8 Zerstörungsfreie Prüfung
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Paper des Monats
- ja (26)
Machine learning-assisted passive thermography has emerged as a powerful tool for characterizing fatigue damage through self-heating-induced temperature hotspots, with its accuracy depending critically on the precise determination of the governing thermal properties. This work proposes a nondestructive method to simultaneously recover the transverse conductivity k_ct and the film coefficient h ─ two thermal parameters that are typically difficult to measure. To this end, a near-infrared laser in conjunction with a DLP-based spatial light modulator was used to create artificial temperature hotspot distributions by illuminating one side of a ±45° glass-epoxy composite laminate. The generated surface temperature distribution on the opposite side ─ denoted as ground truth (GT) ─ was recorded by a cooled midwave infrared thermal camera. Bayesian optimization (BO) was subsequently employed to iteratively suggest the best k_ct and h values for use in a 3D FEM thermal model, which in turn generated synthetic thermal images as similar as possible to the GT. Different BO runs converged after about 10 iterations, yielding effective values of k_ct = 0.40 (W/m K) and h = 5.78 W/(m^2 K), from where the loss objective decreased by a factor of ca. 3 within only seven iterative rounds. A final experimental validation using these optimized parameters successfully reproduced three independent thermal images with only minor deviations, demonstrating the robustness and applicability of the proposed approach.
Verdeckte Risse in Eisenbahn-Spannbetonschwellen können zum plötzlichen Versagen der Schwelle und dem Verlust der Spurhaltefähigkeit führen. Im schlimmsten Fall kann dies bei Überfahrt eines Schienenfahrzeugs eine Entgleisung bewirken. Einige Risstypen entstehen im Inneren der Schwelle und breiten sich unerkannt aus. Dies führt weit vor der äußeren Sichtbarkeit bereits zu einer deutlichen Minderung der Querschnittsfestigkeit und damit Instabilität der Schwelle.
Der Mensch ist in der Lage, sehr schnell den Zustand seiner Umgebung zu erfassen, indem er Informationen über verschiedene Sinneskanäle aufnimmt und miteinander verarbeitet. Für technische Systeme steht heute eine Vielzahl preiswerter Sensoren zur Verfügung, die in ihrer Leistungsfähigkeit teils deutlich über die menschlichen Sinnesorgane hinausgehen. Dennoch ist eine ähnlich umfassende Bewertung der Umgebung noch nicht möglich, weil die einzelnen Sensordaten nicht ausreichend fusioniert und interpretiert werden.
In der Anlagenüberwachung nicht nur in der chemischen Industrie sind heute dedizierte, d. h. für genau eine spezifische Applikation ausgelegte Sensorsysteme üblich, meist verbunden mit hohen Kosten, u. a. wegen der geringen Stückzahlen, die keine Economy of Scale erlauben. Ergänzt werden diese begrenzten technischen Systeme durch den Menschen, der mit seiner 'Sensorik' ungewöhnliche Zustände und mögliche Gefahrensituationen allerdings nur punktuell erfassen kann.
Durch die Verfügbarkeit ausreichender Rechenleistung zur Interpretation der entstehenden Datenflut ist ein Paradigmenwechsel in der sensorischen Überwachung von Anlagen möglich, der in diesem Projekt erstmalig adressiert werden soll. Erwünscht ist eine flächige Erfassung multimodaler Anlagendaten und damit eine deutliche Verbesserung der Bewertungsmöglichkeiten, z.B. zur frühzeitigen Erkennung von Leckagen bei Energie-trägern (Druckluft, Wasserdampf, Gas und zukünftig zunehmend Wasserstoff).
Evaluation of AI for eddy current testing according to the reliability framework for rail inspection
(2026)
The requirements for using AI algorithms are highly stringent for safety-critical and high-risk applications, such as in the field of non-destructive testing (NDT). Furthermore, there is a regulatory need for conclusive metrics to evaluate AI for high-risk applications. This article introduces a process for evaluating AI used in NDT methods. Based on a commonly used AI evaluation metric adapted for the NDT field, the evaluation aligns with the well-known NDT reliability processes. This evaluation process is applied to analyzing eddy current (ET) data in rail inspection. The aim is to quantify the capability of AI-supported data evaluation against that of the testing setup itself. However, the available real-world dataset of rail inspection data was insufficient for training, validation and demonstrating the reliability of the ET process and AI. To address this, simulated ET data was used to generate a large dataset for evaluation purposes. Using this simulated data, a reference probability of detection (POD) curve was created to provide a benchmark for assessing the performance of the AI using a newly introduced metric called Reliability Metric Score (RESa). The AI model analyzed ET data for crack-like defects. The results were then evaluated and compared to the reference POD. This article explores the evaluation process, highlighting potential misinterpretations and situations where an operator’s judgment is necessary to determine the effectiveness of the AI model in specific cases. This process revealed different regions of interest, which are very useful for further assessment of the AI process and continued development.
In this study, the authors investigate the detection of fatigue crack initiation from corrosion pits in offshore structural steel. Fatigue tests were conducted on corroded steel specimens that were previously exposed to a marine environment inside the monopiles of two offshore wind farms. The temperature evolution of the test specimens was monitored using infrared thermography. The moment of crack initiation was determined using thermoelastic amplitude and phase signals derived from a thermographic analysis. A thresholding method was applied to identify this moment for each specimen individually, accounting for variability caused by corrosion severity, loading conditions, and thermography configurations. Fractographic analysis was performed to characterize the pits from which fatigue cracks were initiated, revealing strong correlations between pit morphology, corrosion stage, and fatigue crack initiation. To further quantify the role of pit geometry, stress concentration factors (SCFs) were estimated based on pit dimensions, showing systematic trends with exposure height and confirming that sharper isolated pits act as stronger stress raisers than wide valleys. Results show that the proposed approach can reliably detect the onset of stress redistribution in the sample upon crack initiation. The methodology demonstrates high sensitivity to early-stage structural changes under challenging surface conditions and strengthens the understanding of how corrosion pit morphology influences fatigue crack initiation.
Mikrolegierungselemente wie Niob (Nb) und Titan (Ti) spielen eine entscheidende Rolle bei der Einstellung der gewünschten mechanischen Eigenschaften vergüteter hochfester Feinkornbaustähle mit einer Nennstreckgrenze von ≥ 690 MPa. Aktuelle Spezifikationen der chemischen Zusammensetzung definieren für diese Elemente lediglich Obergrenzen und gewähren den Herstellern damit einen gewissen Spielraum. Bereits geringfügige Abweichungen in den Legierungskonzepten können jedoch die resultierenden mechanischen Eigenschaften erheblich beeinflussen. Infolgedessen wird die zuverlässige Vorhersage der Schweißeignung sowie der Integrität geschweißter Verbindungen aufgrund von Zusammensetzungsvariationen und den damit verbundenen mikrostrukturellen Veränderungen erschwert oder sogar unmöglich. Mögliche nachteilige Effekte umfassen eine Aufweichung der Wärmeeinflusszone (WEZ) oder umgekehrt lokale Aufhärtungsphänomene. Zur Bewältigung dieser Herausforderungen werden erstmals verschiedene Mikrolegierungsstrategien mit unterschiedlichen Ti- und Nb-Gehalten systematisch anhand speziell hergestellter, im Labor gegossener Legierungen untersucht. Jeder Legierungsansatz basiert auf dem häufig verwendeten Stahl S690QL, wobei eine konsistente chemische Zusammensetzung sowie identische Wärmebehandlungsparameter beibehalten werden.
Zur Bewertung der Schweißeignung wurden dreilagige Schweißverbindungen mittels Metall-Schutzgasschweißen durchgeführt und kritische mikrostrukturelle Bereiche – insbesondere solche innerhalb der Wärmeeinflusszone (WEZ) mit ausgeprägter Aufweichung oder Aufhärtung – identifiziert. Der Einfluss der aufgeweichten WEZ-Bereiche auf das Versagensverhalten wurde durch Querzugversuche untersucht. Zur In-situ-Analyse lokaler Dehnungsverteilungen in verschiedenen WEZ-Regionen wurde die digitale Bildkorrelation (DIC) eingesetzt. Darüber hinaus wurden Kerbschlagbiegeversuche (Charpy) an Grundwerkstoff, Schweißgut und WEZ durchgeführt, um die Kerbschlagzähigkeit zu bestimmen.
The thermal performance of metallic brake lining materials plays a decisive role in the safety and efficiency of high-speed railway braking systems. In this study, a combined experimental–numerical methodology is developed to rationalize the influence of microstructural constituents on the effective thermal conductivity of a sintered metal matrix composite (MMC) brake lining. Laser Flash Analysis (LFA) is first employed to determine the thermal conductivity of some individual constituents as well as that of reference composites. X-ray CT (XCT) provides three-dimensional reconstructions of the microstructure that are subsequently used to generate realistic image-based finite element meshes. The unknown thermal conductivities of the graphite particles are identified through a Finite Element Model Updating (FEMU) scheme, where numerical predictions of the effective conductivity are iteratively matched to LFA measurements. These findings highlight the strong anisotropy of graphite particles and their favored orientation after compaction, which governs heat transport pathways. Moreover, the presence of intra-, inter-, and inter-connectivity porosity within and around the graphite is shown to significantly reduce the transverse conductivity, rationalizing the discrepancy between the FEM predictions and experimental values. Overall, the proposed approach demonstrates how combining LFA, XCT and FEMU enables the identification of constituent-level conductivities and provides new insights into the microstructure/thermal-property relationships of MMC brake linings.
Controlling trace humidity is vital for both the fabrication and long-term stability of metal halide perovskite (MHP) solar cells. Relevant humidity levels are typically below 10 ppmV, especially in glovebox-based processing and in well-encapsulated devices. Even minute amounts during fabrication can influence crystallization, introducing defects and lowering efficiency. Over time, humidity accelerates degradation of the perovskite layer and internal interfaces, ultimately reducing operational lifetime. Probing these effects at low concentrations under operando conditions is therefore essential for advancing device performance and durability. In this work, we employed a high-precision transfer standard dew point hygrometer to investigate humidity levels between 5 and 35 ppmV in non-encapsulated MHP solar cells. To permit unobstructed water migration during operation, we fabricated interdigital back contact devices. Operando measurements revealed water transport through the perovskite layer and enabled quantification of outgassing. Under trace-humidified conditions, devices exhibited initial charge-carrier quenching, followed by gradual recovery. Notably, the photocurrent response to humidified nitrogen demonstrated that the MHP layer behaves fully reversibly within the explored timescale and across the investigated humidity levels and conditions. These findings establish a systematic operando framework for examining extrinsic stressors in perovskites and highlight opportunities for assessing passivation strategies.
Controlling trace humidity is vital for both the fabrication and long-term stability of metal halide perovskite (MHP) solar cells. Relevant humidity levels are typically below 10 ppmV, especially in glovebox-based processing and in well-encapsulated devices. Even minute amounts during fabrication can influence crystallization, introducing defects and lowering efficiency. Over time, humidity accelerates degradation of the perovskite layer and internal interfaces, ultimately reducing operational lifetime. Probing these effects at low concentrations under operando conditions is therefore essential for advancing device performance and durability. In this work, we employed a high-precision transfer standard dew point hygrometer to investigate humidity levels between 5 and 35 ppmV in non-encapsulated MHP solar cells. To permit unobstructed water migration during operation, we fabricated interdigital back contact devices. Operando measurements revealed water transport through the perovskite layer and enabled quantification of outgassing. Under trace-humidified conditions, devices exhibited initial charge-carrier quenching, followed by gradual recovery. Notably, the photocurrent response to humidified nitrogen demonstrated that the MHP layer behaves fully reversibly within the explored timescale and across the investigated humidity levels and conditions. These findings establish a systematic operando framework for examining extrinsic stressors in perovskites and highlight opportunities for assessing passivation strategies.
The room temperature cyclic plastic deformation behavior of stainless steel 316L produced by laser powder bed fusion and heat treated to two microstructural conditions was investigated in strain-controlled incremental-step-test low-cycle fatigue experiments. The heat treatments were performed at 450 °C for 4 h and at 900 °C for 1 h. The lower temperature heat treatment retains the cell structure present in the as-built material. The higher temperature heat treatment leads to disappearance of the cell structure and a decreased proof strength. Both investigated heat treatment conditions exhibited cyclic softening. In the condition heat treated at 900 °C for 1 h, the ability of the cell structure to act as barrier against plastic deformation when cyclically strained is degraded, which is reflected in the reduction of the cyclic yield strength. In that same condition, the cyclic softening was less pronounced. The presence or absence of the manufacturing-induced cell structure seems to determine the slip mode. When present, the microstructural evidence points to a planar slip behavior. After heat treatment at 900 °C for 1 h, which led to its dissolution, microstructural investigations revealed a wavy slip behavior, which has been also reported for the conventionally manufactured 316L counterpart [1]. In this case, the formation of low-energy dislocation structures acts as softening agent.