Open Access

Compared with conventional optical excitation thermography techniques, such as pulsed thermography (PT), lock-in thermography (LIT), step-pulse thermography (SPT) and linear-chirp thermography, chirped-pulse thermal-wave radar thermography has been demonstrated to provide a higher signal-to-noise ratio (SNR) and more effective depth-profiling capability. This enhanced performance originates from the use of short chirped pulses, which maintain a nearly flat power spectrum over a broad bandwidth, even in the presence of diffusive attenuation. However, conventional laser-based signal-modulated heating schemes require both spatial and temporal modulation – typically generating a rectangular heating area via projection or lensing, followed by digital waveform modulation. This leads to localized heating areas and prolonged heating durations, which significantly limit their applicability in practical industrial inspections. In addition, industrial samples, such as cast components, often exhibit large-scale and complex geometries. To perform infrared thermography on such samples, they must typically be divided into multiple sub-areas for sequential heating and recording, introducing pronounced boundary effects and complex data concatenation issue. In this work, we propose a spatially modulated heating strategy combined with a robotic arm moving at a constant speed. In details, the heating source consists of multiple linear heating strips arranged with different spatial intervals, while the heat source and infrared camera remain fixed. The robotic arm moves the sample at a constant speed across the heating and recording area. Using a dynamic-to-static reconstruction algorithm, the chirped-pulse thermal-wave radar signal can be generated for each pixel. This approach enables continuous chirp-pulse thermal-wave radar thermography by replacing temporal modulation with spatial modulation, thereby not only simplifying the system and reducing overall cost but also allowing for uniform heating of large and complex samples. Experiments and simulations are conducted to optimize key parameter, and various image processing algorithms - including principal component thermography (PCT), pulsed phase thermography (PPT), and partial least square regression (PLSR) - are applied to further enhance detectability. Image quality is quantitatively evaluated using signal-to-noise ratio metrics. The results demonstrate that this method holds strong potential for real-world industrial inspection applications. Moreover, the strategy of replacing temporal modulation with spatial modulation is expected to be extended to other emerging applications, such as random-coded and adaptive thermography, broadening its versatility in non-destructive testing.

Temperature represents a key characteristic in additive manufacturing (AM) processes for metals. As a physical quantity, temperature provides a direct measure of the actual process state and can be an indicator for the process quality. Consequently, process evaluation based on temperature measurements, rather than solely on the monitoring of process radiation in gray values, is expected to offer increased robustness and explanatory power. However, dependable quantitative in-situ temperature measurements remain highly challenging due to extreme temperature gradients, emissivity changes and, in case of the widely used process laser powder bed fusion of metals (PBF-LB/M), due to the required high spatial and temporal resolution. To nevertheless obtain reliable temperature measurements, besides other factors, a robust thermal calibration of the measurement system is fundamental. The de facto standard for the thermal calibration of thermal camera systems is the use of black-body radiators, as set out in several technical norms and standards (among others: ASTM E1933, IEC 62942, VDI 5585, VDI 3511). However, especially in the visible and near-infrared (VIS-NIR) range, this is not always feasible in practical applications. Apart from the high costs of calibrated blackbody radiators, their calibration is usually not valid in this wavelength range and significant deviation to black body radiation occurs. Furthermore, their physical size usually prohibits use within a PBF-LB/M build chamber, so it is not possible to calibrate the entire optical path with this type of calibration source. This limitation is exacerbated by machine- and system-specific optical interfaces (e.g., protective windows and viewports) that must be traversed in operation and can alter transmission and spectral response. In-situ solutions are therefore recommended, as these enable calibration of the complete on-machine optical path. In this contribution, three different proof-of-concept approaches for the thermal calibration of monitoring systems for the PBF-LB/M process are presented and compared: 1) Calibration using blackbody-like radiation sources - For this purpose, the suitability of the pipetting hole of the graphite tube of a graphite furnace atomic absorption device and a graphite cylinder heated by the PBF-LB/M process laser with a suitable borehole are examined. 2) Calibration using a calibrated halogen light source and an isotopic (Ulbricht) sphere with a known color temperature – as special case for the VIS/NIR range and 3) the single-point calibration at the solidification plateau of molten metal samples – the melting also takes place within the PBF-LB/M process chamber using the process laser. While, as described, some of the approaches presented here are ex-situ, others can be carried out in situ in the PBF-LB/M process chamber, allowing the calibration of the complete optical path. As a ground-truth, additional measurements at a calibrated blackbody radiator were performed. The calibration approaches are tested using a Multispectral Optical Tomography (MS-OT) sensor system. MS-OT operates in the VIS-NIR range and constitutes an approach for determining apparent maximum surface temperatures Tmax during the PBF-LB/M process.

Cold Spray Additive Manufacturing (CSAM) is a solid-state repair technique for metallic components, where the structural integrity of the repair–substrate interface remains a critical concern under fatigue loading. Infrared thermography (IRT) with lock-in post-processing has shown high sensitivity to early interfacial damage through the detection of local heat generation associated with microcrack formation. In parallel with these thermal measurements, selected CSAM-repaired specimens were investigated using a stereo digital image correlation (DIC) system to obtain full-field surface strain information during cyclic loading. The DIC measurements do not provide the same early indication of subsurface damage at the repair–substrate interface as observed with IRT. However, in cases where edge cracks develop, the initiation and subsequent growth detected by DIC align closely with the timelines indicated by the thermal signals. The combination of subsurface-sensitive IRT and surface-sensitive DIC therefore enables a more comprehensive assessment of crack-initiation behaviour in CSAM repairs, offering complementary insight into the onset and progression of interfacial failure mechanisms during fatigue. The presentation will also include results from tensile tests examined with both techniques.

Active thermography is increasingly used in automated non-destructive testing, yet freshly cast grey iron components remain challenging due to irregular surface topography and strong spatial emissivity variations. Reliable data-driven correction strategies require physically consistent reference data, which are currently scarce for industrial cast surfaces. This work presents the acquisition and structure of a dedicated emissivity reference dataset for grey cast iron components. Twenty-six samples were characterized inside a MWIR-compatible integrating sphere coated with Infragold to obtain controlled radiometric measurements. Each sample was recorded from both sides and from two defined perspectives within the sphere, enabling spatially resolved emissivity characterization under uniform radiance conditions. In addition, robot-assisted multimodal inspection data were acquired under realistic conditions. For each component side, thermal and high-resolution VIS images were recorded from six fixed and six randomized positions, capturing geometric and surface-dependent emissivity effects across varying viewpoints. The resulting dataset establishes a reproducible and physically grounded basis for supervised learning approaches targeting emissivity estimation and thermographic artifact compensation. Ongoing work focuses on model development and quantitative validation to support robust and automated NDT workflows for complex cast surfaces.

Thermal Barrier Coatings (TBCs) are critical for high-temperature applications, such as gas turbines and aerospace engines, protecting metallic substrates from extreme thermal stress and degradation. Accurate evaluation of TBCs is essential to improve operational efficiency and extend component life. Conventional non-destructive evaluation (NDE) techniques such as infrared thermography (IRT) and terahertz (THz) imaging have been widely used for TBC inspection with limitations when used independently, including sensitivity to surface conditions, limited penetration depth mainly in multi-layer coatings. This study proposes a novel framework called simulation-assisted multimodal deep learning (Sim-MDL) that combines IRT and THz data for a comprehensive evaluation of TBCs. To generalize the study to varying thermophysical properties of TBCs, the study uses simulation-generated data along with experimental data for training deep learning models. Two deep learning frameworks based on a 1D convolutional neural networks (CNN) and a long short-term memory (LSTM) with attention were developed for the multimodal feature fusion. The IR-THz fused frameworks enable simultaneous prediction of key TBC topcoat parameters including thermal conductivity, heat capacity, topcoat thickness and refractive index. The proposed Sim-MDL framework outperformed single-modality and conventional parameter estimation methods in accuracy, highlighting the potential of multimodal data for automated analysis of TBC.