Prediction on friction characteristics of mine hoist disc brakes using artificial neural networks
TL;DRAbstract
Safety and reliability are the main requirements for brake devices in the mining winding installations. Trouble-free performance under changing braking parameters is mandatory. Therefore, selection of the right materials for the friction brake elements (pads and discs) is the most challenging task for brake system designers. The coefficient of friction for the friction couple should be relatively high (≈ 0.4); but, above all, it should be stable. In order to achieve the desired brake friction couple performance, a new approach to the prediction of the tribological processes versus friction materials formulation is needed. The paper shows that the application of the artificial neural network (ANN) can be productive in modelling complex, multi-dimensional functional relationships directly from experimental data. The ANN can learn to produce an input/output relationship, and the model of friction brake behaviour can be established.
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Safety and reliability are the main requirements for brake devices in the mining winding installations. Trouble-free performance under changing braking parameters is mandatory. Therefore, selection of the right materials for the friction brake elements (pads and discs) is the most challenging task for brake system designers. The coefficient of friction for the friction couple should be relatively high (≈ 0.4); but, above all, it should be stable. In order to achieve the desired brake friction couple performance, a new approach to the prediction of the tribological processes versus friction materials formulation is needed. The paper shows that the application of the artificial neural network (ANN) can be productive in modelling complex, multi-dimensional functional relationships directly from experimental data. The ANN can learn to produce an input/output relationship, and the model of friction brake behaviour can be established.
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