Towards Precision in Bolted Joint Design: A Preliminary Machine Learning-Based Parameter Prediction

TL;DR

This paper introduces a neural network approach to predict key parameters in bolted joint design, achieving high accuracy and addressing limitations of traditional methods, with potential for improved efficiency and reliability.

Contribution

It presents a novel machine learning model that effectively captures nonlinear behaviors in bolted joints, improving prediction accuracy over conventional techniques.

Findings

Achieved 95.24% predictive accuracy in parameter estimation.

Effectively modeled nonlinear relationships using neural networks.

Demonstrated potential for neural networks as reliable tools in bolted joint design.

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.