[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125536-en":3,"doc-seo-125536-105":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},125536,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Automatic Dispersion, Defect, Curing, and Thermal Characteristics Determination of Polymer Composites using Micro-Scale Infrared Thermography and Machine Learning Algorithm","Infrared thermography enables non-destructive evaluation of polymer composites by leveraging changes in emissivity and thermal diffusivity to infer defects, components, and curing state, but manual thermal-image processing can introduce artifacts and lead to erroneous determinations. This study applies an automatic machine-learning model to thermal images of graphite/graphene-based polymer composites produced by hand, planetary, and batch mixing. Filler dispersion is quantified with ~20 µm resolution despite imaging artifacts, and curing time differences are characterized via thermal characteristic curves to compare composite quality.","University of Texas Rio Grande Valley  \nScholarWorks @ UTRGV  \n\n| Mechanical Engineering Faculty Publications and Presentations | College of Engineering and Computer Science |\n| --- | --- |\n\n11-18-2022  \nAutomatic Dispersion, Defect, Curing, and Thermal Characteristics Determination of Polymer Composites using Micro-Scale Infrared Thermography and Machine Learning Algorithm  \nMd Ashiqur Rahman Mirza Masfiqur Rahman Ali Ashraf  \nFollow this and additional works at: [https://scholarworks.utrgv.edu/me_fac](https://scholarworks.utrgv.edu/me_fac)  \n Part of the Mechanical Engineering Commons  \nThe University of Texas Rio Grande Valley  \nPurdue University West Lafayette  \n 􀂦   \nThe University of Texas Rio Grande Valley  \nNovember 18th, 2022  \n [https://doi.org/10.21203/rs.3.rs-2265045/v1](https://doi.org/10.21203/rs.3.rs-2265045/v1)  \n 􀁱 􀅏 This work is licensed under a Creative Commons Attribution 4.0 International License.  \nRead Full License  \nInfrared thermography is a non-destructive technique that can be exploited in many ¦elds including polymer composite investigation. Based on emissivity and thermal diffusivity variation, components, defects, and curing state of the composite can be identi¦ed. However, manual processing of thermal images that may contain signi¦cant artifacts, is prone to erroneous component and property determination. In this study, thermal images of different graphite/graphene-based polymer composites fabricated by hand, planetary, and batch mixing techniques were analyzed through an automatic machine learning model. Filler size, shape, and location can be identi¦ed in polymer composites and thus, the dispersion of different samples was quanti¦ed with a resolution of ~ 20 µm despite having artifacts in the thermal image. Thermal diffusivity comparison of three mixing techniques was performed for 40% graphite in the elastomer. Batch mixing demonstrated superior dispersion than planetary and hand mixing as the dispersion index (DI) for batch mixing was 0.07 while planetary and hand mixing showed 0.0865 and 0.163 respectively. Curing was investigated for a polymer with different ¦llers (PDMS took 500s while PDMS-Graphene and PDMS Graphite Powder took 800s to cure), and a thermal characteristic curve was generated to compare the composite quality. Therefore, the above-mentioned methods with machine learning algorithms can be a great tool to analyze composite both quantitatively and qualitatively.  \nPolymer composites are comprised of two or more materials (matrix and ¦ller/reinforcing/additive materials) that have properties that are superior to the properties of the individual materials1–3. Because of its synergistic properties and applications in aerospace, automotive, maritime, energy, and consumer ¦elds, it has attracted the interest of both industry and academia4–8. Among all the ¦llers or reinforcing materials, graphite or graphene has become an ideal candidate due to its exceptional mechanical, thermal, or electrical properties. Thus, graphene-based polymer composites have captured the scienti¦ccommunity's interest during the past few decades.  \nThe properties of polymer composites largely depend on the dispersion of ¦ller materials on the polymer matrix. Thus, the performance of a polymer composite (poor or good) is determined directly by the degree of agglomeration, which can lead to property variation over the composite. The study of particle/loading size, shape, and size can be accomplished using transmission electron microscopy (TEM) 9, but it is restricted to relatively smaller samples. Scanning electron microscopy can be another technique to determine dispersion, and Fu et al. calculated the carbon nanotube (CNT) dispersion index by dividing the images into grids10. The majority of TEM and SEM procedures, which are expensive and require a complex sample preparation process (sample preparation might be destructive), are employed to estimate the dispersion of low-weight percentage of ¦ller materials ","cbCaimqBGWrv0YeT","https://ap.wps.com/l/cbCaimqBGWrv0YeT","pdf",2094933,1,19,"English","en",105,"# Automatic machine learning analysis of micro-scale infrared thermography\n## Dispersion quantification from thermal images\n## Mixing-technique comparison via thermal diffusivity\n## Curing investigation and thermal characteristic curves\n## Context: nondestructive evaluation needs for composites","[{\"question\":\"How does the approach determine dispersion in polymer composites?\",\"answer\":\"Thermal images are analyzed using an automatic machine-learning model to identify filler size, shape, and location, enabling quantification of dispersion despite artifacts in the images.\"},{\"question\":\"What does the study compare between mixing techniques?\",\"answer\":\"It compares thermal diffusivity across samples made with hand, planetary, and batch mixing for a 40% graphite elastomer, using a dispersion index to assess which technique yields better dispersion.\"},{\"question\":\"How is curing quality evaluated?\",\"answer\":\"Curing is investigated by measuring different curing times for PDMS-based composites with various fillers and generating a thermal characteristic curve to compare composite quality.\"}]","Automatic Dispersion, Defect, Curing, and Thermal Characteristics Determination of Polymer Composites using Micro-Scale Infrared Thermography and Machine Learning Algorithm | 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does the approach determine dispersion in polymer composites?","Question",{"text":75,"@type":76},"Thermal images are analyzed using an automatic machine-learning model to identify filler size, shape, and location, enabling quantification of dispersion despite artifacts in the images.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What does the study compare between mixing techniques?",{"text":80,"@type":76},"It compares thermal diffusivity across samples made with hand, planetary, and batch mixing for a 40% graphite elastomer, using a dispersion index to assess which technique yields better dispersion.",{"name":82,"@type":73,"acceptedAnswer":83},"How is curing quality evaluated?",{"text":84,"@type":76},"Curing is investigated by measuring different curing times for PDMS-based composites with various fillers and generating a thermal characteristic curve to compare composite 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