Numerical simulation to calculate the free spectral range scans (FSR scans) of laser resonators is a computationally intensive task. OSCAR is a well-established Matlab toolbox that enables for such simulations based on Fourier optics. Any arbitrary discrete complex electromagnetic input fields as well as misalignment or mismatching of resonators can be considered in the FSR simulation. Unfortunately, it currently only features CPU based calculations on one or more CPU cores. However, the computational cost increases exponentially with increasing lateral resolution of the complex electromagnetic fields. In addition, only a limited number of roundtrips can be carried out in an acceptable computation time, which limits the applicability only to low finesse resonators. Due to good parallelizability of the FSR scan calculation, this numerical computation is very well suited for modern graphics cards, which are outstanding in performing many calculations in parallel. This paper introduces the extension of FSR scan simulations on modern graphics cards (GPUs) within the OSCAR Toolbox. First, a statistical analysis is provided, that presents the massive performance improvement compared to CPU computations. Subsequently, the disadvantages in the form of memory limitations of GPUs are outlined. Therefore, generally valid data is presented, from which a trade-off between lateral resolution of the complex electromagnetic fields and the number of roundtrips to be performed can be derived. In conclusion, the great potentials of new applications are highlighted, which were previously not feasible. Any code of this GPU implementation discussed in this paper has been integrated into the OSCAR Matlab Toolbox and is made available open source on GitHub.
External Fabry-Perot resonators are widely used in the field of optics and are well established in areas such as frequency selection and spectroscopy. However, fine tuning and thus most efficient coupling of these resonators into the optical path is a time-consuming task, which is usually performed manually by trained personnel. The state of the art includes many different approaches for automatic alignment, which, however, are designed for special optical configurations and cannot be generalized. However, these approaches are only valid for individually designed optical systems and are not universally applicable. Moreover, none of these approaches address the identification of the spatial degrees of freedom of the resonator. Knowledge of this exact pose information can generally be integrated into the alignment process and has great potential for automation. In this work, convolutional neural networks (CNNs) are applied to identify the sensitive spatial degrees of freedom of a FabryPerot resonator in a simulation environment. For this purpose, well established CNN architectures, which are typically used for feature extraction, are adapted to this regression problem. The input of the CNNs was chosen to be the intensity profiles of the transversal modes, which can be obtained from the transmitted power behind the resonator. These modes are known to be highly correlated with the coupling quality and thus with the spatial location of resonators. To achieve an exact pose estimation, the CNN input consists of several images of mode profiles, which are propagated through an encoder structure followed by fully-connected layers providing the four spatial parameters as the network output. For training and evaluation, intensity images as well as resonator poses are obtained from a simulation of a free spectral range of a resonator. Finally, different encoder structures including a memory efficient, small self-developed network architecture are evaluated.
For the automated optical inspection of manufactured components with complex geometries or highly reflective surfaces, a suitable selection of measurement poses and the associated planning of the measurement trajectory is crucial. This is especially important for active triangulation measurement methods like fringe projection. Due to complex measurement object geometries or poor alignment of the measuring system the influence of multiple reflections can potentially lead to incorrect or incomplete 3-D reconstruction of the specimen surface. This paper introduces a simulative GPU-based inverse ray tracing approach to identify low-reflection measurement poses for active optical measurement systems. Starting from the virtual camera origin, rays are emitted from each camera pixel and the reflection at the measurement objects surface is calculated using the Torrence- Sparrow BRDF. With an additional approach based on Whitted raytracing, the influence of multiple reflections and the reflection depth on the rendered camera image is taken into account. By calculating the summed reflection depth of each rendered measurement sequence, a height map of the reflection frequency distribution is created. By sampling a predefined surface point on the path of a limited sphere, the comparability of possible measurement poses is achieved. Thus, local minima can be identified and the poses with the lowest reflection influence can be selected to perform a suitable trajectory planning. This a priori knowledge can also be transferred into application and used for the estimation of image areas, which captured multiple reflections. Thus for these areas specific masks are generated and can be applied in real measurements to reconstruct multiple reflection free surfaces.
The quality of optical measurements is significantly affected by the reflection properties of the measured component. Therefore, it is important to consider the properties of the reflective surface to obtain accurate measurement results. A common method for the mathematical representation of reflections is the bidirectional reflection distribution function (BRDF). Typically BRDFs are measured via a gonioreflectometer. However, these are often only applicable on flat specimens or objects with previously known geometric properties. This paper presents an approach for the measurement of the BRDF on inhomogeneous freeform surfaces. For this purpose, a robot-assisted multisensor system is used consisting of a fringe projection sensor and an industrial camera, which is modified with six light sources that are evenly distributed around the optical axis and point at the measuring object. The reflection measurement consists of the sequential image acquisition of individual lighting configurations by successively switched on light sources. With the assumption of isotropic surface properties and known position of each individual light source, the relative BRDF can be determined pixel by pixel. This enables the BRDF measurement of freeform surfaces with varying reflection properties. Knowing the transformation between both sensor coordinate systems, the resulting BRDF data can be projected onto the points of the fringe projection measurement for geometrical representation. As an application example, a damage characterization of surfaces, based on the measured BRDF data is presented. For this purpose, a worn turbine blade of an aircraft engine is characterized so that burnt regions on the components’ surface can be detected.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.