Disparity pattern-based autostereoscopic 3D metrology system for in situ measurement of microstructured surfaces

Semi-supervised angular super-resolution method for autostereoscopic 3D surface measurement

Autostereoscopic 3D measuring systems are an accurate, rapid, and portable method for in situ measurements. These systems use a micro-lens array to record 3D information based on the light-field theory. However, the spatial-angular-resolution trade-off curtails their performance. Although learning models were developed for super-resolution, the scarcity of data hinders efficient training. To address this issue, a novel, to the best of our knowledge, semi-supervised learning paradigm for angular super-resolution is proposed for data-efficient training, benefiting both autostereoscopic and light-field devices. A convolutional neural network using motion estimation is developed for a view synthesis. Subsequently, a high-angular-resolution autostereoscopic system is presented for an accurate profile reconstruction. Experiments show that the semi-supervision enhances view reconstruction quality, while the amount of training data required is reduced by over 69%.

Self super-resolution autostereoscopic 3D measuring system using deep convolutional neural networks

Autostereoscopy technology can provide a rapid and accurate three-dimensional (3D) measurement solution for micro-structured surfaces. Elemental images (EIs) are recorded within one snapshot and the measurement accuracy can be quantified from the disparities existing in the 3D information. However, a trade-off between the spatial and the angular resolution of the EIs is a major obstacle to the improvement on the measurement results. To address this issue, an angular super-resolution algorithm based on deep neural networks is proposed to construct a self super-resolution autostereoscopic (SSA) 3D measuring system. The proposed super-resolution algorithm can generate novel perspectives between the neighboring EIs so that the angular resolution is enhanced. The proposed SSA 3D measuring system can achieve self super-resolution on its measurement data. A comprehensive comparison experiment was conducted to verify the feasibility and technical merit of the proposed measuring system. The results show that the proposed SSA system can significantly improve the resolution of the measuring data by around 4 folds and enhance the measurement accuracy to a sub-micrometer level with lower standard deviations and biases.

Fiducial-aided calibration of a displacement laser probing system for in-situ measurement of optical freeform surfaces on an ultra-precision fly-cutting machine

Surface metrology is an essential operation to determine whether the quality of manufactured surfaces meets the design requirements. In order to improve the surface accuracy and machining efficiency in the manufacturing of optical freeform surfaces, in-situ surface measurement without re-positioning the workpiece is considered as a promising technique in advanced manufacturing. In this study, a displacement laser scanner is integrated into an ultra-precision fly-cutting machine in order to perform as a coordinate measuring machine. However, some inevitable errors such as motion errors of the machine tool, thermal drift, vibrations, and errors of the laser sensor are introduced due to the manufacturing environment. To improve the performance of the measurement system, calibration of the main error sources is investigated with consideration of the characteristics of the built laser scanner system. Hence, the relationship between the moving speed of the laser scanner and the vibration of the tested signals is studied. Following that, the errors of the z-axis scale could be corrected by measuring a four-step heights artefact. Furthermore, volumetric positioning errors are identified by the proposed modified chi-square method and Gaussian processing prediction method. Simulation and measurement experiments are conducted, and the results indicate that the calibrated measuring system can measure ultra-precision freeform surfaces with micrometre form accuracy.

Surface topography acquisition method for double-sided near-right-angle structured surfaces based on dual-probe wavelength scanning interferometry

This paper proposes an approach to measure double-sided near-right-angle structured surfaces based on dual-probe wavelength scanning interferometry (DPWSI). The principle and mathematical model is discussed and the measurement system is calibrated with a combination of standard step-height samples for both probes vertical calibrations and a specially designed calibration artefact for building up the space coordinate relationship of the dual-probe measurement system. The topography of the specially designed artefact is acquired by combining the measurement results with white light scanning interferometer (WLSI) and scanning electron microscope (SEM) for reference. The relative location of the two probes is then determined with 3D registration algorithm. Experimental validation of the approach is provided and the results show that the method is able to measure double-sided near-right-angle structured surfaces with nanometer vertical resolution and micrometer lateral resolution.