.2024.02.013","article-title":"Deformation prediction in flank milling of thin-walled parts based on cutter-workpiece engagement","volume":"115","author":"Lin","year":"2024","journal-title":"J. Manuf. Process."},{"key":"10.1016\/j.aei.2026.104784_b0070","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.ijmachtools.2018.01.005","article-title":"Surface form error prediction in five-axis flank milling of thin-walled parts","volume":"128","author":"Li","year":"2018","journal-title":"Int. J. Mach. Tool Manuf."},{"issue":"5","key":"10.1016\/j.aei.2026.104784_b0075","doi-asserted-by":"crossref","DOI":"10.1016\/j.cja.2025.103853","article-title":"Statistical characteristics mining of measured machining error of multi-stage compressor blisks","volume":"39","author":"Dan","year":"2026","journal-title":"Chin. J. Aeronaut."},{"key":"10.1016\/j.aei.2026.104784_b0080","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.jmsy.2025.01.005","article-title":"Digital twin technology in modern machining: a comprehensive review of research on machining errors","volume":"79","author":"Fu","year":"2025","journal-title":"J. Manuf. Syst."},{"key":"10.1016\/j.aei.2026.104784_b0085","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijmecsci.2020.106111","article-title":"A hybrid driven approach to integrate surrogate model and Bayesian framework for the prediction of machining errors of thin-walled parts","volume":"192","author":"Sun","year":"2021","journal-title":"Int. J. Mech. Sci."},{"key":"10.1016\/j.aei.2026.104784_b0090","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijmecsci.2025.110262","article-title":"Aero-engine blade error distributions predictions using novel machine learning models","volume":"295","author":"Yang","year":"2025","journal-title":"Int. J. Mech. Sci."},{"key":"10.1016\/j.aei.2026.104784_b0095","doi-asserted-by":"crossref","DOI":"10.1016\/j.rcim.2024.102936","article-title":"A contour error prediction method for tool path correction using a multi-feature hybrid model in robotic milling systems","volume":"93","author":"Tan","year":"2025","journal-title":"Rob. Comput. Integr. Manuf."},{"issue":"3\u20134","key":"10.1016\/j.aei.2026.104784_b0100","doi-asserted-by":"crossref","first-page":"1365","DOI":"10.1007\/s00170-024-13817-z","article-title":"Research on online prediction of deformation of thin-walled parts based on digital twin technology","volume":"133","author":"Geng","year":"2024","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"10.1016\/j.aei.2026.104784_b0105","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.jmsy.2025.05.017","article-title":"A method for monitoring machining errors of complex thin-walled parts based on the fusion of physical information and CNN","volume":"81","author":"Li","year":"2025","journal-title":"J. Manuf. Syst."},{"issue":"7\u20138","key":"10.1016\/j.aei.2026.104784_b0110","doi-asserted-by":"crossref","first-page":"3799","DOI":"10.1007\/s00170-025-15304-5","article-title":"A method for predicting machining error of thin-walled part considering the dynamic response of elastic deformation","volume":"137","author":"Li","year":"2025","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"10.1016\/j.aei.2026.104784_b0115","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2026.104459","article-title":"Robotic machining quality enhancement via physics-informed error prediction and collaborative compensation","volume":"72","author":"Zhang","year":"2026","journal-title":"Adv. Eng. Inf."},{"key":"10.1016\/j.aei.2026.104784_b0120","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.precisioneng.2026.01.014","article-title":"On-machine measurement error modeling and compensation in three-axis machine tools based on measurement error transformation matrices","volume":"99","author":"Li","year":"2026","journal-title":"Precis. Eng."},{"key":"10.1016\/j.aei.2026.104784_b0125","doi-asserted-by":"crossref","unstructured":"Zhuang Q. Error distribution prediction of five-axis on-machine measurement for aerospace structural parts. Measurement, 267.","DOI":"10.1016\/j.measurement.2026.120573"},{"key":"10.1016\/j.aei.2026.104784_b0130","doi-asserted-by":"crossref","DOI":"10.1016\/j.rcim.2025.103217","article-title":"Learning-based robotic machining error prediction for high precision manufacturing","volume":"100","author":"Niu","year":"2026","journal-title":"Rob. Comput. Integr. Manuf."},{"key":"10.1016\/j.aei.2026.104784_b0135","doi-asserted-by":"crossref","first-page":"454","DOI":"10.1016\/j.jmapro.2021.05.055","article-title":"A state-of-art review on chatter and geometric errors in thin-wall machining processes","volume":"68","author":"Wu","year":"2021","journal-title":"J. Manuf. Process."},{"key":"10.1016\/j.aei.2026.104784_b0140","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.jmsy.2025.09.009","article-title":"Digital twin-driven staged error prediction and compensation framework for the whole process of robotic machining","volume":"83","author":"Zhang","year":"2025","journal-title":"J. Manuf. Syst."},{"issue":"1","key":"10.1016\/j.aei.2026.104784_b0145","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1016\/j.cirp.2024.03.003","article-title":"Optimal stock removal to reduce chatter and deflection errors for five-axis ball-end milling of thin-walled blades","volume":"73","author":"Karimi","year":"2024","journal-title":"CIRP Ann."},{"key":"10.1016\/j.aei.2026.104784_b0150","doi-asserted-by":"crossref","first-page":"682","DOI":"10.1016\/j.precisioneng.2017.11.010","article-title":"Five-axis milling vibration attenuation of freeform thin-walled part by eddy current damping","volume":"51","author":"Butt","year":"2018","journal-title":"Precis. Eng."},{"key":"10.1016\/j.aei.2026.104784_b0155","doi-asserted-by":"crossref","DOI":"10.1016\/j.rcim.2024.102726","article-title":"A stiffness matching-based deformation errors control strategy for dual-robot collaborative machining of thin-walled parts","volume":"88","author":"Xu","year":"2024","journal-title":"Rob. Comput. Integr. Manuf."},{"key":"10.1016\/j.aei.2026.104784_b0160","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1016\/j.jmapro.2025.04.029","article-title":"Static deflection of cantilever thin wall workpieces in peripheral milling: an analytical model","volume":"143","author":"Morelli","year":"2025","journal-title":"J. Manuf. Process."},{"key":"10.1016\/j.aei.2026.104784_b0165","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijmecsci.2022.107863","article-title":"CWE identification and cutting force prediction in ball-end milling process","volume":"239","author":"Qin","year":"2023","journal-title":"Int. J. Mech. Sci."},{"issue":"4","key":"10.1016\/j.aei.2026.104784_b0170","first-page":"233","article-title":"A combination method of the theory and experiment in determination of cutting force coefficients in ball-end mill processes","volume":"2","author":"Huang","year":"2015","journal-title":"J. Comput. Des. Eng."},{"key":"10.1016\/j.aei.2026.104784_b0175","doi-asserted-by":"crossref","DOI":"10.1016\/j.advengsoft.2025.103909","article-title":"New fast micro-topography estimation algortihms for 5 axis milling","volume":"205","author":"Zekalmi","year":"2025","journal-title":"Adv. Eng. Softw."},{"key":"10.1016\/j.aei.2026.104784_b0180","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2025.103403","article-title":"Multi-process digital twin closed-loop machining through shape-feature state update and error propagation knowledge graph","volume":"65","author":"Lin","year":"2025","journal-title":"Adv. Eng. Inf."},{"key":"10.1016\/j.aei.2026.104784_b0185","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2025.112410","article-title":"Study on developing predicted system model of cutting-edge trajectory for micro-milling process based on tool runout error, chip thickness and force signal","volume":"228","author":"Sun","year":"2025","journal-title":"Mech. Syst. Sig. Process."},{"key":"10.1016\/j.aei.2026.104784_b0190","doi-asserted-by":"crossref","DOI":"10.1016\/j.jmatprotec.2021.117258","article-title":"Force-induced deformation prediction and flexible error compensation strategy in flank milling of thin-walled parts","volume":"297","author":"Li","year":"2021","journal-title":"J. Mater. Process. Technol."},{"key":"10.1016\/j.aei.2026.104784_b0195","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijmecsci.2023.108807","article-title":"Quasistatic deflection analysis of slender ball-end milling cutter","volume":"264","author":"Wang","year":"2024","journal-title":"Int. J. Mech. Sci."},{"key":"10.1016\/j.aei.2026.104784_b0200","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.112456","article-title":"Finite element-integrated neural network framework for spatial modal prediction in machine tool structures","volume":"162","author":"Ullah","year":"2025","journal-title":"Eng. Appl. Artif. Intel."},{"issue":"1","key":"10.1016\/j.aei.2026.104784_b0205","doi-asserted-by":"crossref","DOI":"10.1115\/1.4038000","article-title":"Time-Domain Modeling of Varying Dynamic Characteristics in Thin-Wall Machining using Perturbation and Reduced-Order Substructuring Methods","volume":"140","author":"Tuysuz","year":"2018","journal-title":"J. Manuf. Sci. Eng."},{"key":"10.1016\/j.aei.2026.104784_b0210","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2025.103790","article-title":"PIDGGCN: a novel physics-informed deep learning framework for tool wear monitoring","volume":"68","author":"Chen","year":"2025","journal-title":"Adv. Eng. Inf."},{"key":"10.1016\/j.aei.2026.104784_b0215","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2025.103742","article-title":"Channel-adaptive generative reconstruction and fusion for multi-sensor graph features in few-shot fault diagnosis","volume":"127","author":"You","year":"2026","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.aei.2026.104784_b0220","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2025.112834","article-title":"A multi-sensor tool wear monitoring method based on mechanism-data fusion for industrial scenario","volume":"234","author":"Kang","year":"2025","journal-title":"Mech. Syst. Sig. Process."},{"key":"10.1016\/j.aei.2026.104784_b0225","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2023.102162","article-title":"CFI-LFENet: Infusing cross-domain fusion image and lightweight feature enhanced network for fault diagnosis","volume":"104","author":"Lian","year":"2024","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.aei.2026.104784_b0230","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijmecsci.2022.107375","article-title":"Material removal and surface generation in longitudinal-torsional ultrasonic assisted milling","volume":"227","author":"Qin","year":"2022","journal-title":"Int. J. Mech. Sci."},{"key":"10.1016\/j.aei.2026.104784_b0235","series-title":"Hybrid modeling of surface morphology in longitudinal-torsional ultrasonic-assisted milling of CFRP based on physical mechanisms and signal-driven regression","first-page":"201","author":"Yan","year":"2026"},{"key":"10.1016\/j.aei.2026.104784_b0240","doi-asserted-by":"crossref","DOI":"10.1016\/j.rcim.2026.103303","article-title":"A fusion prediction model of tool wear based on physical information and machine learning in five-axis milling TC4 titanium alloy","author":"Qin","year":"2026","journal-title":"Rob. Comput. Integr. Manuf."},{"key":"10.1016\/j.aei.2026.104784_b0245","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2025.103932","article-title":"A knowledge- and data-driven framework for deformation prediction and control of shield tunneling below existing tunnels","volume":"69","author":"Wu","year":"2026","journal-title":"Adv. Eng. Inf."},{"key":"10.1016\/j.aei.2026.104784_b0250","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2025.118389","article-title":"Thermal error prediction in dry hobbing machine tools: a CNN-BiGRU network with spatiotemporal feature fusion","volume":"256","author":"Yang","year":"2025","journal-title":"Measurement"},{"key":"10.1016\/j.aei.2026.104784_b0255","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2021.108469","article-title":"Operational modal analysis based dynamic parameters identification in milling of thin-walled workpiece","volume":"167","author":"Liu","year":"2022","journal-title":"Mech. Syst. Sig. Process."},{"key":"10.1016\/j.aei.2026.104784_b0260","first-page":"287","article-title":"A non-iterative compensation method for machining errors of thin-walled parts considering coupling effect of tool-workpiece deformation","volume":"41","author":"Ge","year":"2024","journal-title":"Manuf. Lett."},{"key":"10.1016\/j.aei.2026.104784_b0265","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1016\/j.jmsy.2024.09.008","article-title":"Physics-informed tool wear prediction in turning process: a thermo-mechanical wear-included force model integrated with machine learning","volume":"77","author":"Pashmforoush","year":"2024","journal-title":"J. Manuf. Syst."},{"key":"10.1016\/j.aei.2026.104784_b0270","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2023.110685","article-title":"A physics-informed CNN-TSE hybrid network for micro-EDM process monitoring and control","volume":"202","author":"Ye","year":"2023","journal-title":"Mech. Syst. Sig. Process."},{"key":"10.1016\/j.aei.2026.104784_b0275","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1016\/j.jmrt.2024.06.076","article-title":"Tool wear monitoring strategy during micro-milling of TC4 alloy based on a fusion model of recursive feature elimination-bayesian optimization-extreme gradient boosting","volume":"31","author":"Wang","year":"2024","journal-title":"J. Mater. Res. Technol."}],"container-title":["Advanced Engineering Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1474034626004763?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1474034626004763?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T22:11:01Z","timestamp":1778623861000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1474034626004763"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":55,"alternative-id":["S1474034626004763"],"URL":"https:\/\/doi.org\/10.1016\/j.aei.2026.104784","relation":{},"ISSN":["1474-0346"],"issn-type":[{"value":"1474-0346","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Machining error prediction of five-axis milling thin-walled components based on finite element and machine learning","name":"articletitle","label":"Article Title"},{"value":"Advanced Engineering Informatics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.aei.2026.104784","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":"104784"}}