This article reports a novel GPU-based 2D digital image correlation system (2D-DIC) overcoming two major limitations of this technique: It measures marker-free, i.e. without sample preparation, and the sampling rate meets the recommendations of ASTM E606. The GPU implementation enables zero-normalized cross correlation (ZNCC) calculation rates of up to 25 kHz for 256 × 256 pixel ROIs. This high-speed image processing system is combined with a high-resolution telecentric lens observing a 10 mm field-of-view, coaxial LED illumination, and a camera acquiring 2040 × 256 pixel images with 1.2 kHz. The optics resolve the microstructure of the surface even of polished cylindrical steel specimen. The displacement uncertainty is below 0.5 μm and the reproducibility in zero-strain tests approximately 10<sup>-5</sup> (1 σ) of the field-of-view. For strain-controlled testing, a minimum of two displacement subsets per image are evaluated for average strain with a sampling rate of 1.2 kHz. Similar to mechanical extensometers, an analogue 0-10V displacement signal serves as a feedback for standard PID controllers. The average latency is below 2 ms allowing for cycle frequencies up to 10 Hz. For strain-field measurement, the number of ROIs limits the frame rate, e.g., the correlation rate of 25 kHz is sufficient to evaluate 10 images per second with 2500 ROIs each. This frame rate is still sufficient to compare the maximum and minimum strain fields within a cycle in real-time, e.g. for crack detection. The result is a marker-free and non-contact DIC sensor suitable for both strain-controlled fatigue testing and real-time full-field strain evaluation.
In emitter wrap through (EWT) solar cells, laser drilling is used to increase the light sensitive area by removing emitter contacts from the front side of the cell. For a cell area of 156 x 156 mm<sup>2</sup>, about 24000 via-holes with a diameter of 60 μm have to be drilled into silicon wafers with a thickness of 200 μm. The processing time of 10 to 20 s is determined by the number of laser pulses required for safely opening every hole on the bottom side. Therefore, the largest wafer thickness occurring in a production line defines the processing time. However, wafer thickness varies by roughly ±20 %. To reduce the processing time, a coaxial camera control system was integrated into the laser scanner. It observes the bottom breakthrough from the front side of the wafer by measuring the process emissions of every single laser pulse. To achieve the frame rates and latency times required by the repetition rate of the laser (10 kHz), a camera based on cellular neural networks (CNN) was used where the images are processed directly on the camera chip by 176 x 144 sensor–processor–elements. One image per laser pulse is processed within 36 μs corresponding to a maximum pulse rate of 25 kHz. The laser is stopped when all of the holes are open on the bottom side. The result is a quality control system in which the processing time of a production line is defined by average instead of maximum wafer thickness.
A continuous increase in production speed and manufacturing precision raises a demand for the automated detection of small image features on rapidly moving surfaces. An example are wire drawing processes where kilometers of cylindrical metal surfaces moving with 10 m/s have to be inspected for defects such as scratches, dents, grooves, or chatter marks with a lateral size of 100 μm in real time. Up to now, complex eddy current systems are used for quality control instead of line cameras, because the ratio between lateral feature size and surface speed is limited by the data transport between camera and computer. This bottleneck is avoided by “cellular neural network” (CNN) cameras which enable image processing directly on the camera chip. This article reports results achieved with a demonstrator based on this novel analogue camera – computer system. The results show that computational speed and accuracy of the analogue computer system are sufficient to detect and discriminate the different types of defects. Area images with 176 x 144 pixels are acquired and evaluated in real time with frame rates of 4 to 10 kHz – depending on the number of defects to be detected. These frame rates correspond to equivalent line rates on line cameras between 360 and 880 kHz, a number far beyond the available features. Using the relation between lateral feature size and surface speed as a figure of merit, the CNN based system outperforms conventional image processing systems by an order of magnitude.
This paper reports measurement results and some design details of railway measurement systems based on optical principles.
The quality of railway lines is crucial for reliability and safety and therefore to be controlled regularly. Special measuring vehicles operate permanently on all railway lines, even during regular traffic situations. Therefore the measurement systems used to record the data have to be fast enough even at high speeds and robust enough to provide reliable data under almost any environmental conditions. The application of optical methods is advantageous concerning accuracy and speed but of course limited by external influences. We report here measures enabling even a sensitive optical measurement principle, the phase measurement technique, to be applied under these harsh environmental conditions. Exemplarily the optical and mechanical design of a clearance profile scanner is described. It is shown how to make the sensor insensitive against environmental conditions like contamination by dust or water or temperature changes. Measurement results of this scanner and of another system to measure the position of the contact wire are presented.