This study presents a Python-based design tool that utilizes a data-driven backtracking method for aspheric coefficients to effectively address thermal deformation in aspheric glass lenses produced by the Glass Molding Press (GMP) process. To achieve the nanometer-level form accuracy essential for optical communications and high-power laser applications, it is crucial to compute compensation coefficients through nonlinear least-squares fitting of the lens's measured data to the standard aspheric equation. The proposed tool enhances user-friendliness with a PyQt GUI and incorporates the lmfit library, offering unique flexibility by allowing users to select or fix specific variables among up to 22 parameters, including the radius of curvature, conic constant, and aspheric coefficient, during the fitting process.

The ultra-precision cutting is a key technique for the manufacture of optical components such as aluminium mirrors, electroless nickel mirror, plastic mirror in a variety of advanced science and technology applications. The paper presents experimental results of ultra-precision diamond turning of electro less nickel materials. In general, the cutting condition such as feed rate and depth of cut, have effect on the surface roughness in ultra-precision diamond turning. To obtain an optimal cutting condition, we studied the effect of the cutting speed, the tool length, the tool nose radius, the feed rate and depth of cut on the surface roughness. So, the relationship of the surface roughness and cutting condition has been clarified. From the experimental results, the machined surface roughnesses were obtained less than 1㎚ rms.