Surface modeling and influencing factors for microlens array by slow tool servo machining

Abstract

Microlens arrays (MLAs) are widely used in a variety of important fields due to their unique optical properties. Slow tool servo machining (STSM)technology is a manufacturing technology for machining non-rotationally symmetric surfaces. In order to solve the problems of many influencing factors of STSM machining quality, high experimental trial and error cost, and mutual limitation of accuracy and efficiency, on the basis of analyzing and determining reasonable tool trajectories, a microlens array machining surface morphology prediction model is established from the theoretical point of view, and the prediction model is optimized by combining the two-dimensional Gaussian filtering method, and the surface accuracy and roughness are used to evaluate the processing quality. The prediction model is used to analyze the influence, like feed per revolution, radius of tool tip, and tool setting error on the surface quality. After the Microlens machining experiments, the results are matched with the simulation, which shows us the model is correct, and the MLAs were machined by the optimal parameters with the surface shape error PV = 0.741 μm, surface roughness Sa = 22 nm, Sq = 28 nm, and Sz = 59 nm.