[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119508-en":3,"doc-seo-119508-105":30,"detail-sidebar-cat-0-en-105":90},{"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},119508,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Uncertainty Quantification in Numerical Modelling of Polymeric Foam Composites Assisted by Machine Learning","The study addresses uncertainty in polymeric foam composites reinforced with natural fibres by combining Finite Element Analysis (FEA) and Machine Learning (ML). Chaotically distributed fibres and randomly varying pore sizes and shapes create variability that is handled using an Interval Field (IF) approach and a generated Representative Volume Element (RVE) to support homogenization. FEA predictions are validated against existing experimental data, then the validated results with satisfactory R-squared values become training inputs for the ML model. The integrated workflow reduces reliance on extensive experiments and lowers computational cost while enabling analysis and optimisation of polymeric foam composites.","Compilation of Abstracts from the  \n8th International Symposium on DamageMechanics of Materials and Structures(SDMMS2025)  \n9-11th September 2025Selangor,Malaysia  \nPaper ID:53  \n# Uncertainty Quantification in Numerical Modelling of PolymericFoam Composites Assisted by Machine Learning\n\nS.KAMIL\",MS.ZM SUFFLAN,AK ARIFFLN  \nDepartment of Mechanical Engineering Faculty ofScience and Technology.Universitas Samucza,KotaLangra,Aceh,Incionesia  \nDeparzonent of Mechanical and Manufacturing Engineering,Faculty ofEngineering,Unnersi MalaysiaSarawak,Kota Samarahan,Sarawak,Malaysia  \nDepartment of Mechanical and Manuyfacturing Engineering,Facuity ofEngineering and BuiltEvinonment,Universiti Kebangsaan Malaysia,Bangi,Selangor,Malaysia  \n*Tal:+6285270950424Emall:syahirkamil@unsam.ac.id  \n## ABSTRACT\n\nThe use ofnatural fibres to reinforce composites is becoming increasingly widespread,dniven by the growing demandfor renewable and sustainable material solutions.However,as composite structures become more complex,the needfor accurate and reliable numerical modelling techniques becomes increasingly critical to predict their behavioureffectively.In this study,a combination of Finite Element Analysis(FEA)and Machine Leaming(ML)is employedto numerically model and predict the material properties of a composite based on a polyester resin matrix reinforcedwith vanous fillers,inchuding bagasse fibres and oil palm empty fruit bunch(OPEFB)fibres.The polymenic foamcomposite contains chaotically distributed fibres and randomly varying pore sizes and shapes.To address thisuncertainty,an Interval Field(IF)approach is employed.ARepresentative Volume Element(RVE)of the compositestructure is generated uing the integrated FEAand IF approach to facilitate the homogenization procedure.The FEAresults are compared and validated against previousły obtained experimental data.Once satisfactory R-squared valuesare achieved,the validated data are used as training input for the Machine Learning modelThe integration of MachineLearming into numerical modelling reduces the need for extensive experimental testing and helps minimisecomputational costs.This study highlights the potential of Machine Learning in the analysis and optimisation ofpolymeric foam composites.","cbCaioKbjm2y3iBq","https://ap.wps.com/l/cbCaioKbjm2y3iBq","pdf",114671,1,2,"English","en",105,"# Abstract\n## Composite modelling approach (FEA + IF)\n## Representative Volume Element and homogenization\n## Validation and machine learning integration\n## Benefits for optimisation and cost reduction","[{\"question\":\"What sources of uncertainty are considered in the polymeric foam composite modelling?\",\"answer\":\"The modelling accounts for chaotically distributed fibres and randomly varying pore sizes and shapes, which introduce uncertainty in predicted behaviour and material properties.\"},{\"question\":\"How does the study quantify and manage uncertainty in the numerical model?\",\"answer\":\"An Interval Field (IF) approach is employed to represent uncertainty, supported by a Representative Volume Element (RVE) generated using an integrated FEA and IF workflow for homogenization.\"},{\"question\":\"How is machine learning used after validating the numerical results?\",\"answer\":\"After FEA outputs are validated against experimental data and satisfactory R-squared values are obtained, the validated results are used as training inputs for the machine learning model to support prediction and analysis.\"}]","Uncertainty Quantification in Numerical Modelling of Polymeric Foam Composites Assisted by Machine Learning | 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sources of uncertainty are considered in the polymeric foam composite modelling?","Question",{"text":74,"@type":75},"The modelling accounts for chaotically distributed fibres and randomly varying pore sizes and shapes, which introduce uncertainty in predicted behaviour and material properties.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does the study quantify and manage uncertainty in the numerical model?",{"text":79,"@type":75},"An Interval Field (IF) approach is employed to represent uncertainty, supported by a Representative Volume Element (RVE) generated using an integrated FEA and IF workflow for homogenization.",{"name":81,"@type":72,"acceptedAnswer":82},"How is machine learning used after validating the numerical results?",{"text":83,"@type":75},"After FEA outputs are validated against experimental data and satisfactory R-squared values are obtained, the validated results are used as training inputs for the machine learning model to support prediction and 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