Surface roughness prediction model in ultrasonic vibration assisted grinding of BK7 optical glass

Abstract

Pre-knowledge of machined surface roughness is the key to improve whole machining efficiency and meanwhile reduce the expenditure in machining optical glass components. In order to predict the surface roughness in ultrasonic vibration assisted grinding of brittle materials, the surface morphologies of grinding wheel were obtained firstly in the present work, the grinding wheel model was developed and the abrasive trajectories in ultrasonic vibration assisted grinding were also investigated, the theoretical model for surface roughness was developed based on the above analysis. The prediction model was developed by using Gaussian processing regression (GPR) due to the influence of brittle fracture on machined surface roughness. In order to validate both the proposed theoretical and GPR models, 32 sets of experiments of ultrasonic vibration assisted grinding of BK7 optical glass were carried out. Experimental results show that the average relative errors of the theoretical model and GPR prediction model are 13.11% and 8.12%, respectively. The GPR prediction results can match well with the experimental results.

摘要

在光学玻璃零件加工过程中, 对加工表面粗糙度进行预测是提升整个制造工艺链效率和减小总体加工成本的关键。 为预测脆性材料超声振动磨削过程中的加工表面粗糙度, 首先获取金刚石砂轮表面的实际微观形貌, 建立砂轮表面数字化仿真模型, 并分析超声振动磨削过程中磨粒的运动轨迹, 建立加工表面粗糙度的理论预测模型。 超声振动加工过程中材料脆性断裂对加工表面粗糙度影响严重, 因此采用高斯过程回归 (GPR) 方法对理论预测模型进行了修正。 为验证理论模型和 GPR 模型的准确性, 进行 32 组 BK7 光学玻璃超声振动磨削加工实验。 结果表明: 理论模型和 GPR 预测模型的平均误差分别为 13.11%和 8.12%。 GPR 预测模型所获预测结果与实验值吻合较好。