节点文献
磨料水射流抛光熔石英玻璃的多目标参数优化(英文)
Multi-objective parameter optimization of abrasive water jet polishing for fused silica
【摘要】 磨料水射流抛光技术作为一种非接触式超精密加工方法,因其材料去除函数稳定、无亚表面损伤及强形状适应性,在光学元件加工领域具有重要应用价值。本研究通过计算流体动力学(CFD)数值模拟方法,系统分析了射流压力、喷嘴直径及入射角度对抛光流场压力分布、速度分布及壁面剪切力分布的作用规律。基于Box-Behnken实验设计,构建了响应面回归模型,系统研究了工艺参数对熔石英玻璃的材料去除率(MRR)和表面粗糙度(Ra)的影响机制。实验结果表明:通过增大射流压力和喷嘴直径可显著提高MRR,该规律与流场仿真揭示的剪切应力分布特征一致;但增大射流压力和入射角度会导致Ra增大,不利于表面质量提升。通过遗传算法(GA)多目标优化建立Pareto解集,成功实现了加工效率与表面质量的协同优化,在射流压力为2 MPa、喷嘴直径为0.3mm、入射角度为30°的参数组合下MRR达169.05μm~3/s、Ra低至0.50 nm。实验结果表明,模型预测值与实测值误差仅为4.4%(MRR)和3.8%(Ra),验证了模型的可靠性。本研究建立的参数优化体系为复杂曲面光学元件的超精密抛光提供了理论依据与技术支持。
【Abstract】 As a non-contact ultra-precision machining method, abrasive water jet polishing(AWJP) has significant application in optical elements processing due to its stable tool influence function(TIF), no subsurface damage and strong adaptability to workpiece shapes. In this study, the effects of jet pressure, nozzle diameter and impinging angle on the distribution of pressure, velocity and wall shear stress in the polishing flow field were systematically analyzed by computational fluid dynamics(CFD) simulation. Based on the Box-Behnken experimental design, a response surface regression model was constructed to investigate the influence mechanism of process parameters on material removal rate(MRR) and surface roughness(Ra) of fused silica. And experimental results showed that increasing jet pressure and nozzle diameter significantly improved MRR,consistent with shear stress distribution revealed by CFD simulations. However, increasing jet pressure and impinging angle caused higher Ra values, which was unfavorable for surface quality improvement. Genetic algorithm(GA) was used for multi-objective optimization to establish Pareto solutions, achieving concurrent optimization of polishing efficiency and surface quality. A parameter combination of 2 MPa jet pressure,0.3 mm nozzle diameter, and 30° impinging angle achieved MRR of 169.05 μm3/s and Ra of 0.50 nm. Experimental verification showed prediction errors of 4.4%(MRR) and 3.8%(Ra), confirming the model’s reliability. This parameter optimization system provides theoretical basis and technical support for ultra-precision polishing of complex curved optical components.
【Key words】 abrasive; computational fluid dynamics; tool influence function; material removal rate; surface roughness;
- 【文献出处】 中国光学(中英文) ,Chinese Optics , 编辑部邮箱 ,2025年05期
- 【分类号】TQ171.731
- 【下载频次】47