目的 提出一种弧形流道优化的磁动压抛光方法。方法 基于动压润滑理论,设计了一种集成三段式弧形流道的抛光盘结构。采用Python编程进行拉丁超立方采样（LHS）,结合Scikit-learn机器学习库构建流道几何参数（X1, X2, X3）与最大动压力Pmax多项式回归预测模型,并基于SLSQP算法求解约束优化问题;利用ANSYS Fluent软件建立三维稳态流场模型,进一步分析了转速比、工作间隙及磨粒浓度对工件表面动压分布特性及承载力Fc的影响规律。结果 通过Python回归预测与SLSQP算法寻优,获得了优化区域最优流道结构参数的组合。与传统梯形流道相比,优化后的弧形流道使工件表面平均动压提升了35%左右。工艺参数仿真结果显示：当抛光盘与工件转速比$|i|$=10时,动压不均匀系数Mp达到最小值56.93%,分布均匀性最佳;当工作间隙为100 μm时,抛光区承载力Fc达到峰值1.3 N;动压与承载力随磨粒浓度的增加而显著提高。结论 本研究提出的基于Fluent仿真与Python优化算法相结合的流道结构优化方法是有效可行的。优化后的弧形流道结构及工艺参数能够显著提升抛光区的动压效应、承载能力及动压均匀性,为实现铌酸锂晶片的高质量、低损伤加工提供了思路。
Abstract
Lithium niobate (LiNbO3) crystals, renowned for their exceptional electro-optical and acousto-optical properties, are indispensable substrate materials for high-frequency filters and optical waveguides. However, their intrinsic characteristics such as high hardness, brittleness, and strong anisotropy pose significant challenges to ultra-precision machining. Conventional contact polishing techniques often result in low material removal rates, surface scratches, and subsurface damage. To overcome these limitations and achieve high-efficiency, damage-free planarization, the work aims to propose an integrated optimization strategy based on hydrodynamic lubrication theory. The strategy combines flow-channel geometry design, Python-based machine-learning parametric modeling and optimization, and validation through 3D transient numerical simulations with ANSYS Fluent. The objective is to significantly enhance the hydrodynamic effect and load-carrying capacity while improving the uniformity of the pressure distribution by scientifically reconstructing the flow-field characteristics, thereby enabling efficient, stable, and low-damage ultra-precision polishing of hard and brittle materials. Firstly, a parametric model of the arc-shaped flow channel was established, identifying key geometric variables (X1, X2, X3). Through Python programming, Latin Hypercube Sampling (LHS) was employed to generate a representative dataset. This dataset was then fitted to a nonlinear polynomial regression model via the Scikit-learn library to accurately map the complex relationship between the channel parameters (X1, X2, X3) and the maximum dynamic pressure Pmax. Subsequently, the Sequential Least Squares Quadratic Programming (SLSQP) algorithm was applied to solve the constrained optimization problem, determining the optimal geometric-parameter combination that maximizes hydrodynamic pressure. To validate the design and explore the effects of process parameters, a three-dimensional transient flow-field model was developed with Ansys Fluent. The simulation results indicated that the optimized arc-shaped channel significantly outperformed the traditional trapezoidal design, increasing the average dynamic pressure on the workpiece surface by approximately 35%, thereby demonstrating the effectiveness of the optimization strategy. Furthermore, the effects of key process parameters on the magnitude and uniformity of dynamic pressure were systematically analyzed. The results revealed that the speed ratio $|i|$was a key factor controlling flow-field behavior. At low-speed ratios ($|i|\leqslant1$), the pressure distribution was asymmetric and unstable. However, at high-speed ratios ($|i|\geqslant2$), particularly at a ratio of 10∶1, the dynamic pressure exhibited a highly stable, axisymmetric concentric-haoring distribution. Under these conditions, the dynamic-pressure non-uniformity coefficient (Mp) reached a minimum value of 56.93%, indicating optimal uniformity. The working gap had a differentiated impact on polishing-performance parameters. While the average dynamic pressure was relatively insensitive to variations in the gap, the load-carrying capacity Fc showed a distinct peak at a gap of 100 μm, identifying this as the ideal value for stable machining. In addition, increasing the solid volume fraction of silica nanoparticles in the polishing fluid was shown to effectively enhance dynamic-pressure performance. Higher solid content significantly increased fluid viscosity, resulting in substantial improvements in both dynamic pressure and load-carrying capacity. In conclusion, this work demonstrates that the MA-HDP method, supported by an optimized arc-shaped flow channel and appropriate process parameters, can generate a robust and uniform hydrodynamic pressure film. These findings provide a solid theoretical foundation and valuable technical reference for optimizing ultra-precision polishing processes for LiNbO3 and other hard, brittle materials, ensuring a balance between high processing efficiency and excellent surface integrity.
关键词
磁动压抛光 /
弧形流道 /
Fluent /
转速比 /
均匀性 /
低损伤
Key words
magnetohydrodynamic polishing /
arc-shaped flow channel /
Fluent /
speed ratio /
uniformity /
low damage
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基金
浙江省自然科学基金（LQ23E050003）; 国家自然科学基金项目（面上52475446,海外2023）; 安徽省自然科学基金项目（面上2308085ME170）; 安徽省科技创新攻坚计划重大重点项目（202423i08050035）; 温州市重大科技创新攻关项目（ZG2022029）
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