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基于数字双胞胎技术的汽车车身电阻点的质量预测方法
Ruiping Luo1,2, Shengwen Zhou1,2, Liangyi Nie1,2
1School of Mechanical and Electrical Engineering, Hubei Polytechnic University, 16Guilin Rd.(N), Huangshi, Hubei, China.
Scientific reports
|July 8, 2025
概括
本研究引入了一种新的数字双胞胎 (DT) 方法,用于预测汽车制造业中电阻点 (RSW) 质量. 通过生成虚拟数据和使用生成对抗网络 (GAN),该方法提高了对更安全车辆的预测准确性.
科学领域:
- 制造业 工程 制造工程
- 材料科学 材料科学 材料科学
- 汽车工程 汽车工程
背景情况:
- 准确预测阻力点接 (RSW) 质量对于汽车安全和可靠性至关重要.
- 传统的现场数据收集方法用于RSW质量预测面临重大局限性,影响准确性.
- 现有的方法很难解决现实汽车生产中RSW过程的动态和复杂性.
研究的目的:
- 提出一种新的RSW质量预测方法,利用数字双胞胎 (DT) 技术.
- 克服在传统的RSW质量评估中现场数据收集的局限性.
- 提高汽车应用的RSW质量预测的准确性和可靠性.
主要方法:
- 使用数字双胞胎 (DT) 模型生成虚拟动态电阻数据.
- 使用生成对抗网络 (GAN) 扩展虚拟和物理数据.
- 以贝叶斯优化为RSW质量预测优化后传 (BP) 神经网络模型的构建.
主要成果:
- 拟议的基于DT的方法有效地生成虚拟数据来增强RSW质量预测的物理数据.
- 将GAN和BP神经网络与贝叶斯优化集成,显著提高了预测模型的性能.
- 案例研究验证了虚拟数据在提高预测准确度方面的有效性,并证明了该方法的实际应用性.
结论:
- 数字双胞胎 (DT) 方法与生成对抗网络 (GAN) 和先进的神经网络相结合,为RSW质量预测提供了强大的解决方案.
- 虚拟数据生成是一种可行的策略,可以克服工业接过程中的现场数据限制.
- SHAP (沙普利增量解释) 方法提供了对输入变量影响的见解,为RSW过程优化铺平了道路.
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