Data fusion-based method for the assessment of minimum zone for aspheric optics

Multi sensor data fusion is a new challenge in dimensional metrology of freeform surfaces. Although data fusion processes have been extensively investigated in the literature and multi-sensor integrated systems are gradually being implemented by industry, the data obtained by the various sensors are not being optimally processed to assess the geometrical defects of complex workpieces. The work presented in this paper aims to propose a novel framework for form error assessment in multi sensor dimensional metrology. A generic and global approach in multi-sensor metrology combining registration, data fusion and fitting is proposed for aspherical and freeform optics. Driven by a curvature-based approach, the registration of data sets obtained from different sensors is conducted through a developed coarse and fine registration method. In order to improve the accuracy of the form error assessment for freeform surfaces, a data fusion-based method is proposed in this paper. A Gaussian Process (GP) model is built based on each of the transformed data sets, followed by the maximum likelihood data fusion. The fused result has improved characteristics and reduced uncertainty than either of the measurement data sets. Finally, a fitting algorithm is applied on the fused data for assessment of the minimum zone. To demonstrate the feasibility of the proposed method, simulation data and measurement data of aspheric optics are used and evaluated through case studies. © 2021 CAD Solutions, LLC.