基于变压器的多模态学习预测热处理不锈钢的力学性能

IF 7.9 2区 材料科学 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY
Xuefei Wang , Shijie Zhang , Di Jiang , Wei Yu , Yihao Zheng , Chunyang Luo , Haojie Wang , Zhaodong Wang
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引用次数: 0

摘要

准确预测热处理材料的力学性能是智能过程控制和先进制造的关键。本研究提出了一种基于变压器的多模态学习框架,用于预测真空渗碳后渗碳钢的硬度和磨损行为。通过整合微观结构图像、材料成分和工艺参数,该模型有效地捕获了复杂的跨模态关系。实验结果表明,多模态模型具有较高的预测精度,硬度预测的R2为0.98,MAE为5.23 HV。此外,引入变分模态分解(VMD)对磨损曲线进行预处理,降低了噪声,提高了摩擦性能预测的鲁棒性。结果证明了该方法的有效性和可推广性,为智能材料性能评估和工艺优化提供了一种实用的基于人工智能的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Transformer-based multimodal learning for predicting mechanical properties in heat-treated stainless steel

Transformer-based multimodal learning for predicting mechanical properties in heat-treated stainless steel
Accurately predicting mechanical properties of heat-treated materials is critical for intelligent process control and advanced manufacturing. This study proposes a Transformer-based multimodal learning framework for predicting the hardness and wear behavior of carburized steel after vacuum carburizing. By integrating microstructural images, material compositions, and process parameters, the proposed model effectively captures complex cross-modal relationships. Experimental results show that the multimodal model achieves high prediction accuracy, with an R2 of 0.98 and MAE of 5.23 HV for hardness prediction. Furthermore, Variational Mode Decomposition (VMD) is introduced to preprocess the wear curve, reducing noise and improving the robustness of friction performance prediction. The results demonstrate the effectiveness and generalizability of the proposed approach, offering a practical AI-based solution for intelligent material property evaluation and process optimization.
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来源期刊
Materials & Design
Materials & Design Engineering-Mechanical Engineering
CiteScore
14.30
自引率
7.10%
发文量
1028
审稿时长
85 days
期刊介绍: Materials and Design is a multi-disciplinary journal that publishes original research reports, review articles, and express communications. The journal focuses on studying the structure and properties of inorganic and organic materials, advancements in synthesis, processing, characterization, and testing, the design of materials and engineering systems, and their applications in technology. It aims to bring together various aspects of materials science, engineering, physics, and chemistry. The journal explores themes ranging from materials to design and aims to reveal the connections between natural and artificial materials, as well as experiment and modeling. Manuscripts submitted to Materials and Design should contain elements of discovery and surprise, as they often contribute new insights into the architecture and function of matter.
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