[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128296-en":3,"doc-seo-128296-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},128296,962085570644,"Evangeline","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Process mapping and anomaly detection in laser wire directed energy deposition additive manufacturing using in-situ imaging and process-aware machine learning","Laser wire directed energy deposition (LW-DED) additive manufacturing is analyzed to improve process scalability and reliability. The study pursues two goals: mapping process states as a function of processing parameters, and monitoring to detect process anomalies using data from an in-situ meltpool imaging sensor. Single-track experiments cover 128 parameter combinations across laser power, scanning velocity, and linear mass density. High-speed imaging reveals four regimes—stable, dripping, stubbing, and incomplete melting. Physically intuitive meltpool morphology and intensity features train lightweight process-aware machine learning models, achieving near-90% statistical fidelity versus 87% for black-box deep learning using raw meltpool images.","University of Southern Denmark  \nProcess mapping and anomaly detection in laser wire directed energy deposition additive manufacturing using in-situ imaging and process-aware machine learning  \nAssad, Anis; Bevans, Benjamin D. ; Potter, Willem; Rao, Prahalada; Cormier, Denis; Deschamps, Fernando; Hamilton, Jakob; Rivero, Iris V.  \nPublished in:  \nMaterials & Design  \nDOI:  \n10.1016/j.matdes.2024.113281  \nPublication date: 2024  \nDocument version:  \nFinal published version  \nDocument license: CC BY-NC-ND  \nCitation for pulished version (APA):  \nAssad, A. , Bevans, B. D. , Potter, W. , Rao, P. , Cormier, D. , Deschamps, F. , Hamilton, J. , & Rivero, I. V. (2024) . Process mapping and anomaly detection in laser wire directed energy deposition additive manufacturing using in-situ imaging and process-aware machine learning. Materials & Design, 245, Article 113281.  \n[https://doi.org/10.1016/j.matdes.2024.113281](https://doi.org/10.1016/j.matdes.2024.113281)  \nGo to publication entry in University of Southern Denmark's Research Portal  \nTerms of use  \nThis work is brought to you by the University of Southern Denmark.  \nUnless otherwise specified it has been shared according to the terms for self-archiving.  \nIf no other license is stated, these terms apply:  \n• You may download this work for personal use only.  \n• You may not further distribute the material or use it for any profit-making activity or commercial gain  \n• You may freely distribute the URL identifying this open access version  \nIf you believe that this document breaches copyright please contact us providing details and we will investigate your claim. Please direct all enquiries to [puresupport@bib.sdu.dk](puresupport@bib.sdu.dk)  \nDownload date: 05. Aug. 2026  \nMaterials & Design 245 (2024) 113281  \nContents lists available at ScienceDirect Materials & Design  \njournal [homepage:](homepage: www.elsevier.com/locate/matdes)[ www.elsevier.com/locate/matdes](homepage: www.elsevier.com/locate/matdes)  \n| Process mapping and anomaly detection in laser wire directed energy deposition additive manufacturing using in-situ imaging and process-aware machine learning\u003Cbr>Anis Assada,b,f, Benjamin D. Bevansa, Willem Potter c, Prahalada Rao a,g, Denis Cormier d, Fernando Deschamps b, Jakob D. Hamilton c,*, Iris V. Riveroe\u003Cbr>a Grado Department of Industrial and Systems Engineering, Virginia Polytechnical Institute and State University (Virginia Tech), Blacksburg, VA, USA b Department of Industrial and Systems Engineering, Pontifical Catholic University of Parana, Curitiba, PR, Brazil\u003Cbr>c Department of Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, IA, USA d Department of Industrial and Systems Engineering, Rochester Institute of Tech., Rochester, NY, USA e Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USAf Department of Technology and Innovation, University of Southern Denmark, Sønderborg, Denmark\u003Cbr>g Mechanical Engineering, Virginia Polytechnical Institute and State University (Virginia Tech), Blacksburg, VA, USA |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>LW-DED process mapping Process stability\u003Cbr>Meltpool imaging\u003Cbr>Process-aware machine learning Deep learning |  | This work concerns the laser wire directed energy deposition (LW-DED) additive manufacturing process. The objectives were two-fold: (1) process mapping – demarcating the process states as a function of the processing parameters; and (2) process monitoring – detecting process anomalies (instabilities) using data acquired from an in-situ meltpool imaging sensor. The LW-DED process enables high-throughput, near-net shape manufacturing. Without rigorous parameter control, however, LW-DED often introduces defects due to stochastic process drifts. To enhance scalability and reliability, it is essential to understand how LW-DED parameters affect processing regimes, and detect deleterious process drifts. ","cbCaihppXg09o69l","https://ap.wps.com/l/cbCaihppXg09o69l","pdf",17705737,1,19,"English","en",105,"# Introduction\n## Objectives and motivation","[{\"question\":\"What are the two main objectives of the study on LW-DED additive manufacturing?\",\"answer\":\"The study aims to (1) map process states based on processing parameters and (2) monitor the process by detecting anomalies using in-situ meltpool imaging data.\"},{\"question\":\"How are process states identified in the experiments?\",\"answer\":\"Single-track experiments run 128 combinations of laser power, scanning velocity, and linear mass density, while high-speed imaging is used to delineate four regimes: stable, dripping, stubbing, and incomplete melting.\"},{\"question\":\"What data and modeling approach are used for anomaly classification?\",\"answer\":\"Physically intuitive meltpool morphology and intensity signatures are used to train simple process-aware machine learning models that classify the process state into one of the four regimes.\"}]","Process mapping and anomaly detection in laser wire directed energy deposition additive manufacturing using in-situ imaging and process-aware machine learning | 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