轴承表面Al2O3基陶瓷绝缘涂层的粗糙度预测
Roughness prediction of Al2O3-based ceramic insulation coating on bearing surface
徐钰淳 1朱建辉 1师超钰 1王宁昌 1赵延军 1张高亮 2乔帅 2谷春青2
作者信息
- 1. 高性能工具全国重点实验室,郑州 450001;郑州磨料磨具磨削研究所有限公司,郑州 450001
- 2. 郑州磨料磨具磨削研究所有限公司,郑州 450001
- 折叠
摘要
为了提升轴承表面Al2O3 基陶瓷绝缘涂层的粗糙度预测精度,提出基于光谱共焦原理的砂轮表面测量及磨粒特征参数量化方法,以砂轮表面的磨粒特征参数K,砂轮线速度vs,工件进给速度f,切削深度ap及法向磨削力F为输入参数,建立能够直接反映砂轮表面时变状态的工件表面粗糙度BP神经网络预测模型,并通过已知磨削样本及砂轮磨损后的 4组未知样本对网络预测模型性能进行验证.结果表明:已知样本的BP网络模型粗糙度预测结果与实际结果的规律及数值较为一致,其网络输出误差均<±0.04 μm;4组未知样本的网络预测精度下降,但其相对误差最大值的绝对值不超过 20.00%.建立的包含砂轮表面磨粒特征参数的神经网络预测模型,可以适应砂轮磨粒磨损时变状态下的轴承表面Al2O3 基陶瓷绝缘涂层的粗糙度预测,且其对未知样本具有一定的泛化能力.
Abstract
To improve the roughness prediction accuracy of Al2O3-based ceramic insulation coating on bearing sur-faces,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 grind-ing 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 perform-ance of the network model was verified using known grinding samples and four groups of unknown samples after grind-ing 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 char-acteristic 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.
关键词
Al2O3基陶瓷/绝缘涂层/粗糙度预测/BP神经网络/磨粒磨损Key words
Al2O3-based ceramics/insulating coating/roughness prediction/BP neural network/abrasive wear引用本文复制引用
基金项目
国家重点研发计划(2020YFB2007900)
出版年
2024
国家科技期刊平台
NSTL
万方数据