Yin Jiateng, Zhao Jun, Song Fengqi, Xu Xinqiang, Lan Yeshen
College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China.
Key Laboratory of Special Purpose Equipment and Advanced Processing Technology, Zhejiang University of Technology, Ministry of Education & Zhejiang Province, Hangzhou 310023, China.
Materials (Basel). 2023 Mar 28;16(7):2683. doi: 10.3390/ma16072683.
Micro-electro-mechanical systems (MEMS) hemispherical resonant gyroscopes are used in a wide range of applications in defense technology, electronics, aerospace, etc. The surface roughness of the silicon micro-hemisphere concave molds (CMs) inside the MEMS hemispherical resonant gyroscope is the main factor affecting the performance of the gyroscope. Therefore, a new method for reducing the surface roughness of the micro-CM needs to be developed. Micro-ultrasonic machining (MUM) has proven to be an excellent method for machining micro-CMs; shear thickening fluids (STFs) have also been used in the ultra-precision polishing field due to their perfect processing performance. Ultimately, an STF-MUM polishing method that combines STF with MUM is proposed to improve the surface roughness of the micro-CM. In order to achieve the excellent processing performance of the new technology, a Categorical Boosting (CatBoost)-genetic algorithm (GA) optimization model was developed to optimize the processing parameters. The results of optimizing the processing parameters via the CatBoost-GA model were verified by five groups of independent repeated experiments. The maximum absolute error of CatBoost-GA is 7.21%, the average absolute error is 4.69%, and the minimum surface roughness is reduced by 28.72% compared to the minimum value of the experimental results without optimization.
微机电系统（MEMS）半球谐振陀螺仪在国防技术、电子、航空航天等众多领域有着广泛应用。MEMS半球谐振陀螺仪内部硅微半球凹模（CM）的表面粗糙度是影响陀螺仪性能的主要因素。因此，需要开发一种降低微CM表面粗糙度的新方法。微超声加工（MUM）已被证明是加工微CM的一种出色方法；剪切增稠流体（STF）因其优异的加工性能也已被应用于超精密抛光领域。最终，提出了一种将STF与MUM相结合的STF-MUM抛光方法来改善微CM的表面粗糙度。为了实现该新技术的优异加工性能，开发了一种分类提升（CatBoost）-遗传算法（GA）优化模型来优化加工参数。通过五组独立重复实验验证了经CatBoost-GA模型优化加工参数的结果。CatBoost-GA的最大绝对误差为7.21%，平均绝对误差为4.69%，与未优化的实验结果最小值相比，最小表面粗糙度降低了28.72%。
