{"title":"利用有限元建模和双向门控循环单元预测激光定向能沉积的温度变化","authors":"Kai-Xiong Hu, Kai Guo, Wei-Dong Li, Yang-Hui Wang","doi":"10.1007/s40436-024-00511-2","DOIUrl":null,"url":null,"abstract":"<p>In the laser-directed energy deposition (L-DED) process, achieving an efficient temperature evolution prediction of molten pools is critical but challenging. To resolve this issue, this study presents an innovative approach that integrates a high-fidelity finite element (FE) model and an effective machine-learning model. Firstly, a high-fidelity FE model for the L-DED process was developed and subsequently validated through an experimental examination of the cross-sectional geometries of the molten pools and temperature fields of the substrate. Then, a Bi-directional gated recurrent unit (Bi-GRU) was formulated to predict the temperature evolution of the molten pools during L-DED. By training the Bi-GRU model using datasets generated from the FE model, it was deployed to efficiently predict the temperature evolution of the manufactured multi-layer single-bead walls. The results demonstrated that, in terms of the average mean absolute error, this approach outperformed other approaches designed based on the gated recurrent unit (GRU) model, long short-term memory model, and recurrent neural network models by 26.7%, 52.1%, and 65.2%, respectively. The results also showed that the prediction time required by this approach, once trained, was significantly reduced by five orders of magnitude compared with the FE model. Therefore, this approach accurately predicts the temperature evolution of multi-layer single-bead walls in a computationally efficient manner. This approach is a promising solution for supporting the real-time control of the L-DED process in industrial applications.</p>","PeriodicalId":7342,"journal":{"name":"Advances in Manufacturing","volume":null,"pages":null},"PeriodicalIF":4.2000,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Temperature evolution prediction for laser directed energy deposition enabled by finite element modelling and bi-directional gated recurrent unit\",\"authors\":\"Kai-Xiong Hu, Kai Guo, Wei-Dong Li, Yang-Hui Wang\",\"doi\":\"10.1007/s40436-024-00511-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>In the laser-directed energy deposition (L-DED) process, achieving an efficient temperature evolution prediction of molten pools is critical but challenging. 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This approach is a promising solution for supporting the real-time control of the L-DED process in industrial applications.</p>\",\"PeriodicalId\":7342,\"journal\":{\"name\":\"Advances in Manufacturing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.2000,\"publicationDate\":\"2024-08-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advances in Manufacturing\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1007/s40436-024-00511-2\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, MANUFACTURING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Manufacturing","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s40436-024-00511-2","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, MANUFACTURING","Score":null,"Total":0}
引用次数: 0
摘要
在激光直接能量沉积(L-DED)过程中,实现熔池的高效温度演化预测至关重要,但也极具挑战性。为解决这一问题,本研究提出了一种创新方法,将高保真有限元(FE)模型与有效的机器学习模型相结合。首先,开发了 L-DED 工艺的高保真有限元模型,随后通过对熔池横截面几何形状和基底温度场的实验检查进行了验证。然后,制定了一个双向门控循环单元(Bi-GRU)来预测 L-DED 过程中熔池的温度变化。通过使用从 FE 模型生成的数据集训练 Bi-GRU 模型,该模型被用于有效预测制造的多层单珠壁的温度变化。结果表明,就平均绝对误差而言,该方法比基于门控递归单元(GRU)模型、长短期记忆模型和递归神经网络模型设计的其他方法分别高出 26.7%、52.1% 和 65.2%。结果还显示,与 FE 模型相比,该方法训练后所需的预测时间大幅缩短了五个数量级。因此,这种方法能以计算效率高的方式准确预测多层单珠壁的温度演变。这种方法是支持工业应用中 L-DED 过程实时控制的一种有前途的解决方案。
Temperature evolution prediction for laser directed energy deposition enabled by finite element modelling and bi-directional gated recurrent unit
In the laser-directed energy deposition (L-DED) process, achieving an efficient temperature evolution prediction of molten pools is critical but challenging. To resolve this issue, this study presents an innovative approach that integrates a high-fidelity finite element (FE) model and an effective machine-learning model. Firstly, a high-fidelity FE model for the L-DED process was developed and subsequently validated through an experimental examination of the cross-sectional geometries of the molten pools and temperature fields of the substrate. Then, a Bi-directional gated recurrent unit (Bi-GRU) was formulated to predict the temperature evolution of the molten pools during L-DED. By training the Bi-GRU model using datasets generated from the FE model, it was deployed to efficiently predict the temperature evolution of the manufactured multi-layer single-bead walls. The results demonstrated that, in terms of the average mean absolute error, this approach outperformed other approaches designed based on the gated recurrent unit (GRU) model, long short-term memory model, and recurrent neural network models by 26.7%, 52.1%, and 65.2%, respectively. The results also showed that the prediction time required by this approach, once trained, was significantly reduced by five orders of magnitude compared with the FE model. Therefore, this approach accurately predicts the temperature evolution of multi-layer single-bead walls in a computationally efficient manner. This approach is a promising solution for supporting the real-time control of the L-DED process in industrial applications.
期刊介绍:
As an innovative, fundamental and scientific journal, Advances in Manufacturing aims to describe the latest regional and global research results and forefront developments in advanced manufacturing field. As such, it serves as an international platform for academic exchange between experts, scholars and researchers in this field.
All articles in Advances in Manufacturing are peer reviewed. Respected scholars from the fields of advanced manufacturing fields will be invited to write some comments. We also encourage and give priority to research papers that have made major breakthroughs or innovations in the fundamental theory. The targeted fields include: manufacturing automation, mechatronics and robotics, precision manufacturing and control, micro-nano-manufacturing, green manufacturing, design in manufacturing, metallic and nonmetallic materials in manufacturing, metallurgical process, etc. The forms of articles include (but not limited to): academic articles, research reports, and general reviews.