[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123792-en":3,"doc-seo-123792-105":30,"detail-sidebar-cat-0-en-105":91},{"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},123792,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Nondestructive Quantitative Measurement for Precision Quality Control in Additive Manufacturing Using Hyperspectral Imagery and Machine Learning","Measuring metal-powder purity is critical for maintaining product quality in additive manufacturing, where contamination can cause cracks, malfunctions, and costly rework. The paper addresses the limitations of conventional methods that emphasize physical integrity while remaining time-consuming and indirect for composition assessment. It proposes nondestructive inspection using hyperspectral imaging combined with machine learning, leveraging spectral signals across wide frequency ranges and spatial context to capture subtle temperature, moisture, and chemical-composition differences. Near-infrared and visible HSI cameras are used, with solutions demonstrated via three case studies including spectral dictionary construction, contamination detection, and band selection analysis.","Nondestructive quantitative measurement for precision quality control in additive manufacturing using hyperspectral imagery and  \nmachine learning.  \nYAN, Y., REN, J., SUN, H. and WILLIAMS, R.  \n2024  \n© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works  \nThis article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.  \nIEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 1  \nNondestructive Quantitative Measurement for Precision Quality Control in Additive Manufacturing Using Hyperspectral Imagery and Machine Learning  \nYijun Yan , Member, IEEE, Jinchang Ren, Senior Member, IEEE, He Sun, and Robert Williams   \nAbstract—Measuring the purity of the metal powder is essential to maintain the quality of additive manufacturing products. Contamination is a significant concern, leading to cracks and malfunctions in the final products. Conventional assessment methods focus more on physical integrity rather than material composition and can be time-consuming. By capturing spectral data from a wide frequency range along with the spatial information, hyperspectral imaging (HSI) can detect minor differences in terms of temperature, moisture, and chemical composition to tackle this challenge. In this article, we explore the application of HSI in conjunction with machine learning for nondestructive inspection of metal powders. By employing near-infrared and visible HSI cameras, we introduce the utilization of HSI for this purpose. We delve into the technical challenges encountered and present detailed solutions through three case studies, including the establishment of a spectral dictionary, contamination detection, and band selection analysis. Our experimental results demonstrate the immense potential of HSI and its synergy with machine learning for nondestructive testing in powder metallurgy, particularly in meeting the requirements of industrial manufacturing environments.  \nIndex Terms—3-D printing, additive manufacturing (AM), hyperspectral imaging (HSI), metal powder, nondestructive testing (NDT), quality control.  \nManuscript received 31 May 2023; revised 31 October 2023; accepted 27 March 2024 . This work was supported by Carpenter Additive, LPW Technology, Ltd., who also provided the metal samples in our case studies. The work of He Sun was supported by the National Natural Science Foundation of China under Grant 62301534 . Paper no. TII-23-1952 .(Corresponding author: Jinchang Ren.)  \nYijun Yan is with the National Subsea Centre, Robert Gordon University, AB21 0BH Aberdeen, U.K., and also with the School of Science and Engineering, University of Dundee, DD1 4HN Dundee, U.K. (e-mail: [yijun.yan@ieee.org](yijun.yan@ieee.org)).  \nJinchang Ren is with the National Subsea Centre, Robert Gordon University, AB21 0BH Aberdeen, U.K. ([e-mail: j.ren@rgu.ac.uk](e-mail: j.ren@rgu.ac.uk)).  \nHe Sun is with the Key Laboratory of Computational Optical Imaging Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China (e-mail: [sunhe@aircas.ac.cn](sunhe@aircas.ac.cn)) .  \nRobert Williams is with the LPW Technology, Ltd., WA8 0GU Liverpool, U.K. (e-mail: [rwilliams@carpenteradditive.com](rwilliams@carpenteradditive.com)).  \nColor versions of one or more figures in this article are available at [https://doi.org/10.1109/TII.2024.3384609](https://doi.org/10.1109/TII.2024.3384609) .  \nI. INTRODUCTION  \nT AKING advantage of3-Dprinting, additive manufacturing  \n(AM) has become one of the most significant manufacturing industries due to its capacity to save time and cost, reduce waste, andreuse material during the prin","cbCaidkrmsqq8T3A","https://ap.wps.com/l/cbCaidkrmsqq8T3A","pdf",4348130,1,14,"English","en",105,"# Introduction\n## Contamination and its impact on additive manufacturing quality\n## Sources of powder degradation\n# Abstract & Index Terms\n## Core idea: HSI + machine learning for nondestructive inspection\n## Experimental focus and case studies","[{\"question\":\"Why is measuring metal powder purity important in additive manufacturing?\",\"answer\":\"Purity directly affects the quality of printed products. Contamination can lead to cracks and malfunctions, harming strength and reliability.\"},{\"question\":\"How does hyperspectral imaging help detect contamination in metal powder?\",\"answer\":\"Hyperspectral imaging captures spectral data across a wide frequency range together with spatial information, enabling detection of subtle temperature, moisture, and chemical-composition differences.\"},{\"question\":\"What are the main components of the proposed machine-learning approach?\",\"answer\":\"The paper demonstrates solutions through three case studies: building a spectral dictionary, conducting contamination detection, and performing band selection analysis.\"}]","Nondestructive Quantitative Measurement for Precision Quality Control in Additive Manufacturing Using Hyperspectral Imagery and Machine Learning | PDF",1785818587,35,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"nondestructive-quantitative-measurement-for-precision-quality-control-in-additive-manufacturing-using-hyperspectral-imagery-and-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/nondestructive-quantitative-measurement-for-precision-quality-control-in-additive-manufacturing-using-hyperspectral-imagery-and-machine-learning/123792/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is measuring metal powder purity important in additive manufacturing?","Question",{"text":75,"@type":76},"Purity directly affects the quality of printed products. Contamination can lead to cracks and malfunctions, harming strength and reliability.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does hyperspectral imaging help detect contamination in metal powder?",{"text":80,"@type":76},"Hyperspectral imaging captures spectral data across a wide frequency range together with spatial information, enabling detection of subtle temperature, moisture, and chemical-composition differences.",{"name":82,"@type":73,"acceptedAnswer":83},"What are the main components of the proposed machine-learning approach?",{"text":84,"@type":76},"The paper demonstrates solutions through three case studies: building a spectral dictionary, conducting contamination detection, and performing band selection analysis.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]