A neural network fitted to 34 experimental samples reports 95.24% accuracy for bolted-joint load capacity and friction coefficients, but the targets are derived from the input measurements and only 7 test samples are used.
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Towards Precision in Bolted Joint Design: A Preliminary Machine Learning-Based Parameter Prediction
A neural network fitted to 34 experimental samples reports 95.24% accuracy for bolted-joint load capacity and friction coefficients, but the targets are derived from the input measurements and only 7 test samples are used.