Summary of Towards Precision in Bolted Joint Design: a Preliminary Machine Learning-based Parameter Prediction, by Ines Boujnah et al.
Towards Precision in Bolted Joint Design: A Preliminary Machine Learning-Based Parameter Prediction
by Ines Boujnah, Nehal Afifi, Andreas Wettstein, Sven Matthiesen
First submitted to arxiv on: 11 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neural and Evolutionary Computing (cs.NE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this study, researchers developed a machine learning model that can accurately predict the behavior of bolted joints in engineering applications. The traditional methods used in this field often struggle to capture the complex non-linear relationships between various parameters, which can limit their accuracy and efficiency. To address these limitations, the authors combined empirical data with a feed-forward neural network to predict load capacity and friction coefficients. The model was trained on experimental data and achieved 95.24% predictive accuracy after systematic preprocessing and rescaling of output variables. This work demonstrates the potential of neural networks as a reliable and efficient alternative for bolted joint design, although further research is needed to expand dataset size and diversity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Bolted joints are important in engineering because they help keep structures strong and reliable. To make sure these joints work well, we need to understand how different factors affect their behavior. Traditional methods don’t always get this right, so researchers developed a new machine learning model to predict the load capacity and friction coefficients of bolted joints. They used data from experiments and training the model allowed them to achieve 95.24% accuracy in predicting its performance. This work shows that neural networks can be a reliable and efficient way to design better bolted joints, but more research is needed to make it even more useful. |
Keywords
» Artificial intelligence » Machine learning » Neural network
