Abstract
Single-crystal calcium fluoride (CaF2) is essential in high-level optical systems due to its excellent optical properties, including high penetration, laser damage resistance, and low dispersion across ultraviolet and infrared wavelengths. However, CaF2 is soft, brittle, and its anisotropic nature makes predicting surface roughness (Ra) during lens manufacturing challenging. Traditionally, operators rely on manual adjustments and measurements to achieve desired surface finishes, a process that is both time-consuming and costly. This study addresses these challenges by developing a model to predict surface roughness in ultra-precision machining of CaF2. The machining depth was set to 200 nm to maintain ductile cutting conditions. A central composite design (CCD) experiment gathered data on three critical machining parameters: spindle speed, feed rate, and tool rake angle. The surface roughness prediction models were then constructed using a synthetic artificial neural network (S-ANN), response surface methodology (RSM), and kriging. Evaluation results showed that the S-ANN model achieved the highest accuracy (97.4% accuracy, 0.907 nm error), outperforming the RSM (90.4% accuracy, 1.343 nm error) and kriging (87.1% accuracy, 1.931 nm error) models. The S-ANN model's performance is suitable for practical application, offering an effective method to predict CaF2Ra, significantly reducing costs associated with single-point diamond turning.