用神经网络预测螺栓连接的承力与摩擦，准确率达95.24%。
Towards Precision in Bolted Joint Design: A Preliminary Machine Learning-Based Parameter Prediction
- 结合实验数据与前馈神经网络，捕捉螺栓连接的非线性关系。
- 模型在有限数据下实现95.24%的预测准确率，提升设计效率。
- 适合结构工程中需要快速参数预测的研究者参考。
螺栓连接在工程中对结构完整性和可靠性至关重要。准确预测影响其性能的关键参数对优化设计至关重要。传统方法难以捕捉螺栓连接的非线性行为，或需大量计算资源，限制了精度与效率。本研究通过结合实验数据与前馈神经网络，预测载荷能力与摩擦系数。利用实验数据并进行系统预处理，模型有效捕捉非线性关系，包括对输出变量进行重缩放以解决量纲差异，最终达到95.24%的预测准确率。尽管数据集规模和多样性有限，影响泛化能力，但结果表明神经网络可作为可靠高效的螺栓连接设计替代方案。未来工作将聚焦于扩大数据集及探索混合建模技术以增强适用性。
原文摘要 · Abstract (English)
Bolted joints are critical in engineering for maintaining structural integrity and reliability. Accurate prediction of parameters influencing their function and behavior is essential for optimal performance. Traditional methods often fail to capture the non-linear behavior of bolted joints or require significant computational resources, limiting accuracy and efficiency. This study addresses these limitations by combining empirical data with a feed-forward neural network to predict load capacity and friction coefficients. Leveraging experimental data and systematic preprocessing, the model effectively captures nonlinear relationships, including rescaling output variables to address scale discrepancies, achieving 95.24% predictive accuracy. While limited dataset size and diversity restrict generalizability, the findings demonstrate the potential of neural networks as a reliable, efficient alternative for bolted joint design. Future work will focus on expanding datasets and exploring hybrid modeling techniques to enhance applicability.
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