8.3 Thermografische Verfahren
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Infrared thermography (IRT) using a focused laser is effective for surface defect detection. Nevertheless, testing complex‐shaped components remains a challenging task. The state‐of‐the‐art focuses on testing a limited region of interest rather than the full sample. Thus, detection and location of surface defects has been less researched. Most attempts require a manual scan of the full sample, which makes it hard to reconstruct the full scanned surface. Here, we introduce a reliable workflow for crack detection and semi‐automated inspection of complex‐shaped components using IRT excited with a laser line. A 6‐axis robot arm is used for moving the sample in front of the setup. This approach has been tested on a section of a rail and a gear, both containing defects due to heavy use. Crack detection is based on the segmentation of thermograms obtained by Fourier transform of sorted temperatures. Moreover, texture mapping is used to visualize a reconstructed thermogram on the 3D model of the sample. Our approach illustrates a reliable process towards the digitalization of thermographic testing.
In this work, we continue to develop and investigate the Thermal Shock Response Spectrum (TSRS) method as an alternative data processing method for infrared thermography (IRT). We focus on improving the current TSRS algorithm and present an optimization methodology for finding the optimal thermal Q-factor and characteristic frequency pair, which is based on the widely applied random sampling method. We show the qualitative relationship between the determined optimal characteristic frequency and the corresponding maximum difference in diffusion length between reference and defective models, as calculated by selecting a specific one-dimensional thermal N-layer model. The investigations were performed on an inhomogeneous plate made of carbon fiber reinforced polymer (CFRP) with artificial square defects at different depths. Furthermore, two different heat sources were used: a xenon flash lamp and a laser. These sources are not only distinct by their underlying physics but also generate inherently different pulse shapes. To quantitatively estimate the contrast between defect and non-defect areas, and to compare these results with commonly used infrared thermography (IRT) data post-processing methods such as Pulse Phase Thermography (PPT) and Thermographic Signal Reconstruction (TSR), the Tanimoto criterion (TC) and signal-tonoise ratio (SNR) were used.
Safety-critical applications of products manufactured by laser powder bed fusion (PBF-LB/M) are still limited to date. This is mainly due to a lack of knowledge regarding the complex relationship between process, structure, and resulting properties. The assurance of homogeneity of the microstructure and homogeneity of the occurrence and distribution of defects within complexly shaped geometries is still challenging. Unexpected and unpredicted local inhomogeneities may cause catastrophic failures. The identification of material specific and machine specific process parameter windows for production of fully dense simple laboratory specimens is state of the art. However, the incorporation of changing thermal conditions that a complexly shaped component can be faced with during the manufacturing process is often neglected at the stage of a process window determination. This study demonstrates the tremendous effect of changing part temperatures on the defect occurrence for the broadly used stainless steel alloy AISI 316L. Process intrinsic variations of the surface temperature are caused by heat accumulation which was measured by use of a temperature adjusted mid-wavelength infrared (MWIR) camera. Heat accumulation was triggered by simple yet effective temporal and geometrical restrictions of heat dissipation. This was realized by a variation of inter layer times and reduced cross section areas of the specimens. Differences in surface temperature of up to 800 K were measured. A severe development of keyhole porosity resulted from these distinct intrinsic preheating temperatures, revealing a shift of the process window towards unstable melting conditions. The presented results may serve as a warning to not solely rely on process parameter optimization without considering the actual process conditions a real component is faced with during the manufacturing process. Additionally, it motivates the development of representative test specimens.
Fused filament fabrication (FFF) is a material extrusion-based technique often used in desktop 3D printers. Polymeric filaments are melted and are extruded through a heated nozzle to form a 3D object in layers. The extruder temperature is therefore a key parameter for a successful print job but also one of the main emission driving factors as harmful pollutants (e.g., ultrafine particles) are formed by thermal polymer degradation. The awareness of potential health risks has increased the number of emission studies in the past years. However, studies usually refer their calculated emission data to the printer set extruder temperature for comparison purposes. In this study, we used a thermocouple and an infrared camera to measure the actual extruder temperature and found significant temperature deviations to the displayed set temperature among printer models. Our result shows that printing the same filament feedstocks with three different printer models and with identical printer set temperature resulted in a variation in particle emission of around two orders of magnitude. A temperature adjustment has reduced the variation to approx. one order of magnitude. Thus, it is necessary to refer the measured emission data to the actual extruder temperature as it poses a more accurate comparison parameter for evaluation of the indoor air quality in user scenarios or for health risk assessments.
The detection of internal irregularities is crucial for quality assessment in metal-based additive manufacturing (AM) technologies such as laser powder bed fusion (L-PBF). The utilization of in-process thermography as an in situ monitoring tool in combination with post-process X-ray micro computed tomography (XCT) as a reference technique has shown great potential for this aim. Due to the small irregularity dimensions, a precise registration of the datasets is necessary as a
requirement for correlation. In this study, the registration of thermography and XCT reference datasets of a cylindric specimen containing keyhole pores is carried out for the development of a porosity prediction model. The considered datasets show variations in shape, data type and dimensionality, especially due to shrinkage and material elevation effects present in the manufactured part. Since the resulting deformations are challenging for registration, a novel preprocessing methodology is introduced that involves an adaptive volume adjustment algorithm which is based on the porosity distribution in the specimen. Thus, the implementation of a simple three-dimensional image-to-image registration is enabled. The results demonstrate the influence of the part deformation on the resulting porosity location and the importance of registration in terms of irregularity prediction.
Laser powder bed fusion is used to create near net shape metal parts with a high degree of freedom in geometry design. When it comes to the production of safety critical components, a strict quality assurance is mandatory. An alternative to cost-intensive non-destructive testing of the produced parts is the utilization of in-situ process monitoring techniques. The formation of defects is linked to deviations of the local thermal history of the part from standard conditions. Therefore, one of the most promising monitoring techniques in additive manufacturing is thermography. In this study, features extracted from thermographic data are utilized to investigate the thermal history of cylindrical metal parts. The influence of process parameters, part geometry and scan strategy on the local heat distribution and on the resulting part porosity are presented. The suitability of the extracted features for in-situ process monitoring is discussed.
Laser powder bed fusion is used to create near net shape metal parts with a high degree of freedom in geometry design. When it comes to the production of safety critical components, a strict quality assurance is mandatory. An alternative to cost-intensive non-destructive testing of the produced parts is the utilization of in-situ process monitoring techniques. The formation of defects is linked to deviations of the local thermal history of the part from standard conditions. Therefore, one of the most promising monitoring techniques in additive manufacturing is thermography. In this study, features extracted from thermographic data are utilized to investigate the thermal history of cylindrical metal parts. The influence of process parameters, part geometry and scan strategy on the local heat distribution and on the resulting part porosity are presented. The suitability of the extracted features for in-situ process monitoring is discussed.
Recording the temperature distribution of the layer under construction during laser powder bed fusion (L-PBF) is of utmost interest for a deep process understanding as well as for quality assurance and in situ monitoring means. While having a notable number of thermal monitoring approaches in additive manufacturing (AM), attempts at temperature calibration and emissivity determination are relatively rare. This study aims for the experimental temperature adjustment of an off-axis infrared (IR) thermography setup used for in situ thermal data acquisition in L-PBF processes. The temperature adjustment was conducted by means of the so-called contact method using thermocouples at two different surface conditions and two different materials: AISI 316L L-PBF bulk surface, AISI 316L powder surface, and IN718 powder surface. The apparent emissivity values for the particular setup were determined. For the first time, also corrected, closer to real emissivity values of the bulk or powder surface condition are published. In the temperature region from approximately 150 °C to 580 °C, the corrected emissivity was determined in a range from 0.2 to 0.25 for a 316L L-PBF bulk surface, in a range from 0.37 to 0.45 for 316L powder layer, and in a range from 0.37 to 0.4 for IN718 powder layer.
Thanks to its good strength/mass ratio, a glass fibre reinforced plastic (GFRP) composite is a common material widely used in aviation, power production, automotive and other industries. In its turn, active infrared (IR) nondestructive testing (NDT) is a common inspection technique for detecting and characterizing structural defects in GFRP. Materials to be tested are typically subjected to optical heating which is supposed to occur on the material surface. However, GFRP composite is semitransparent for optical radiation of both visual and IR spectral bands. Correspondingly, the inspection process represents a certain combination of both optical and thermal phenomena. Therefore, the known characterization algorithms based on pure heat diffusion cannot be applied to semi-transparent materials. In this study, the phenomenon of GFRP semi-transparency has been investigated numerically and experimentally in application to thermal NDT. Both Xenon flash tubes and a laser have been used for thermal stimulation of opaque and semi-transparent test objects. It has been shown that the Penetration of optical heating radiation into composite reduces detectability of shallower defects, and the signal-to-noise ratio can be enhanced by applying the technique of thermographic signal reconstruction (TSR). In the inspection of the semi-transparent GFRP composite, the most efficient has been the laser heating followed by the TSR data processing. The perspectives of defect characterization of semi-transparent materials by using laser heating are discussed. A neural network has been used as a candidate tool for evaluating defect depth in composite materials, but its training should be performed in identical with testing conditions.
Among additive manufacturing (AM) technologies, the laser powder bed fusion (L-PBF) is one of the most important technologies to produce metallic components. The layer-wise build-up of components and the complex process conditions increase the probability of the occurrence of defects. However, due to the iterative nature of its manufacturing process and in contrast to conventional manufacturing technologies such as casting, L-PBF offers unique opportunities for in-situ monitoring. In this study, two cameras were successfully tested simultaneously as a machine manufacturer independent process monitoring setup: a high-frequency infrared camera and a camera for long time exposure, working in the visible and infrared spectrum and equipped with a near infrared filter. An AISI 316L stainless steel specimen with integrated artificial defects has been monitored during the build. The acquired camera data was compared to data obtained by computed tomography. A promising and easy to use examination method for data analysis was developed and correlations between measured signals and defects were identified. Moreover, sources of possible data misinterpretation were specified. Lastly, attempts for automatic data analysis by data Integration are presented.