Estudo de sensibilidade ao alinhamento e desenvolvimento de uma metodologia para alinhamento de sistemas ópticos por meio da análise de aberrações de frente de onda utilizando redes neurais artificiais

Abstract

Ao Alan, pela compreensão e paciência ao longo de todos estes anos e pelo incentivo e motivação nos momentos mais difíceis.Aos meus pais pelo apoio irrestrito em todos os momentos, pelo incentivo e compreensão.Ao meu irmão Rogério e à Valdaisa pelo apoio e ajuda.Aos meus familiares e amigos próximos pelo apoio e incentivo.Aos meus sobrinhos Gabriel e Yasmin, pela alegria e descontração.À Nina, pela alegria e companheirismo incondicional.A todos que, de alguma forma, contribuiram para a realização deste trabalho. Although misalignments in optical systems do not generate new aberration forms, they change the field-dependence of the known ones. In this research, the sensitivity of two-mirror optical systems due to misalignments is evaluated in function of the conic constants of the mirrors. Among the different configurations considered in this study, a specific one has shown low sensitivity due to decenter misalignments. The application of the wave aberration theory for plane-symmetric optical systems has revealed that the proper choice of the secondary mirror conic constant allows third-order uniform coma to be compensated, leading to a less sensitive system, free from the most important misalignment-induced aberration.This thesis also presents an alignment methodology based on the analysis of the transmitted wavefront utilizing artificial neural networks to estimate alignment errors in the components of the system. The transmitted wavefront carries information about the aberrations in the optical system, which can be described in terms of Zernike polynomials. Such polynomials are used for the analysis of the effects of misalignments on the aberrations of the system. Artificial neural networks are employed in the analysis of the coefficients of Zernike polynomials and used to evaluate both type and magnitude of the misalignments. Theoretical misalignments estimated in reflexive and refractive optical systems are satisfactory for perfect systems, i.e., systems with no surface errors, and noiseless data. When surface imperfections are considered, the performance of the estimator is reduced. Besides decenter and tilt misalignments, artificial neural networks can estimate axial positioning errors of the elements in the system, therefore they are believed to be a promising alternative for the alignment of complex optical systems.