Open AccessParametric removal rate survey study and numerical modeling for deterministic optics manufacturing
Author(s) -
Vipender Singh Negi,
Harshit Garg,
Shravan Kumar Rr,
Vinod Karar,
Umesh Tiwari,
Dae Wook Kim
Publication year - 2020
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.399105
Subject(s) - optics , polishing , parametric statistics , optical transfer function , taguchi methods , range (aeronautics) , computer science , materials science , mathematics , mechanical engineering , physics , statistics , engineering , machine learning , composite material
Surface errors directly affect the performance of optical systems in terms of contrast and resolution. Surface figure errors at different surface scales are deterministically removed using controlled material removal rate (MRR) during a precision optics fabrication process. We systematically sectioned the wide range of MRR space with systematic parameters and experimentally evaluated and mapped the MRR values using a flexible membrane-polishing tool. We performed numerical analysis with a tool influence function model using a distributed MRR-based Preston's constant evaluation approach. The analysis procedure was applied to a series of experimental data along with the tool influence function models to evaluate removal rates. In order to provide referenceable survey data without entangled information, we designed the experiments using Taguchi's L27 orthogonal array involving five control parameters and statistically analyzed a large number of programmatic experiments. The analysis of variance showed that the most significant parameters for achieving a higher MRR are the spot size and active diameter.