使用 Swin Transformer 集成深度学习框架实时重建铝合金锻造模具的三维温度场

IF 6.1 2区 工程技术 Q2 ENERGY & FUELS
Zeqi Hu , Yitong Wang , Hongwei Qi , Yongshuo She , Zunpeng Lin , Zhili Hu , Lin Hua , Min Wu , Xunpeng Qin
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引用次数: 0

摘要

锻造模具中的温度场分布对质量控制和缺陷预防至关重要,尤其是对铝合金而言。目前的方法仅限于离散点或表面测量,因此实时三维温度场采集具有挑战性。本文提出了一种新型 Swin 变压器集成深度学习框架,用于锻造模具的实时三维温度场重建,开创了变压器架构在物理场预测中的应用。在该框架中,首先进行数值模拟以提供温度演变的基本事实和基本见解,然后利用有限的稀疏热传感器提供校正的实时输入参数。通过将斯温变换器与 U 型编码器-解码器结构相结合,建立了三维温度场重建模型,并利用各种传感器配置、初始化方法和数据集(包括实际实验)对该模型进行了训练和测试。结果表明,所提出的 Swin-UNETR 模型实现了三维温度场预测,每帧时间成本为 0.98 秒,平均绝对误差为 0.8658 °C,比次好的基于 CNN 的模型(ResUNet3D,1.0461 °C)提高了 17.23%,比次好的机器学习模型(LightGBM,0.9078 °C)提高了 4.63%,这可归功于 Swin 变换器捕捉局部和全局上下文信息的能力以及移动窗口机制。所提出的方法对于确保锻件的成形质量和推动锻造过程中数字孪生技术的发展具有重要意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Real-time 3D temperature field reconstruction for aluminum alloy forging die using Swin Transformer integrated deep learning framework
Temperature field distribution in forging dies is crucial for quality control and defect prevention, particularly for aluminum alloys. Current methods are limited to discrete points or surface measurements, making real-time three-dimensional temperature field acquisition challenging. In this paper, a novel Swin Transformer-integrated deep learning framework is proposed for real-time 3D temperature field reconstruction of forging dies, pioneering the application of transformer architecture in physical field prediction. In this framework, numerical simulations are first conducted to provide ground truth and fundamental insights into the temperature evolution, and then limited sparse thermal sensors are utilized to offer corrected real-time input parameters. The model for 3D temperature field reconstruction is developed through the combination of Swin Transformers with the U-shaped encoder-decoder structure, which is trained and tested with various sensor configurations, initialization methods, and datasets, including actual experiments. The results demonstrate that the proposed Swin-UNETR model achieves 3D temperature field prediction with time cost of 0.98 s per frame, mean absolute error of 0.8658 °C, showing a 17.23 % improvement over the next best CNN-based model (ResUNet3D at 1.0461 °C), and a 4.63 % improvement over the next best machine learning model (LightGBM at 0.9078 °C), which can be attributed to the Swin Transformer’s ability to capture both local and global contextual information and shifted window mechanism. The proposed method holds significant implications for ensuring the forming quality of forgings and propelling the development of digital twin technology in forging processes.
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来源期刊
Applied Thermal Engineering
Applied Thermal Engineering 工程技术-工程:机械
CiteScore
11.30
自引率
15.60%
发文量
1474
审稿时长
57 days
期刊介绍: Applied Thermal Engineering disseminates novel research related to the design, development and demonstration of components, devices, equipment, technologies and systems involving thermal processes for the production, storage, utilization and conservation of energy, with a focus on engineering application. The journal publishes high-quality and high-impact Original Research Articles, Review Articles, Short Communications and Letters to the Editor on cutting-edge innovations in research, and recent advances or issues of interest to the thermal engineering community.
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