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

arXiv: https://arxiv.org/abs/2412.08286 · HTML: https://mlsanity.com/paper/2412.08286/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 41 references that are in the map.
- First posted on arXiv 11 Dec 2024.

## Builds on (most cited first, 3 of 3)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [A survey on modern trainable activation functions](https://mlsanity.com/paper/2005.00817/) (2020)

## Built on by (most cited first, 1 of 1)

- [Empirical Evaluation of No Free Lunch Violations in Permutation-Based Optimization](https://mlsanity.com/paper/2603.03613/) (2026)

## Abstract (arXiv)

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.