螺栓预负荷与拉紧扭矩的非线性变化预测模型基于机制和数据融合
Yueqi Qiao1, Bing Zhao2, Dingshan Deng1
1Department of Mechanical Engineering, Qinghai University, Xining, 810016, China.
Scientific reports
|March 7, 2025
概括
本研究引入了一种新的螺栓预加载预测方法,使用六角端面螺栓的机制和数据融合. 机器学习模型准确预测预负载变化和置信区间,改进了传统的扭矩系数方法.
科学领域:
- 机械工程 机械工程
- 计算力学 计算力学 计算力学
- 机器学习应用 机器学习应用
背景情况:
- 螺栓装载的扭矩方法在准确预测螺栓预装载方面存在重大挑战.
- 现有的依赖扭矩系数的方法提供间接预装载荷,容易出现错误.
- 摩擦和其他因素的变化使得精确的螺栓预载预测变得困难.
研究的目的:
- 为六角端面螺栓开发一个强大的螺栓预载预测方法.
- 建立基于机器学习的收紧预测模型,整合机制和数据融合.
- 为了克服传统间接螺栓预加载装载技术的局限性.
主要方法:
- 建立一个收紧机制模型,以识别预测错误的来源.
- 开发一个灵敏度评估指标,用于参数灵敏度分析.
- 实施"机制模型指导数据模型"的融合方法来进行特征选择.
- 对于收紧预测模型的高斯过程回归的应用.
主要成果:
- 拟议的模型准确地预测了螺栓预载变化与拉紧扭矩.
- 该模型提供了对螺栓预加载波动的置信区间的同步显示.
- 在各种操作条件下,预测准确度始终超过98.18%.
结论:
- 开发的机制和数据融合方法在直接螺栓预装预测方面取得了突破.
- 高斯过程回归为螺栓紧固提供了可靠和准确的预测模型.
- 工程预测软件可实现精确的预加载控制,增强结构完整性.
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