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

• URL: http://arxiv.org/abs/2412.08286v1
• Date: Wed, 11 Dec 2024 11:00:39 GMT
• Title: Towards Precision in Bolted Joint Design: A Preliminary Machine Learning-Based Parameter Prediction
• Authors: Ines Boujnah, Nehal Afifi, Andreas Wettstein, Sven Matthiesen,
• Abstract summary: Bolted joints are critical in engineering for maintaining structural integrity and reliability. Traditional methods often fail to capture the non-linear behavior of bolted joints. This study combines empirical data with a feed-forward neural network to predict load capacity and friction coefficients.
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• 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.

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