[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122948-en":3,"doc-seo-122948-105":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},122948,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Extending structural optimization capabilities of FEA softs according to machine learning principles","This paper extends structural optimization capabilities of selected finite element analysis (FEA) software, including NASTRAN/PATRAN and MARC-MENTAT, by coupling them with MATLAB code to enable lightweight design guided by machine learning principles. The approach is applied to composite materials featuring a gyroid structure. Results are presented as both theoretically relevant and practically useful, with a focus on accelerating the design cycle for this class of geometries.","Extending structural optimization capabilities of FEA softs according to machine learning principles  \nMircea BOCIOAGA*, 1, Cristian MOISEI1, Octavian NISTOR1, Ciprian BACRIA1,  \nDenise NITESCU1, Daniela BARAN2  \n*Corresponding author  \n1INCAS – National Institute for Aerospace Research “Elie Carafoli”, B-dul Iuliu Maniu 220, Bucharest 061126, Romania, [bocioaga.mircea@incas.ro](bocioaga.mircea@incas.ro) *  \n2Aerospace Consulting,  \nB-dul Iuliu Maniu 220, Bucharest 061126, Romania  \nDOI: 10. 13111/2066-8201.2024.16.2.3  \nReceived: 15 April 2024/ Accepted: 7 May 2024/ Published: June 2024  \nCopyright © 2024. Published by INCAS. This is an “open access” article under the CC BY-NC-ND license ([http://creativecommons.org/licenses/by-nc-nd/4.0/](http://creativecommons.org/licenses/by-nc-nd/4.0/))  \nAbstract: The purpose of this paper is to extend structural optimization capabilities of some FEA softs (NASTRAN/PATRAN, MARC-MENTAT) by coupling them with MATLAB codes in order to develop light weight design based on machine learning principles. We apply these ideas to some composite material with gyroid structure. The obtained results are interesting from both a theoretical and a practical point of view, shortening the design cycle for this type of materials.  \nKey Words: structural optimization, finite element simulations, machine learning, composite materials with gyroid structure, stress analysis  \n1. INTRODUCTION  \nTwo main concepts are used in this paper: design optimisation and machine learning.  \nDesign Optimisation means finding a structural design regarding certain aspects when some structural paramtres ofthe design vary in some imposed limits previosly defined [5, 12] .  \nThe optimisation concept is defined by some constitutive elements [17, 18] :  \n-The objective function. It is a function depending on the structural design parametres and some of the properties of the design object (mass, rigidity, displacements under s specific loads previously defined, stresses) . In an optimisation analysis, the objective function shoudbe maximized or minimized.  \n- Design variables. These are the structural parameters that are modified to obtain a maximum or minimum objective function locus. Design variables are for example: the thickness of plates, the number of layers of a composite material, the elasticity modulus of a material, or the nature of a material.  \n- Constrains of the design variables. These are the limits between which the design variables can fluctuate in order to mantain the structure in a resonable state. For short the  \noptimisation problem is:  \nminimize W with the constraints, cj ≥ 0 j=1,…,m  \nwhere W is the objective function and cj are the constraints applied to the design variables.  \nUsually, in FEA softs the constraints are imposed for stresses, strains, displacements and natural frequencies.  \nFor stresses and strains, the constraints are defined at the element level and for displacements and frequencies the constraints are define at the node level.  \nThe other main concept we take into account is machine learning. The definition of machine learning as in Wikipedia [23] is as follows:  \n“Machine learning(ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions  \nMachine learning approaches have been applied to many fields including large language models, computer vision, speech recognition, email filtering, agriculture, and medicine, where it is too costly to develop algorithms to perform the needed tasks.  \nML is known in its application across to business problems as predictive analytics.  \nAlthough not all machine learning is statistically based, computational statistics is an important source of methods in this field.  \nThe mathematical foundations of ML are provided by mathematical optimization (mathematical programming)”.  \n2. EXTENDING F","cbCaibHszC6Dlq4Z","https://ap.wps.com/l/cbCaibHszC6Dlq4Z","pdf",1225511,1,11,"English","en",105,"# Introduction\n## Design optimisation concepts\n## Machine learning concepts\n# Extending FEA soft optimization possibilities through MATLAB coupling\n## MARC/MATLAB coupling workflow","[{\"question\":\"How does the paper extend optimization capabilities of FEA software?\",\"answer\":\"It couples FEA tools such as MARC (and also NASTRAN/PATRAN) with MATLAB codes to support lightweight structural design driven by machine learning principles.\"},{\"question\":\"What types of optimization settings and constraints are discussed?\",\"answer\":\"The paper describes objective functions, design variables, and constraints, typically imposed on stresses, strains, displacements, and natural frequencies.\"},{\"question\":\"How is MATLAB used together with MARC in the proposed approach?\",\"answer\":\"MATLAB scripts can launch MARC analyses via DOS commands, modify MARC input files by treating specific variables as design variables, and read values from MARC output files to define optimization problems.\"}]","Extending structural optimization capabilities of FEA softs according to machine learning principles | 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does the paper extend optimization capabilities of FEA software?","Question",{"text":75,"@type":76},"It couples FEA tools such as MARC (and also NASTRAN/PATRAN) with MATLAB codes to support lightweight structural design driven by machine learning principles.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What types of optimization settings and constraints are discussed?",{"text":80,"@type":76},"The paper describes objective functions, design variables, and constraints, typically imposed on stresses, strains, displacements, and natural frequencies.",{"name":82,"@type":73,"acceptedAnswer":83},"How is MATLAB used together with MARC in the proposed approach?",{"text":84,"@type":76},"MATLAB scripts can launch MARC analyses via DOS commands, modify MARC input files by treating specific variables as design variables, and read values from MARC output files to define optimization 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