Roughness prediction of Al2O3-based ceramic insulation coating on bearing surface

Abstract

To improve the roughness prediction accuracy of Al2O3-based ceramic insulation coating on bearing surfaces, a method based on the spectral confocal principle was proposed for measuring the surface of grinding wheels and quantifying the characteristic parameters of abrasive particles. The abrasive characteristic parameter K of the grinding wheel surface, the grinding wheel line speed vs, the workpiece feed speed f, the cutting depth ap, and the normal grinding force F were taken as input parameters. A BP neural network prediction model of workpiece surface roughness, which directly reflects the time-varying state of the grinding wheel surface, was established. The prediction performance of the network model was verified using known grinding samples and four groups of unknown samples after grinding wheel wear. The results show that the predicted roughness results of the BP network model with known samples are consistent with the actual roughness results in terms of regularity and numerical values, with network output errors are all less than ± 0.04 μm. The network prediction accuracy for the four unknown samples decreases, but the absolute value of the maximum relative error does not exceed 20.00%. The neural network prediction model, which includes the characteristic parameters of abrasive particles on the grinding wheel surface , can be used to predict the roughness of Al2O3-based ceramic insulation coating on the bearing surface under the time-varying state of abrasive wear on the grinding wheel. It also demonstrates a certain generalization ability for unknown samples.

Keywords

• al2o3-based ceramics
• insulating coating
• roughness prediction
• bp neural network
• abrasive wear