{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T04:12:17Z","timestamp":1781755937625,"version":"3.54.5"},"reference-count":32,"publisher":"ASME International","issue":"11","license":[{"start":{"date-parts":[[2025,4,17]],"date-time":"2025-04-17T00:00:00Z","timestamp":1744848000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.asme.org\/publications-submissions\/publishing-information\/legal-policies"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72371219"],"award-info":[{"award-number":["72371219"]}],"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":["72001139"],"award-info":[{"award-number":["72001139"]}],"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":["52372308"],"award-info":[{"award-number":["52372308"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["asmedigitalcollection.asme.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Surface quality inspection of manufacturing parts through 3D point cloud data has increasingly gained focus in recent years. Accurate 3D anomaly detection on manufacturing parts is challenging due to their complex shapes, the scarcity of anomaly-free samples, and the irregular distribution of scanned points. This article proposes a novel untrained anomaly detection methodology integrated with domain knowledge, i.e., inherent manufacturing characteristics of real products, to precisely detect anomalies using 3D point cloud data for complex manufacturing surfaces. Specifically, the manufacturing parts such as many axis-symmetric products tend to be component-wise surfaces, i.e., consisting of basic and simple components, and the cut profiles of these parts exhibit similarity. On the basis of the domain knowledge, we propose to segment complex surfaces into simple components and model the similar profiles as low-rank representations, thus enabling the modeling of manufacturing surfaces. Finally, combined low-rank representations and assumed sparsity of anomaly, we introduce robust principal component analysis (RPCA) as a unified formula for surface anomaly detection. Extensive numerical experiments across various types of parts have shown that our method delivers promising results, surpassing existing benchmarks.<\/jats:p>","DOI":"10.1115\/1.4068472","type":"journal-article","created":{"date-parts":[[2025,4,17]],"date-time":"2025-04-17T14:10:51Z","timestamp":1744899051000},"update-policy":"https:\/\/doi.org\/10.1115\/crossmarkpolicy-asme","source":"Crossref","is-referenced-by-count":6,"title":["3D-ADCS: Untrained 3D Anomaly Detection for Complex Manufacturing Surfaces"],"prefix":"10.1115","volume":"25","author":[{"given":"Xuanming","family":"Cao","sequence":"first","affiliation":[{"name":"The Hong Kong University of Science and Technology (Guangzhou) Smart Manufacturing Thrust, Systems Hub, , \u00a0 ,","place":["Guangzhou, China, 511453"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengyu","family":"Tao","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology Interdisciplinary Programs Office, , \u00a0 ,","place":["Hong Kong SAR, China, 999077"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan","family":"Du","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology (Guangzhou) Smart Manufacturing Thrust, Systems Hub, , \u00a0 , ;","place":["Guangzhou, China, 511453"]},{"name":"The Hong Kong University of Science and Technology Department of Mechanical and Aerospace Engineering, , \u00a0 ,","place":["Hong Kong SAR, China, 999077"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"33","published-online":{"date-parts":[[2025,8,19]]},"reference":[{"key":"2025081912243184300_CIT0001","first-page":"1","article-title":"3D Vision-Based Anomaly Detection in Manufacturing: A Survey","author":"Du","year":"2025","journal-title":"Front. Eng. Manage."},{"issue":"6","key":"2025081912243184300_CIT0002","doi-asserted-by":"publisher","first-page":"061008","DOI":"10.1115\/1.4052761","article-title":"Semi-Supervised Learning for Anomaly Classification Using Partially Labeled Subsets","volume":"144","author":"Cohen","year":"2022","journal-title":"ASME J. Manuf. Sci. Eng."},{"issue":"11","key":"2025081912243184300_CIT0003","doi-asserted-by":"publisher","first-page":"1174","DOI":"10.1080\/24725854.2022.2152140","article-title":"Anomaly Detection for Fabricated Artifact by Using Unstructured 3D Point Cloud Data","volume":"55","author":"Tao","year":"2023","journal-title":"IISE Trans."},{"key":"2025081912243184300_CIT0004","volume-title":"Three-Dimensional Imaging Metrology","author":"Tang","year":"2009"},{"issue":"4","key":"2025081912243184300_CIT0005","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10921-017-0453-1","article-title":"3D Point Cloud Analysis for Detection and Characterization of Defects on Airplane Exterior Surface","volume":"36","author":"Jovan\u010devi\u0107","year":"2017","journal-title":"J. Nondestruct. Eval."},{"issue":"2","key":"2025081912243184300_CIT0006","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1080\/24725854.2023.2285840","article-title":"PointSGRADE: Sparse Learning With Graph Representation for Anomaly Detection by Using Unstructured 3D Point Cloud Data","volume":"57","author":"Tao","year":"2025","journal-title":"IISE Trans."},{"key":"2025081912243184300_CIT0007","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW59228.2023.00298","article-title":"Back to the Feature: Classical 3D Features Are (Almost) All You Need for 3D Anomaly Detection","author":"Horwitz","year":"2023"},{"key":"2025081912243184300_CIT0008","first-page":"2613","article-title":"Anomaly Detection in 3D Point Clouds Using Deep Geometric Descriptors","author":"Bergmann","year":"2023"},{"key":"2025081912243184300_CIT0009","first-page":"2592","article-title":"Asymmetric Student-Teacher Networks for Industrial Anomaly Detection","author":"Rudolph","year":"2023"},{"issue":"3","key":"2025081912243184300_CIT0010","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1970392.1970395","article-title":"Robust Principal Component Analysis?","volume":"58","author":"Cand\u00e8s","year":"2011","journal-title":"J. ACM"},{"issue":"10","key":"2025081912243184300_CIT0011","doi-asserted-by":"publisher","first-page":"2262","DOI":"10.3390\/s17102262","article-title":"A Method for Automatic Surface Inspection Using a Model-Based 3D Descriptor","volume":"17","author":"Madrigal","year":"2017","journal-title":"Sensors"},{"issue":"1","key":"2025081912243184300_CIT0012","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1080\/17452759.2020.1832695","article-title":"Rapid Surface Defect Identification for Additive Manufacturing With In-Situ Point Cloud Processing and Machine Learning","volume":"16","author":"Chen","year":"2021","journal-title":"Virtual Phys. Prototyping"},{"key":"2025081912243184300_CIT0013","doi-asserted-by":"publisher","first-page":"624","DOI":"10.1016\/j.jmapro.2022.06.046","article-title":"A Deep-Learning-Based In-Situ Surface Anomaly Detection Methodology for Laser Directed Energy Deposition Via Powder Feeding","volume":"81","author":"Kaji","year":"2022","journal-title":"J. Manuf. Process."},{"key":"2025081912243184300_CIT0014","first-page":"11108","article-title":"RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds","author":"Hu","year":"2020"},{"issue":"4","key":"2025081912243184300_CIT0015","doi-asserted-by":"publisher","first-page":"1498","DOI":"10.1109\/TII.2016.2585982","article-title":"A Multiresolution Approach to Model-Based 3-D Surface Quality Inspection","volume":"12","author":"Von Enzberg","year":"2016","journal-title":"IEEE Trans. Ind. Inf."},{"key":"2025081912243184300_CIT0016","first-page":"3212","article-title":"Fast Point Feature Histograms (FPFH) for 3D Registration","author":"Rusu","year":"2009"},{"key":"2025081912243184300_CIT0017","first-page":"8032","article-title":"Multimodal Industrial Anomaly Detection via Hybrid Fusion","author":"Wang","year":"2023"},{"key":"2025081912243184300_CIT0018","doi-asserted-by":"publisher","first-page":"105965","DOI":"10.1016\/j.engfailanal.2021.105965","article-title":"Pipeline of Turbine Blade Defect Detection Based on Local Geometric Pattern Analysis","volume":"133","author":"Miao","year":"2022","journal-title":"Eng. Failure Anal."},{"key":"2025081912243184300_CIT0019","doi-asserted-by":"publisher","first-page":"587","DOI":"10.1016\/j.jmsy.2020.04.001","article-title":"Efficiently Registering Scan Point Clouds of 3D Printed Parts for Shape Accuracy Assessment and Modeling","volume":"56","author":"Decker","year":"2020","journal-title":"J. Manuf. Syst."},{"issue":"2","key":"2025081912243184300_CIT0020","doi-asserted-by":"publisher","first-page":"1179","DOI":"10.1007\/s00371-023-02839-5","article-title":"Hierarchical Registration Method for Surface Quality Inspection of Long Products","volume":"40","author":"DelaCalle","year":"2024","journal-title":"Vis. Comput."},{"issue":"9","key":"2025081912243184300_CIT0021","doi-asserted-by":"publisher","first-page":"3677","DOI":"10.1007\/s00170-019-03794-z","article-title":"Anomaly Detection in Skin Model Shapes Using Machine Learning Classifiers","volume":"105","author":"Yacob","year":"2019","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"2025081912243184300_CIT0022","doi-asserted-by":"publisher","first-page":"202","DOI":"10.5220\/0010865000003124","article-title":"The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localization","author":"Bergmann","year":"2022"},{"key":"2025081912243184300_CIT0023","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v109.i02","article-title":"scikit-fda: A Python Package for Functional Data Analysis","volume-title":"J. Stat. Soft.","author":"Ramos-Carre\u00f1o","year":"2024"},{"issue":"7\u20138","key":"2025081912243184300_CIT0024","doi-asserted-by":"publisher","first-page":"1568","DOI":"10.1016\/j.mcm.2013.04.007","article-title":"Comparative Study of Different B-Spline Approaches for Functional Data","volume":"58","author":"Aguilera","year":"2013","journal-title":"Math. Comput. Model."},{"key":"2025081912243184300_CIT0025","doi-asserted-by":"publisher","first-page":"2549","DOI":"10.1016\/j.matpr.2022.03.368","article-title":"DIC Deformation Analysis Using B-Spline Smoothing With Consideration of Characteristic Noise Properties","volume":"62","author":"Lehmann","year":"2022","journal-title":"Mater. Today: Proc."},{"issue":"2\u20133","key":"2025081912243184300_CIT0026","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1016\/0098-3004(84)90020-7","article-title":"FCM: The Fuzzy C-Means Clustering Algorithm","volume":"10","author":"Bezdek","year":"1984","journal-title":"Comput. Geosci."},{"key":"2025081912243184300_CIT0027","first-page":"89","article-title":"High-Dimensional Data Clustering With Fuzzy C-Means: Problem, Reason, and Solution","author":"Shen","year":"2021"},{"key":"2025081912243184300_CIT0028","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.csda.2018.03.017","article-title":"Directional Outlyingness for Multivariate Functional Data","volume":"131","author":"Dai","year":"2019","journal-title":"Comput. Stat. Data Anal."},{"key":"2025081912243184300_CIT0029","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2021.3056738","article-title":"Surface Defects Detection Using Non-Convex Total Variation Regularized RPCA With Kernelization","volume":"70","author":"Wang","year":"2021","journal-title":"IEEE Trans. Instrum. Measure."},{"issue":"3","key":"2025081912243184300_CIT0030","doi-asserted-by":"publisher","first-page":"1170","DOI":"10.1109\/TASE.2020.2997718","article-title":"Weighted Double-Low-Rank Decomposition With Application to Fabric Defect Detection","volume":"18","author":"Mo","year":"2020","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"issue":"2","key":"2025081912243184300_CIT0031","doi-asserted-by":"publisher","first-page":"1081","DOI":"10.1109\/TPAMI.2020.3027968","article-title":"Truncated Robust Principle Component Analysis With a General Optimization Framework","volume":"44","author":"Nie","year":"2020","journal-title":"IEEE. Trans Pattern Anal. Mach. Intell."},{"issue":"12","key":"2025081912243184300_CIT0032","doi-asserted-by":"publisher","first-page":"527","DOI":"10.3390\/ijgi8120527","article-title":"Non-Temporal Point Cloud Analysis for Surface Damage in Civil Structures","volume":"8","author":"Mohammadi","year":"2019","journal-title":"ISPRS Int. J. Geo-Inf."}],"container-title":["Journal of Computing and Information Science in Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/asmedigitalcollection.asme.org\/computingengineering\/article-pdf\/25\/11\/111002\/7466894\/jcise-24-1480.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/asmedigitalcollection.asme.org\/computingengineering\/article-pdf\/25\/11\/111002\/7466894\/jcise-24-1480.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,19]],"date-time":"2025-08-19T16:24:37Z","timestamp":1755620677000},"score":1,"resource":{"primary":{"URL":"https:\/\/asmedigitalcollection.asme.org\/computingengineering\/article\/25\/11\/111002\/1215344\/3D-ADCS-Untrained-3D-Anomaly-Detection-for-Complex"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,19]]},"references-count":32,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2025,11,1]]}},"URL":"https:\/\/doi.org\/10.1115\/1.4068472","relation":{},"ISSN":["1530-9827","1944-7078"],"issn-type":[{"value":"1530-9827","type":"print"},{"value":"1944-7078","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,19]]},"article-number":"111002"}}