Intelligent Design of Squeeze Casting Gating System Based on Enhanced Conditional Generative Adversarial Networks and Hypernetworks

Intelligent Design of Squeeze Casting Gating System Based on Enhanced Conditional Generative Adversarial Networks and Hypernetworks | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Intelligent Design of Squeeze Casting Gating System Based on Enhanced Conditional Generative Adversarial Networks and Hypernetworks Yun Chen, Ziqi Wang, Jubao Gao, Kaixiong Hu, Lingyu Guo, Qing Teng, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7817347/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Squeeze casting, a hybrid metal forming process that combines the benefits of forging and casting, is widely used in automotive, aerospace, and related industries. Traditional design methods for squeeze casting molds rely heavily on engineers' experience, making it difficult to meet the growing demand for high efficiency and quality. To address these challenges, this study develops an AI-driven design platform for squeeze casting molds, focusing on the gating system and leveraging Siemens NX. A grid projection method is introduced to efficiently characterize part features with minimal data, while parametric gating system modeling is achieved through guide curve techniques. By integrating an enhanced conditional generative adversarial network (ECGAN) with a hypernetwork-based variable structure strategy, sample similarity is improved from 44.3% to 68.4%. The framework combines sample library construction, training parameter optimization, and NX secondary development, unifying grid projection, intelligent algorithms, and guide curve methods. Experimental validation using a new energy vehicle bracket and runner plate confirms the system's effectiveness. It not only generates high-quality solutions for simple parts but also provides valuable design guidance for complex geometries. Squeeze casting Casting system Generative adversarial network Hypernetwork NX secondary development Full Text Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major Revisions Needed 21 Jan, 2026 Reviewers agreed at journal 08 Dec, 2025 Reviewers invited by journal 08 Dec, 2025 Editor assigned by journal 13 Oct, 2025 First submitted to journal 10 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7817347","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":557025516,"identity":"981b1604-fb02-4ab8-954d-de8c2da7c732","order_by":0,"name":"Yun Chen","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yun","middleName":"","lastName":"Chen","suffix":""},{"id":557025517,"identity":"7fb442d0-1415-4228-b803-f55d8303e2d2","order_by":1,"name":"Ziqi 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Traditional design methods for squeeze casting molds rely heavily on engineers' experience, making it difficult to meet the growing demand for high efficiency and quality. To address these challenges, this study develops an AI-driven design platform for squeeze casting molds, focusing on the gating system and leveraging Siemens NX. A grid projection method is introduced to efficiently characterize part features with minimal data, while parametric gating system modeling is achieved through guide curve techniques. By integrating an enhanced conditional generative adversarial network (ECGAN) with a hypernetwork-based variable structure strategy, sample similarity is improved from 44.3% to 68.4%. The framework combines sample library construction, training parameter optimization, and NX secondary development, unifying grid projection, intelligent algorithms, and guide curve methods. Experimental validation using a new energy vehicle bracket and runner plate confirms the system's effectiveness. It not only generates high-quality solutions for simple parts but also provides valuable design guidance for complex geometries.\u003c/p\u003e","manuscriptTitle":"Intelligent Design of Squeeze Casting Gating System Based on Enhanced Conditional Generative Adversarial Networks and Hypernetworks","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-11 05:32:10","doi":"10.21203/rs.3.rs-7817347/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revisions Needed","date":"2026-01-21T07:35:46+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-12-09T01:45:36+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-08T13:51:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-14T01:36:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"The International Journal of Advanced Manufacturing Technology","date":"2025-10-11T03:43:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"the-international-journal-of-advanced-manufacturing-technology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jamt","sideBox":"Learn more about [The International Journal of Advanced Manufacturing Technology](https://www.springer.com/journal/170)","snPcode":"170","submissionUrl":"https://submission.nature.com/new-submission/170/3","title":"The International Journal of Advanced Manufacturing Technology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"8d557444-40ee-421c-8158-044f61934b98","owner":[],"postedDate":"December 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-29T06:47:53+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-11 05:32:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7817347","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7817347","identity":"rs-7817347","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}