Towards Precision in Bolted Joint Design: A Preliminary Machine Learning-Based Parameter Prediction
Journal:
arXiv
Published Date:
Dec 11, 2024
Abstract
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.
