概括
两个新的数字图像相关性 (DIC) 模型,D-ST和S-ST,使用Swin变压器进行精确的位移和应变预测. 它们的性能优于传统和深度学习方法,增强了材料测试和结构监测.
科学领域:
- 机械工程 机械工程
- 计算机视觉 计算机视觉
- 材料科学 材料科学 材料科学
背景情况:
- 数字图像相关性 (DIC) 对于测量变形至关重要.
- 传统的基于DIC和CNN的方法与复杂的应变梯度和非线性模式作斗争.
- 准确的全场位移和应变预测仍然是一个挑战.
研究的目的:
- 引入两个基于Swin变压器架构的新型端到端DIC模型,D-ST和S-ST.
- 为了提高全场位移和应变测量的准确性和稳定性.
- 为了解决捕捉高频变形特征和复杂应变模式的局限性.
主要方法:
- 开发了使用Swin变压器架构的D-ST和S-ST模型.
- 综合本地和全球信息使用基于窗口和基于转移窗口的多头自我注意.
- 采用类似于U-Net的编码器-解码器框架,具有多级特征融合.
- 使用B-spline有限元素方法进行训练,生成了定制合成数据集.
主要成果:
- 与传统的DIC和现有的深度学习模型相比,D-ST和S-ST模型实现了更高的性能.
- 这些模型证明了对全场位移和应变分布的强大而精确的预测.
- 即使在多样化和杂的条件下,也获得了高准确度和稳定的预测.
- 实现了高频变形特征和复杂的应变梯度的有效捕捉.
结论:
- 拟议的D-ST和S-ST模型代表了DIC技术的重大进步.
- 这些模型提供了更高的准确性,更高的分辨率和更广泛的适用于材料测试和结构健康监测.
- 基于Swin变压器的方法有效地解决了对变形测量的长期挑战.
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