The T-stress as a measure for the multiaxiality or the degree of constraint is well-known in linear-elastic fracture mechanics. It is known to affect the size and shape of the plastic zone in front of the crack tip and is therefore expected to directly influence plasticity-induced crack closure (PICC). At the same time, individual overload cycles also influence the size of this plastic zone not only during the current overload cycle but also during subsequent normal loading cycles.The focus of the present study is to experimentally study the influence of multiaxial far field loading on the crack opening displacement (COD) and hence crack closure and opening in two materials commonly used in gas turbine engines. For this, room temperature crack propagation tests are performed on the titanium alloy Ti6246 and the tempered steel 26NiCrMoV14-5. The experiments are conducted using cruciform specimens in a biaxial test-rig. This experimental set-up allows for a systematic variation and comparison of different T values as well as combined overload and normal load cycles, enabling targeted investigation of their effects on the plastic zone in the crack tip area. The tests are monitored and analyzed using a newly developed Digital Image Correlation (DIC) setup. This enables to evaluate crack growth and crack flank opening as well as the measurement of crack closure forces at different crack flank positions. Ultimately, this approach allows for a systematic investigation of PICC.In conclusion, a workflow is developed to automatically extract COD, crack closure forces, and the compliance of both open and closed crack states, as well as the resulting compliance offset, from the DIC data. The results show that while the influence of isolated overload cycles is not pronounced, the T-stress has a clear and measurable effect on crack closure forces in biaxially loaded cruciform specimens.

Multi-wavelength digital holography enables surface-shape measurements with an exceptional dynamic range by combining interferometric resolution with synthetic wavelengths spanning multiple length scales. Although the concept promises measurement ranges of many orders of magnitude, its practical implementation is limited by the lack of light sources that allow fast, reliable, and calibration-free switching between synthetic wavelengths over a wide frequency range. Here, we present a synthetic-wavelength generator based on an electro-optic frequency comb with electronically tunable modulation frequency and a set of switchable band-pass filters. By combining discrete selection of comb-lines with continuous radio-frequency tuning, the proposed scheme merges the advantages of single-sideband modulation and filter-based comb extraction. Using only off-the-shelf components, the system provides synthetic frequencies from 0.1-220GHz, corresponding to synthetic wavelengths from meters down to millimeters in the visible. The generator achieves MHz-level frequency accuracy, side-mode suppression exceeding 40dB, and switching times below 25ms, even without active stabilization. We characterize the spectral purity and frequency agility of the source and demonstrate rapid tuning of synthetic wavelengths over 3 orders of magnitude. We apply the light source to multi-wavelength digital holography and reconstruct the surface of an industrially machined metal part featuring height variations from 0.1-100mm. The measurements achieve ten-mum-level precision using 7 single wavelengths covering synthetic wavelengths from 1.36mm to 1.874m within an acquisition time < 2s. The presented architecture combines high dynamic measurement range of 50dB, fast electronic reconfigurability, and intrinsic frequency calibration, making it a promising light source for high-speed interferometric surface metrology.

Der Beitrag stellt eine markierungsfreie Track-&-Trace-Lösung für Elektroden- beziehungsweise Endlosmaterialien vor. Die Identifikation anhand der Oberflächenmikrostruktur ermöglicht die prozessübergreifende, eindeutige Zuordnung und Rückverfolgung einzelner Bandsegmente. Dies bildet die Grundlage für eine datenbasierte Prozessoptimierung. Die Systemintegration im ZDB und bei einem Industriepartner zeigte hohe Wiedererkennungsraten. Die Lösung ist skalierbar und branchenübergreifend einsetzbar.

This paper explores the optimization of light field deconvolution, a key process in image processing that reconstructs a 3D object space or a 2D refocus plane from a light field. Despite the critical role of deconvolution in light field technology, existing methods are often slow, computationally intensive, and unsuitable for real-time processing. Existing algorithms, such as the Richardson-Lucy approach, while groundbreaking, still suffer performance limitations due to their iterative nature and high computational costs. Central to our approach is the strategic selection of influential pixels within the point-spread-function, reducing redundant computations by focusing only on pixels contributing to a significant portion of the point-spread-function’s total intensity. In addition, we explore the potential to directly invert the image formation model, bypass iterative computations, and further accelerate the deconvolution process. Our findings reveal notable improvements in computational efficiency, with some of our methods achieving real-time performance. The reconstruction quality, measured using metrics such as the mean squared error, remained comparable to existing approaches, indicating a favorable balance between speed and reconstruction quality.

This article presents advancements in the Track & Trace Fingerprint technology applied to lithium-ion battery production, focusing on its innovative approach to material identification using unique surface microstructures. Traditional traceability methods often compromise material integrity through physical markers or fail when continuous material (e.g., electrode or other web material) is interrupted. This technology eliminates these issues by leveraging marker-free identification, enabling reliable tracking of continuous and segmented electrode materials without altering their properties. Experimental results demonstrate the effectiveness of the technology across various materials, including aluminum, copper, graphite, lithium-iron-phosphate, and nickel-manganese-cobalt coatings, with high identification rates and robust traceability. Additionally, software enhancements have improved predictive algorithms for estimating fingerprint locations, increasing processing speed and efficiency. Future developments will focus on graphics processing unit acceleration and optimized local database management to increase the current supported feed rate from 25 m min-1 to 60 m min-1 or more to broaden applicability. The technology's versatility extends beyond battery production, with potential applications in other continuous manufacturing processes, such as paper and steel production.

Single-wavelength interferometry achieves high resolution for smooth surfaces but struggles with rough, industrially relevant ones due to limited unambiguous measuring range and speckle effects. Multi-wavelength interferometry addresses these challenges by using synthetic waveleths, enabling a balance between extended measurement range and resolution by combining several synthetic wavelengths. This approach holds immense potential for diverse industrial applications, yet it remains largely untapped due to the lack of suitable light sources. Existing solutions are constrained by limited flexibility in synthetic-wavelength generation and slow switching speeds. We demonstrate a light source for multi-wavelength interferometry based on electro-optic single-sideband modulation. It reliably generates synthetic wavelengths with arbitrary values from centimeters to meters and switching times below 30 ms. This breakthrough paves the way for dynamic, reconfigurable multi-wavelength interferometry capable of adapting to complex surfaces and operating efficiently even outside laboratory settings. These capabilities unlock the full potential of multi-wavelength interferometry, offering unprecedented flexibility and speed for industrial and technological applications.

Spectral speckle correlation (SSC) enables fast, non-contact surface-roughness metrology, but object motion usually degrades its accuracy. We aim to enable measurements on moving objects and present a unified model that separates the total correlation into a wavelength-dependent term, from which the surface roughness is derived, and a kinematic term that quantifies motion blur, which affects the speckle contrast. The kinematic contribution yields a closed-form variance expression, similar to that in laser speckle contrast imaging (LSCI). The model was validated using a galvanized steel sheet with a roughness of 1.23 µm Sa, translated at up to 20 mm s-1 (approximately five speckles per exposure). Measured correlations agree with theory to within 1.06 % on average and 4.4 % at worst. Further improvements can reduce the error to 2.5 % for blur values smaller than 4 speckles per exposure. Scaling laws derived from the model indicate that the use of lasers with a power output of 125 mW and an illumination spot diameter of 5 mm can enable SSC at velocities up to 16 m s-1, which is comparable to typical industrial band feeding rates. The results establish SSC as a viable, high-throughput alternative to conventional profilometry for continuous in-line roughness monitoring of fast-moving surfaces.

Polymer films with a thickness in the two-digit micrometer range are coated with nanometer-thin oxide layers in roll-to-roll coating systems. The coating improves the properties of the film, such as gas or water permeation. Maintaining a sufficiently large coating thickness is crucial to ensure its barrier function; thus, inline quality control of the thickness is indispensable. For this purpose, we have developed a sensing principle that addresses specific absorption bands of the coating via a reflection measurement in the infrared spectral range. However, for thin and weakly absorbing polymer substrates, light is reflected not only by the coating and the surface of the polymer. Partly it is also transmitted and reflected by the backside of the film, leading to interference effects that significantly affect the measurement signal. As industrial films vary in thickness by several percent and their exact values are unknown, determining the thickness of an oxide coating is hindered. In this paper, we demonstrate an approach for measuring coating thickness on such varying polymer films by averaging the interferences obtained at multiple angles of incidence. Calculations and measurements on industrial film samples indicate the effectiveness of our approach. It produces results with ± 2 nm precision and ± 5 nm accuracy for a thickness in the range of 5–100 nm. Furthermore, we discuss a possible implementation of this approach in an inline measurement system by fulfilling its requirements, for example, versatility and compactness.

Single-wavelength interferometry achieves high resolution for smooth surfaces but struggles with rough industrially relevant ones due to limited unambiguous measuring range and speckle effects. Multiwavelength interferometry addresses these challenges using synthetic wavelengths, enabling a balance between extended measurement range and resolution by combining several synthetic wavelengths. This approach holds immense potential for diverse industrial applications, yet it remains largely untapped due to the lack of suitable light sources. Existing solutions are constrained by limited flexibility in synthetic-wavelength generation and slow switching speeds. We demonstrate a light source for multiwavelength interferometry based on electro-optic single-sideband modulation. It reliably generates synthetic wavelengths with arbitrary values from centimeters to meters and switching time below 30 ms. This breakthrough paves the way for dynamic reconfigurable multiwavelength interferometry capable of adapting to complex surfaces and operating efficiently even outside laboratory settings. These capabilities unlock the full potential of multiwavelength interferometry, offering unprecedented flexibility and speed for industrial and technological applications.

Surface roughness is a critical quality parameter in metal sheet processing, yet the current measurement methods are limited and just capable of assessing areas of up to a few square millimeters in-line. A fast measurement of larger areas can be achieved using the spectral speckle correlation (SSC), where the speckle pattern of the surface is captured at two slightly different wavelengths. It was shown that by correlating these two speckle fields, the surface roughness can be determined: the rougher the surface, the lower the correlation. This allows contact-free measurements, larger working distances, and the identification of local roughness variations within a single measurement, aligning with the areal integrating category of ISO 25178 part 6 standards. Previously, the roughness was evaluated at the image center or optical axis, yielding a single roughness value for the entire image. We demonstrate the SSC technique's ability to measure the areal roughness parameter Sq of roughness standards ranging from 0.81 to 2.07 mu m, with deviations of less than 5% compared with reference values obtained using a white light interferometer. The work includes the derivation of suitable sub-image sizes for spatial resolution, connection to existing standards, and the reduction of surface structure influences through defocused imaging and high-pass filtering. Experimental results show the method's potential for surface measurements of areas with a 15 mm diameter.

Digital holographic multiwavelength sensor systems integrated in the production line on multi-axis systems suchas robots or machine tools are exposed to unknown, complex vibrations that affect the measurement quality. Todetect vibrations during the early steps of hologram reconstruction, we propose a deep learning approach using adeep neural network trained to predict the standard deviation of the hologram phase. The neural network achieves96.0% accuracy when confronted with training-like data while it achieves 97.3% accuracy when tested with datasimulating a typical production environment. It performs similar to or even better than comparable classicalmachine learning algorithms. A single prediction of the neural network takes 35 mu s on the GPU

High-precision applications of multiwavelength holography typically requires stable laboratory-like environments, which is hard to achieve in industrial applications. The influence of schlieren is crucial, especially in large field-of-view applications, where long working distances can result in optical paths up to one meter. Schlieren is a well-known effect in interferometry and can be seen in the reconstructed object wavefronts. Small perturbations in temperature change the refractive index of air resulting in local variations of the optical path. Proper encapsulation or vacuum techniques are typically employed to compensate for this. In this work, we investigate the impact of schlieren on multiwavelength holography and propose a compensation method. The sensor used is a Mach-Zehnder-based interferometer with a field-of-view of 17.9mm x 13.4mm and a camera with 9344 pxx7000 px. A mirror was positioned at a distance of 1m in front of the sensor. We performed holographic and interferometric measurements with and without an encapsulating pipe around the beam to investigate the influence of schlieren. The deviations of the phase shifts of the holographic data were laterally resolved using a modified version of the algorithm proposed by CAI et al. The fringe patterns of the interferometric data captured with different exposure times and frame rates were analyzed using a sinusoidal fit and discrete Fourier transformations (DFT) to show lateral frequency deviations. Both methods show that encapsulation leads to improved measurements. A potential compensation method is proposed.

In diesem Beitrag präsentieren wir Ergebnisse einer Studie zur kamerabasierten, markierungsfreien Rückverfolgung von Baumstämmen vom Zeitpunkt der Ernte bis ins Sägewerk. Die Identifikation eines Baumstammabschnitts erfolgt lediglich durch die Analyse von Bilddaten der Stirnfläche. Zur weitgehend automatischen Erzeugung der Bilddaten wurden unterschiedliche Kamerasysteme entwickelt und in den Produktionsablauf integriert. Die erste Bildaufnahme erfolgte im Wald durch ein Vollernter-integriertes Kamerasystem, die zweite mittels handgehaltener Kamera an einem Polter und die dritte im Sägewerk nach der Anlieferung der Baumstammabschnitte. Insgesamt wurden 639 Stirnflächen jeweils an allen drei Stationen aufgenommen. Aus jedem Bild wurden automatisch in zwei Schritten mehrere stirnflächenindividuelle Merkmale extrahiert. Im ersten Schritt wurde ein Convolutional Neural Network (CNN) zur Extraktion großflächiger Merkmale im Bild verwendet. Im zweiten Schritt wurde die Mikrostruktur durch einen regelbasierten Bildverarbeitungsalgorithmus analysiert. Zur Identifikation einer Stirnfläche wurden zunächst die CNN-generierten Merkmale verglichen. Die zehn Kandidaten, die einer gesuchten Stirnfläche am ähnlichsten sind, wurden weiter in ihrer Mikrostruktur verglichen und so die gesuchte Stirnfläche identifiziert. Die dazu notwendige Ausrichtung der Stirnflächen im Bild erfolgte manuell. Die Aufnahmen von 574 Stirnflächen wurden für das Training des CNNs verwendet. Die Aufnahmen der übrigen 65 Stirnflächen wurden für die Tests der Identifikation herangezogen. Bis auf eine Stirnfläche konnten alle eindeutig identifiziert werden. Die Studie leistet einen wesentlichen Beitrag zur Frage, ob sich Baumstämme anhand von Fotos, die im Produktionsablauf automatisch erzeugt werden, markierungsfrei identifizieren lassen.