Towards precision in bolted joint design: a preliminary machine learning-based parameter prediction

Bolted joints are critical for maintaining structural integrity and reliability. Accurate prediction of parameters is essential for optimal performance. Traditional methods often fail to capture the non-linear behavior or require significant computational resources, limiting accuracy and efficiency. This study addresses these limitations by combining empirical data with a feed-forward neural network. Leveraging experimental data and systematic preprocessing, the model effectively captures nonlinear relationships, including rescaling output variables to address scale discrepancies, achieving 95% predictive accuracy. While limited dataset size restricts generalizability, the findings demonstrate the potential of neural networks as a reliable, efficient alternative for bolted joint design. Future work aims to expand datasets and explore hybrid modeling techniques to enhance applicability.