[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127096-en":3,"doc-seo-127096-105":30,"detail-sidebar-cat-0-en-105":95},{"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},127096,5909887256941,"Levi","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Efficient analysis of composites manufacturing using multi-fidelity simulation and probabilistic machine learning","This paper presents an efficient framework to analyze composites manufacturing processes and resulting phenomena by combining low- and high-fidelity simulations with limited experimental data. Spatially Weighted Gaussian Process Regression (SWGPR) is used to build surrogate machine-learning models with a multi-scale, datapoint-dependent uncertainty structure by assigning noise levels to simulation points. The approach is validated by predicting process-induced deformations for L-shaped cross-ply laminates using minimal experimental effort. The goal is a cost-effective, broadly applicable method to understand and improve composites design, development, and manufacturing.","POLITECNICO DI TORINO  \nRepository ISTITUZIONALE  \nEfficient analysis of composites manufacturing using multi-fidelity simulation and probabilistic machine learning  \nOriginal  \nEfficient analysis of composites manufacturing using multi-fidelity simulation and probabilistic machine learning / Schoenholz, C. ; Zappino, E. ; Petrolo, M. ; Zobeiry, N.. -In: COMPOSITES. PART B, ENGINEERING. -ISSN 1359-8368.  \n-ELETTRONICO. -280:(2024) . [10 . 1016/j.compositesb.2024. 111499]  \nAvailability:  \nThis version is available at: 11583/2988922 since: 2024-05-22T13:42:45Z  \nPublisher: Elsevier  \nPublished  \nDOI:10.1016/j.compositesb.2024.111499  \nTerms of use:  \nThis article is made available under terms and conditions as specified in the corresponding bibliographic description in the repository  \nPublisher copyright  \nElsevier preprint/submitted version  \nPreprint (submitted version) of an article published in COMPOSITES. PART B, ENGINEERING © 2024, [http://doi.org/10.1016/j.compositesb.2024.111499](http://doi.org/10.1016/j.compositesb.2024.111499)  \n(Article begins on next page)  \n27 March 2025  \nEfficient Analysis of Composites Manufacturing Using Multi-fidelity Simulation and  \nProbabilistic Machine Learning  \nCaleb Schoenholz a, Enrico Zappino b, Marco Petrolo b, Navid Zobeiry a,*  \naMaterials Science & Engineering Department, University of Washington  \n302 Roberts Hall, Box 352120  \nSeattle, WA, 98195-2120, U.S.A.  \nb MUL2 Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino  \nCorso Duca degli Abruzzi 24  \n10129 Torino, Italy  \n* Corresponding author, [navidz@uw.edu](navidz@uw.edu)  \nAbstract  \nThis paper introduces an innovative approach for the efficient analysis of composites manufacturing processes and phenomena. The method combines low- and high-fidelity simulation schemes with limited amounts of experimental data to train surrogate machine learning (ML) models. Guided by a novel approach, Spatially Weighted Gaussian Process Regression (SWGPR), a predictive model is efficiently constructed and calibrated by assigning datapoint-dependent noise levels to simulation points, establishing a multi-scale data-driven uncertainty structure. This study demonstrates the effectiveness of the method inaccurately predicting process-induced deformations (PIDs) for L-shaped cross-ply laminates using minimal experimental efforts. The presented method aims to provide a cost-effective and broadly applicable framework for understanding and improving the design, development, and manufacturing of composites.  \nKeywords: A. Polymer-matrix composites (PMCs), B. Residual/internal stress, C. Statistical properties/methods, E. Autoclave, OTHER: Probabilistic machine learning  \n1. Introduction  \nWhile carbon fiber-reinforced polymer (CFRP) composites are widely utilized in the aerospace industry, manufacturers continue to face several challenges, one of which is mitigating process-induced deformations (PIDs), which significantly affect assembly of aerostructure timelines [1–3]. During high-temperature/pressure processing (e.g., autoclave) of composites, residual stresses develop due to complex, interdependent, and multi-scale phenomena [1] . Upon demolding, PIDs such as variations in a part’s enclosed angles (e.g., spring-in) or warpage of initially flat sections, may emerge to alleviate some stresses [4] . Figure 1 schematically illustrates these PIDs for an L-shaped part, a common geometry utilized for composite aerostructure components. Both spring-in and warpage can lead to assembly joining gaps, extended production timelines and costs, and compromised mechanical performance in the final structure [5] .  \nAlthough PIDs are recognized among manufacturers, minimizing undesired deformations via process (e.g., cure cycle) optimization remains challenging. These difficulties primarily stem from limitations of traditional methods used for manufacturing analyses and PID predictions, typically classified into three categories, as depi","cbCaihrm1JUlDcid","https://ap.wps.com/l/cbCaihrm1JUlDcid","pdf",1262415,1,21,"English","en",105,"# Abstract\n# 1. Introduction\n## Process-induced deformations and manufacturing challenges\n## Limitations and trade-offs of existing analysis methods\n# Keywords","[{\"question\":\"What problem does the paper address in composites manufacturing?\",\"answer\":\"Manufacturers need to mitigate process-induced deformations that arise during high-temperature/pressure processing, since these deformations impact assembly timelines, costs, and mechanical performance.\"},{\"question\":\"How does the proposed method combine simulations and experimental data?\",\"answer\":\"It merges low- and high-fidelity simulation schemes with limited experimental data to train surrogate machine-learning models, rather than relying on large experimental campaigns.\"},{\"question\":\"What is SWGPR and how does it improve uncertainty handling?\",\"answer\":\"SWGPR constructs a predictive model by assigning datapoint-dependent noise levels to simulation points, creating a multi-scale data-driven uncertainty structure across fidelities.\"},{\"question\":\"What does the paper demonstrate as a validation case?\",\"answer\":\"It demonstrates effectiveness at predicting process-induced deformations for L-shaped cross-ply laminates using minimal experimental effort.\"}]","Efficient analysis of composites manufacturing using multi-fidelity simulation and probabilistic machine learning | 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problem does the paper address in composites manufacturing?","Question",{"text":75,"@type":76},"Manufacturers need to mitigate process-induced deformations that arise during high-temperature/pressure processing, since these deformations impact assembly timelines, costs, and mechanical performance.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed method combine simulations and experimental data?",{"text":80,"@type":76},"It merges low- and high-fidelity simulation schemes with limited experimental data to train surrogate machine-learning models, rather than relying on large experimental campaigns.",{"name":82,"@type":73,"acceptedAnswer":83},"What is SWGPR and how does it improve uncertainty handling?",{"text":84,"@type":76},"SWGPR constructs a predictive model by assigning datapoint-dependent noise levels to simulation points, creating a multi-scale data-driven uncertainty structure across fidelities.",{"name":86,"@type":73,"acceptedAnswer":87},"What does the 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