{"title":"结构单元在变载荷条件下的参数化有限元分析模型","authors":"Xiaoqin Wang, Zhuming Bi","doi":"10.1049/cim2.70045","DOIUrl":null,"url":null,"abstract":"<p>The value of a product relies greatly on the significance of the innovations when a product is designed and manufactured. Whereas numerous <i>computer aided design</i> (CAD) tools, such as <i>computer aided engineering</i> (CAE) and <i>artificial intelligence</i> (AI) tools, are widely used to create and accelerate the innovations in engineering design, commercially available AI tools sacrifice the efficiency for generality in virtual design. Because the behaviours of a physical model must be interpreted as governing mathematical models, this may ignore key analytical correspondence of inputs and outputs in the physical model. Design optimisation is simulation based with a limited exploration of a design space. We argue that for innovations such as routine designs or parametric designs that are rooted in <i>knowledge-based engineering</i> (KBE), a sophisticated tool, rather than a general-purpose CAE tool, should be developed to optimise a design solution analytically. To illustrate the feasibility and effectiveness of the proposed idea, a parametric FEA model was developed for a client company in construction. The model is programed and implemented, its conciseness, efficiency and accuracy was proven by comparative studies with SolidWorks simulation. It was recommended and used by the client company for practical use.</p>","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"7 1","pages":""},"PeriodicalIF":3.1000,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.70045","citationCount":"0","resultStr":"{\"title\":\"A Parametric FEA Model for Evaluation of Structural Elements Subjected to Varying Loading Conditions\",\"authors\":\"Xiaoqin Wang, Zhuming Bi\",\"doi\":\"10.1049/cim2.70045\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The value of a product relies greatly on the significance of the innovations when a product is designed and manufactured. Whereas numerous <i>computer aided design</i> (CAD) tools, such as <i>computer aided engineering</i> (CAE) and <i>artificial intelligence</i> (AI) tools, are widely used to create and accelerate the innovations in engineering design, commercially available AI tools sacrifice the efficiency for generality in virtual design. Because the behaviours of a physical model must be interpreted as governing mathematical models, this may ignore key analytical correspondence of inputs and outputs in the physical model. Design optimisation is simulation based with a limited exploration of a design space. We argue that for innovations such as routine designs or parametric designs that are rooted in <i>knowledge-based engineering</i> (KBE), a sophisticated tool, rather than a general-purpose CAE tool, should be developed to optimise a design solution analytically. To illustrate the feasibility and effectiveness of the proposed idea, a parametric FEA model was developed for a client company in construction. The model is programed and implemented, its conciseness, efficiency and accuracy was proven by comparative studies with SolidWorks simulation. It was recommended and used by the client company for practical use.</p>\",\"PeriodicalId\":33286,\"journal\":{\"name\":\"IET Collaborative Intelligent Manufacturing\",\"volume\":\"7 1\",\"pages\":\"\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2025-09-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.70045\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IET Collaborative Intelligent Manufacturing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/cim2.70045\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, INDUSTRIAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Collaborative Intelligent Manufacturing","FirstCategoryId":"1085","ListUrlMain":"https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/cim2.70045","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
A Parametric FEA Model for Evaluation of Structural Elements Subjected to Varying Loading Conditions
The value of a product relies greatly on the significance of the innovations when a product is designed and manufactured. Whereas numerous computer aided design (CAD) tools, such as computer aided engineering (CAE) and artificial intelligence (AI) tools, are widely used to create and accelerate the innovations in engineering design, commercially available AI tools sacrifice the efficiency for generality in virtual design. Because the behaviours of a physical model must be interpreted as governing mathematical models, this may ignore key analytical correspondence of inputs and outputs in the physical model. Design optimisation is simulation based with a limited exploration of a design space. We argue that for innovations such as routine designs or parametric designs that are rooted in knowledge-based engineering (KBE), a sophisticated tool, rather than a general-purpose CAE tool, should be developed to optimise a design solution analytically. To illustrate the feasibility and effectiveness of the proposed idea, a parametric FEA model was developed for a client company in construction. The model is programed and implemented, its conciseness, efficiency and accuracy was proven by comparative studies with SolidWorks simulation. It was recommended and used by the client company for practical use.
期刊介绍:
IET Collaborative Intelligent Manufacturing is a Gold Open Access journal that focuses on the development of efficient and adaptive production and distribution systems. It aims to meet the ever-changing market demands by publishing original research on methodologies and techniques for the application of intelligence, data science, and emerging information and communication technologies in various aspects of manufacturing, such as design, modeling, simulation, planning, and optimization of products, processes, production, and assembly.
The journal is indexed in COMPENDEX (Elsevier), Directory of Open Access Journals (DOAJ), Emerging Sources Citation Index (Clarivate Analytics), INSPEC (IET), SCOPUS (Elsevier) and Web of Science (Clarivate Analytics).