Jin Young Choi , Sina Malakpour Estalaki , Daniel Quispe , Rujing Zha , Rowan Rolark , Mojtaba Mozaffar , Jian Cao
{"title":"迁移学习使几何、工艺和材料不可知的RGNN用于定向能沉积的温度预测","authors":"Jin Young Choi , Sina Malakpour Estalaki , Daniel Quispe , Rujing Zha , Rowan Rolark , Mojtaba Mozaffar , Jian Cao","doi":"10.1016/j.addma.2025.104876","DOIUrl":null,"url":null,"abstract":"<div><div>Thermal simulation in directed energy deposition is an area of active interest, as the evolution of the temperature field during deposition is a key variable that affects resulting build properties. Surrogate machine learning models enable an accelerated alternative to finite element analysis, but often face challenges in generalizing new inference conditions. In this work, we present a geometry- and process-agnostic recurrent graph neural network (RGNN) that preserves the mesh connectivity through graph nodes and edges. Compared to finite element analysis, the RGNN model provides thermal predictions up to 405 times faster in computational speed. Furthermore, we demonstrate the effectiveness of transfer learning (TL) via fine-tuning to adapt the pretrained model for a new material system with distinct heat transfer characteristics. We evaluate the TL model in a challenging scenario, where it successfully predicts thermal behavior over an extended 1000 time steps using a new geometry. The TL model exhibits much lower error accumulation over time compared to the pretrained model, while requiring only a fraction of training data and training time. The TL predictions show a good match with an experimental temperature field obtained from IR imaging, demonstrating its robustness and adaptability.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"109 ","pages":"Article 104876"},"PeriodicalIF":11.1000,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Transfer learning enabled geometry, process, and material agnostic RGNN for temperature prediction in directed energy deposition\",\"authors\":\"Jin Young Choi , Sina Malakpour Estalaki , Daniel Quispe , Rujing Zha , Rowan Rolark , Mojtaba Mozaffar , Jian Cao\",\"doi\":\"10.1016/j.addma.2025.104876\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Thermal simulation in directed energy deposition is an area of active interest, as the evolution of the temperature field during deposition is a key variable that affects resulting build properties. Surrogate machine learning models enable an accelerated alternative to finite element analysis, but often face challenges in generalizing new inference conditions. In this work, we present a geometry- and process-agnostic recurrent graph neural network (RGNN) that preserves the mesh connectivity through graph nodes and edges. Compared to finite element analysis, the RGNN model provides thermal predictions up to 405 times faster in computational speed. Furthermore, we demonstrate the effectiveness of transfer learning (TL) via fine-tuning to adapt the pretrained model for a new material system with distinct heat transfer characteristics. We evaluate the TL model in a challenging scenario, where it successfully predicts thermal behavior over an extended 1000 time steps using a new geometry. The TL model exhibits much lower error accumulation over time compared to the pretrained model, while requiring only a fraction of training data and training time. The TL predictions show a good match with an experimental temperature field obtained from IR imaging, demonstrating its robustness and adaptability.</div></div>\",\"PeriodicalId\":7172,\"journal\":{\"name\":\"Additive manufacturing\",\"volume\":\"109 \",\"pages\":\"Article 104876\"},\"PeriodicalIF\":11.1000,\"publicationDate\":\"2025-07-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Additive manufacturing\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2214860425002404\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, MANUFACTURING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Additive manufacturing","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2214860425002404","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MANUFACTURING","Score":null,"Total":0}
Transfer learning enabled geometry, process, and material agnostic RGNN for temperature prediction in directed energy deposition
Thermal simulation in directed energy deposition is an area of active interest, as the evolution of the temperature field during deposition is a key variable that affects resulting build properties. Surrogate machine learning models enable an accelerated alternative to finite element analysis, but often face challenges in generalizing new inference conditions. In this work, we present a geometry- and process-agnostic recurrent graph neural network (RGNN) that preserves the mesh connectivity through graph nodes and edges. Compared to finite element analysis, the RGNN model provides thermal predictions up to 405 times faster in computational speed. Furthermore, we demonstrate the effectiveness of transfer learning (TL) via fine-tuning to adapt the pretrained model for a new material system with distinct heat transfer characteristics. We evaluate the TL model in a challenging scenario, where it successfully predicts thermal behavior over an extended 1000 time steps using a new geometry. The TL model exhibits much lower error accumulation over time compared to the pretrained model, while requiring only a fraction of training data and training time. The TL predictions show a good match with an experimental temperature field obtained from IR imaging, demonstrating its robustness and adaptability.
期刊介绍:
Additive Manufacturing stands as a peer-reviewed journal dedicated to delivering high-quality research papers and reviews in the field of additive manufacturing, serving both academia and industry leaders. The journal's objective is to recognize the innovative essence of additive manufacturing and its diverse applications, providing a comprehensive overview of current developments and future prospects.
The transformative potential of additive manufacturing technologies in product design and manufacturing is poised to disrupt traditional approaches. In response to this paradigm shift, a distinctive and comprehensive publication outlet was essential. Additive Manufacturing fulfills this need, offering a platform for engineers, materials scientists, and practitioners across academia and various industries to document and share innovations in these evolving technologies.