Liang Huazhuo, Chen Wenjie, Fu Youzhi, Zhou Wenjie, Mo Ling, Jian Yue, Wen Qi, Liu Dawei, He Junfeng
School of Mechatronic Engineering, Guangdong Polytechnic Normal University, Guangzhou 510665, China.
Micromachines (Basel). 2025 Feb 27;16(3):271. doi: 10.3390/mi16030271.
Magnetorheological-chemical-polishing tests are carried out on single-crystal silicon carbide (SiC) to study the influence of the process parameters on the polishing effect, predict the polishing results via a back propagation (BP) neural network, and construct a model of the processing parameters to predict the material removal rate (MRR) and surface quality. Magnetorheological-chemical polishing employs mechanical removal coupled with chemical action, and the synergistic effect of both actions can achieve an improved polishing effect. The results show that with increasing abrasive particle size, hydrogen peroxide concentration, workpiece rotational speed, and polishing disc rotational speed, the MRR first increases and then decreases. With an increasing abrasive concentration and carbonyl iron powder concentration, the MRR continues to increase. With an increasing machining gap, the MRR shows a continuous decrease, and the corresponding changes in surface roughness tend to decrease first and then increase. The prediction models of the MRR and surface quality are constructed via a BP neural network, and their average absolute percentage errors are less than 2%, which is important for the online monitoring of processing and process optimisation.
对碳化硅（SiC）单晶进行磁流变化学抛光试验，以研究工艺参数对抛光效果的影响，通过反向传播（BP）神经网络预测抛光结果，并构建加工参数模型以预测材料去除率（MRR）和表面质量。磁流变化学抛光采用机械去除与化学作用相结合的方式，两种作用的协同效应可实现更好的抛光效果。结果表明，随着磨粒尺寸、过氧化氢浓度、工件转速和抛光盘转速的增加，材料去除率先增大后减小。随着磨料浓度和羰基铁粉浓度的增加，材料去除率持续增大。随着加工间隙的增大，材料去除率持续减小，表面粗糙度的相应变化趋势为先减小后增大。通过BP神经网络构建了材料去除率和表面质量的预测模型，其平均绝对百分比误差小于2%，这对于加工过程的在线监测和工艺优化具有重要意义。
