[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122381-en":3,"doc-seo-122381-105":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":4,"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},122381,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Machine Learning in Thermography Non-Destructive Testing - A Systematic Review","This paper reviews recent advances in machine learning algorithms used to improve postprocessing and interpretation of thermographic data in non-destructive testing. Thermographic methods such as pulsed thermography and laser thermography generate large thermal datasets that are difficult to analyze accurately and efficiently. The review explains how ML supports faster defect detection and automated classification, summarizes widely used algorithms, and evaluates limitations in existing workflows. A structured analysis clarifies how artificial intelligence can enhance defect characterization for industrial applications.","Review  \nMachine Learning in Thermography Non-Destructive Testing: A Systematic Review  \nShaoyang Peng *, Sri Addepalli † and Maryam Farsi †  \nAcademic Editors: Evangelos Hristoforou and Victor Giurgiutiu  \nReceived: 6 May 2025  \nRevised: 19 July 2025  \nAccepted: 22 August 2025  \nPublished: 1 September 2025  \nCitation: Peng, S.; Addepalli, S.; Farsi, M. Machine Learning in Thermography Non-Destructive Testing: A Systematic Review. Appl.  \nSci. 2025, 15, 9624. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/app15179624](10.3390/app15179624)  \nCopyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) .  \nCentre for Digital and Design Engineering, Cran􀀂eld University, Bedford MK43 0AL, UK;  \np.n.addepalli@cran􀀂eld.ac.uk (S.A.); maryam.farsi@cran􀀂eld.ac.uk (M.F.)  \n* Correspondence: shaoyang.peng.875@cran􀀂eld.ac.uk † These authors contributed equally to this work.  \nAbstract  \nThis paper reviews recent advances in machine learning (ML) algorithms to improve the postprocessing and interpretation of thermographic data in non-destructive testing (NDT) . While traditional NDT methods (e.g., visual inspection, ultrasonic testing) each have their own advantages and limitations, thermographic techniques (e.g., pulsed thermography, laser thermography) have become valuable complementary tools, particularly in inspecting advanced materials such as carbon 􀀂ber-reinforced polymers (CFRPs) and superalloys. These techniques generate large volumes of thermal data, which can be challenging to analyze ef􀀂ciently and accurately. This review focuses on how ML can accelerate defect detection and automated classi􀀂cation in thermographic NDT. We summarize currently popular algorithms and analyze the limitations of existing work􀀃ows. Furthermore, this structured analysis provides an in-depth understanding of how arti􀀂cial intelligence can assist in processing NDT data, with the potential to enable more accurate defect detection and characterization in industrial applications.  \nKeywords: machine learning; neural network algorithm; non-destructive testing; systematic review  \n1. Introduction  \nThe proliferation of advanced manufacturing techniques, such as additive manufacturing and 􀀂ber-reinforced composites, has reshaped key industries such as aerospace while simultaneously introducing new challenges for inspection and validation. Traditional visual inspections, once suf􀀂cient to identify surface defects, have proven inadequate to assess the internal integrity of increasingly complex structures. As a result, non-destructive testing (NDT) technologies, including ultrasonic testing (UT) and infrared thermography (IRT), have seen widespread adoption in detecting subsurface anomalies via changes in physical properties such as heat, sound, and magnetism [1] .  \nAmong these, thermography stands out for its non-contact, real-time imaging capabilities, large-area coverage, and minimal operational disruption. Thermographic inspection is generally classi􀀂ed as passive, relying on natural thermal emissions, or active, where external stimuli such as 􀀃ash or laser pulses are applied to induce measurable thermal responses. Active thermography, including pulsed and laser-based methods, has shown strong performance in the evaluation of carbon 􀀂ber-reinforced polymer (CFRP) materials [2] .  \nAli et al. [3] have provided a comprehensive overview of the primary categories of active thermographic techniques currently used in NDT. Based on their summary, six main  \ntypes of active infrared thermography are widely adopted in the literature, each with distinct operational principles and application scopes.  \nEach of these techniques is tailored to speci􀀂c material types, defect depths,","cbCaitgAvRst7dCD","https://ap.wps.com/l/cbCaitgAvRst7dCD","pdf",701479,1,24,"English","en",105,"# Introduction\n## Thermography in Non-Destructive Testing\n## Active Infrared Thermography Types\n## Mapping Active IRT Methods to ML Algorithms","[{\"question\":\"为什么热成像在无损检测中具有优势？\",\"answer\":\"热成像具备非接触、实时成像、覆盖面积大且对操作干扰较小，能通过热响应反映缺陷相关的物理特性变化。文中同时区分了被动与主动热成像方式。\"},{\"question\":\"该综述重点关注机器学习如何用于热成像数据处理？\",\"answer\":\"综述聚焦ML在热成像NDT中的后处理与解释，强调其用于加速缺陷检测、支持自动分类，并总结当前常用算法及现有流程的局限。\"},{\"question\":\"文章如何组织主动红外热成像方法及其对应的ML算法？\",\"answer\":\"文中先介绍主动红外热成像的分类框架，再用表格汇总常见方法（如锁相、脉冲、频率调制、脉冲相位等）并给出代表性的ML算法对应关系。\"}]","Machine Learning in Thermography Non-Destructive Testing - A Systematic Review | PDF",1785810338,60,{"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},"machine-learning-in-thermography-non-destructive-testing-a-systematic-review","",{"@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/machine-learning-in-thermography-non-destructive-testing-a-systematic-review/122381/",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":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"为什么热成像在无损检测中具有优势？","Question",{"text":75,"@type":76},"热成像具备非接触、实时成像、覆盖面积大且对操作干扰较小，能通过热响应反映缺陷相关的物理特性变化。文中同时区分了被动与主动热成像方式。","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"该综述重点关注机器学习如何用于热成像数据处理？",{"text":80,"@type":76},"综述聚焦ML在热成像NDT中的后处理与解释，强调其用于加速缺陷检测、支持自动分类，并总结当前常用算法及现有流程的局限。",{"name":82,"@type":73,"acceptedAnswer":83},"文章如何组织主动红外热成像方法及其对应的ML算法？",{"text":84,"@type":76},"文中先介绍主动红外热成像的分类框架，再用表格汇总常见方法（如锁相、脉冲、频率调制、脉冲相位等）并给出代表性的ML算法对应关系。","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},[93,97,101,105,109,114,119,122,127,130,134],{"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":29,"slug":108},5,"Comic","comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]