5. Quality Control and Characterization

Quality inspection is an essential tool for quality assurance during production. Generally, the precise geometrical and surface inspection of a test object plays a decisive role for developing and/or optimizing the manufacturing process. In the microscopic domain, where the manufactured objects have a size of less than 1 mm in at least two dimensions, very often mass production takes place with high demands regarding the failure rate, as micro components generally form the basis for larger assemblies. Especially when it comes to safety-relevant parts, e.g. in the automobile or medical industry, often a 100% quality inspection is mandatory [Kop13].

Achieving 100% quality inspection is especially challenging in the micro-domain, e.g. when measurement uncertainty has to be in the range of a few microns, as there is usually a trade-off between precision and speed [Ago17]. Processes like micro cold forming, however, allow for production rates of multiple parts per second [Flo14]. Presently, no solutions exist that are fast, precise and suitable for in-process measurements at the same time [Ago17b]. Actually, most industrial quality inspection processes of micro parts rely on manual sampling using confocal microscopy. This means also that no automatic data processing regarding the geometry and surface inspection is applied because such measurements are affected by strong image noise.

Due to these industrial requirements, methods such as tactile, confocal microscopy and phase retrieval [Fal12a] by means of multiple illumination directions [Ago10] are not suited for the inspection task since they are much too slow. In contrast to these methods, interferometric methods are established for a full field measurement. Examples of interferometric techniques include digital holography (DH) [Sch94], white-light interferometry (WLI) [Wya98] and computational shear interferometry (CoSI) [Fal15]. These methods are based on the determination of the optical path difference (OPD) of light scattered by or transmitted through a test object. Since available cameras can only measure intensities of light, the OPD must be encoded consequently. Based on the encoding technique utilized and the geometrical complexity of the test object, measurement uncertainties down to a fraction of the illumination’s wavelength can be achieved [Mar05].

Here, we present a robust, fast and precise metrology method comprised of a holographic contouring system, as well as fast algorithms for geometric evaluation and surface defect detection, that paves the way for the inspection of metallic micro cups in less than a second. The holographic system is composed of four two-wavelength digital holographic microscopy setups. It uses four directions of illumination in order to enable the simultaneous observation of the whole object surface and for speckle noise reduction as well as two-wavelength contouring to collect form data. Spatial multiplexing, coherent gating, is used to capture four holograms by each camera in a single shot [Ago17]. Moreover, it utilizes a telecentric microscope objective instead of a standard microscope objective to compensate scaling effects and wave field curvature, which distort reconstruction in digital holographic microscopy. In order to enhance the limited depth of focus of the microscope objective, depth information from different object layers is stitched together to yield 3D data of its geometry utilizing the auto-focus approach presented in [Ago18].

The 3D data map is converted into a point cloud. The resulting point cloud data are automatically decomposed into simple geometric elements with a holistic approximation approach in order to analyze geometric deviations. In addition, surface defects are detected with a convolutional neural network. The measurement and data evaluation approaches are demonstrated by quality inspection of cold formed micro cups. As a result, defects larger than 2 μm in the lateral and 5 μm in the axial (depth) dimension can be detected.

The present section is organized in the following three subsections: Sect. 5.2.2 describes the measurement approach for dimensional inspection that allows the geometric features of deep-drawn micro cups to be characterized. Section 5.2.3 describes the dimensional inspection that allows for an automated dimensional analysis of prismatic surface data, where the surface can be a combination of different simple geometric base bodies (cylinder, plane, torus, etc.). Section 5.2.4 describes the detection of surface defects using convolutional neural networks (CNNs), which achieve high accuracy with only a few training samples.

Dimensional inspection implies the evaluation of surface data with respect to dimensional, form and position deviations of certain geometric features. These deviations are compared to the specified tolerances in order to decide whether the workpiece meets the quality requirements or not. The following subsections give a brief survey of the state of the art in evaluating point clouds and present the holistic approximation as the method of choice for the dimensional inspection of optically acquired surfaces of micro parts.

Surface defects such as scratches or dirt might be too small to cause a detectable change in the measured phase distribution. Hence, reliable detection necessitates additional methods, which incorporate the measured amplitude image.

Micro cold formed parts are produced in high quantities, as many of such are incorporated inside a complete system. The mass production of micro parts can only be efficient if the quality inspection of these parts is incorporated within the production line. Optical metrology offers the opportunity to determine the shape of the structures of such parts and allows for quality control. Automated quality control with the help of an automated optical system within the industrial production line reduces the costs and time that would otherwise be required for a sophisticated manual inspection. Up to now, tactile methods have been used to inspect components on a sample basis, but these are not suitable for fast quality inspection in the production line as they are too slow and might alter the sample.

Among the non-tactile methods, confocal microscopy is commonly used for inspection but is clearly too slow for an automated 100% inspection. An overview of such methods can be found in [Ber12, Kop13]. Alternatively, white-light interferometry (WLI) is suitable, as it measures with high speed and is highly precise. For a review of WLI, see [Gro15]. However, WLI requires a comparatively large number of recordings, commonly by depth scanning, to capture depth information.

Digital holography (DH) is precise and only requires a small number of recordings to obtain the object shape, as shown in [Fal15]. This makes it a good candidate for the fast three-dimensional inspection of micro parts. Usually DH uses the method of temporal phase-shifting for phase evaluation, which is generally realized with a piezoelectric device. To realize a system which exhibits an even higher robustness, the method of two-frame phase-shifting is used to measure the object shape in two consecutive exposures.

For the inspection of the interior of a micro deep drawing component with DHM, a Michelson interferometer based setup, shown in Fig. 5.16, is constructed with the object in one arm and a plane mirror in the other arm to provide a reference wave. The Michelson interferometer has been placed as close to the object as possible in order to minimize the lengths of the separated light paths. The object is introduced to the setup with the help of a positioning apparatus, constructed by the industrial partner Stüken Corp. Furthermore, the usage of polarization optics and two cameras enables the simultaneous recording of two phase-shifted images. In this configuration, the setup is more stable with respect to exterior disturbances compared to a setup using a piezoelectric device.

After leaving the fiber switch, the light is parallelized with the help of a collimating lens and is linearly polarized. A λ/4 plate then polarizes the light circularly and illuminates the object through a beam splitter. At the same time, half of the intensity is redirected and linearly polarized again to illuminate the reference mirror. After traveling through a microscope with 5x magnification, the light is again divided with the help of a polarization-sensitive beam splitter to lead it to two camera targets at the same time. By using such a beam splitter, two interference patterns are projected on the camera targets, shifted by 90°.

As light sources, two diode lasers with output powers of \( P_{1} = 70 \) mW and \( {\text{P}}_{2} = 20 \) mW and with wavelengths \( \uplambda_{1} = 642.2 \) nm and \( \uplambda_{2} = 637.8 \) nm are used. With that, the camera exposure times were set to 8 ms to fully illuminate the camera targets. With the employed wavelengths, the synthetic wavelength amounts to \( \Lambda = 93.1 \) µm. To avoid coherent amplification and to minimize speckle noise, laser light with coherence lengths of less than 1 mm is used.

For the effective utilization of the setup’s measurement speed, manual evaluation of the measurements is not feasible. Hence a solution for automatic defect detection was developed. Thus the challenge was threefold: Firstly, the method had to be fast, as a slow algorithm would be detrimental to the fast measurement system. Secondly, defect detection had to be very accurate, with zero false negatives (undetected defects) and less than 4% false positives (intact parts falsely labeled as defective). Thirdly, due to the well optimized process, the number of defective samples was very small. Consequently, the application of state-of-the-art machine learning methods, like e.g. convolutional neural networks, which have been applied by Ronneberger et al. [Ron15] and Weimar et al. [Wei16] for example, was not feasible.

The detection of the measured part is achieved by detecting circles in the phase distribution \( \Delta\Phi \). Potential defects are then filtered out by applying a low-pass filter (LPF) in a circular motion. The resulting prototype is then subtracted from the measured \( \Delta\Phi \) to identify deviations via the application of a threshold. The whole defect detection pipeline is schematically shown in Fig. 5.22.

Laser chemical machining (LCM) is a promising alternative process that allows for inexpensive manufacturing of micro geometries in hard metals, such as dies for micro forming, without heat damage or structural alterations to the material [Mik17]. Laser chemically machined geometries can reach structure sizes between \( 10\,\upmu{\text{m}} \) and \( 400\,\upmu{\text{m}} \), with steep slopes and a surface roughness of up to \( 0.3\,\upmu{\text{m}} \) [Ste10]. However, factors in the process environment, such as chaotic thermal interactions between the fluid and workpiece geometry, complicate the manufacture of a desired geometry, necessitating a closed-loop quality control [Zha17] with an in situ measurement feedback to improve the manufacturing quality (see Sect. 4.​3). The challenging conditions of the LCM process, such as the requirement that the workpiece needs to be submerged by a fluid layer (typically 1‒40 mm thick), hinder the in situ application of many measurement methods. The general lack of accessibility to the workpiece, for instance, requires the use of contactless measurement methods based on optical acquisition.

Conventional micro-topography measurement techniques can be separated into interferometric methods (e.g. displacement interferometry, digital holography [Kop13]) and other techniques, such as conventional laser-scanning confocal microscopy [Han06]. Conventional confocal microscopy is hindered, however, by the in situ conditions of high surface angles of the specimen [Liu16]. Interferometric methods were also investigated as a means of control feedback [Zha13], but were found to be unsuitable: The tested measurement systems integrated the interferometer directly into the machining head of the laser jet system as a two-beam interferometer according to the Michelson principle in order to increase the signal strength. The measuring beam was guided coaxially to the etchant and processing beam onto the surface of the workpiece. To obtain the path difference from the interferogram, the number of interference fringes was determined with phototransistors. If the measuring and reference arms are in different ambient media, a correction with the refractive indices of the media is also required. Evaluating the interferometer with samples of different surface roughness, it was determined that the measuring signal strength decreases with increasing surface roughness [Ger10]. In the end however, successful in situ measurement application proved to be unfeasible due to the formation of thermal gradients and gas bubbles that act as moving micro lenses and cause strong disruptions of the measuring beam [Ger10]. Thus, a suitable in situ measurement method capable of dealing with the process-induced currents, thermal gradients, and refractive index fluctuations is needed to improve the feasibility and acceptance of laser chemical machining as a competitive manufacturing process. A method based on the confocal detection of the fluorescence emitted by the fluid shows promise for in situ measurement application. The measurement is based on detecting the boundary position of the specimen surface and the fluid through the change in fluorescence signal while the confocal detection volume is scanned vertically through the fluid [Mic14].

The principle of measurement as shown in Fig. 5.24 is based on the detection of the fluorescence intensity emitted from the fluid covering the specimen using a confocal microscopy setup [Mik18]. The light of a green diode laser (λ = 532 nm) is expanded by a Keplerian beam expander and redirected by a beam splitter to the objective lens (NA = 0.42, WD = 20 mm), exciting the fluorescent fluid (aqueous solution of Rhodamine B) submerging the specimen. The specimen container is positioned using a 3-axis linear stage to enable the scanning of the focus position through the fluid. Only the fluorescence light emitted by the fluid (λ = 565 nm) is collected by a charge-coupled device (CCD). The confocal principle causes light not originating from a volume around the focus of the objective (confocal volume) to contribute less to the detected fluorescence signal. Scanning the confocal volume of the excitation laser vertically (in the \( z \)-direction) through the fluorescent fluid produces a characteristic fluorescence intensity signal (see Fig. 5.24, bottom right).

Since the excitation light is filtered out, only light that is emitted by the fluid inside the confocal volume is detected. Only for values of z inside the boundaries of the fluid (z₀ <  z  < z₁, see Fig. 5.24) will a significant signal be produced, since no fluorescent fluid is present to generate light when the confocal volume is fully located in either air or the specimen. The signal does not decay abruptly at the boundary but gradually, depending on the vertical extent of the confocal volume. The exact determination of the surface position z₀ is not trivial, as opposed to the case of very thin fluid layers, where the depth response is more similar to that of conventional confocal microscopy where the intensity peak corresponds directly to the surface position. For the case of thicker fluid layers, the properties of the fluorescence signal depend strongly on the fluorophore concentration and the fluid depth. With high fluorophore concentrations or thick fluid layers, the Lambert–Beer law of absorption causes less excitation light to reach far into the fluid, resulting in the decay of the fluorescence signal before the confocal volume reaches the specimen surface. This effect is negligible for thin fluid layers, but needs to be taken into consideration when choosing the fluorophore concentration for measurements in thicker layers. For the purpose of determining the surface position of the specimen from the fluorescence signal, a physical model of the fluorescence signal is used.

The in situ measurement technique was validated by measuring the geometry parameter step height of a referenced step object [Mik18]. The fluorescence intensity signal resulting from the measurement of a single point on the step-specimen is shown in Fig. 5.25.

In order to obtain the geometry parameter step height, the fluorescence intensity signal (see Fig. 5.25 for the signal of a single point) of 23 points along a line perpendicular to the edge of the step-specimen was acquired (i.e. performing a \( z \)-scan for each \( \left( {x,y} \right) \)-point). The surface position \( z_{0} \) was determined from each measured intensity signal, with a least-squares approximation using Eq. 5.27. The resulting specimen surface positons \( z_{0} \) are shown in Fig. 5.26. After correcting the tilt of the step and the focus shift due to refraction at the fluid surface, the step height \( h \) was determined by the difference of the mean surface positions of each step surface

Comparing the step height result of \( h = \left( {258.2 \pm 8.8} \right)\,\upmu{\text{m}} \) with the tactile reference measurement of \( \left( {253.5 \pm 0.2} \right)\,\upmu{\text{m}} \) shows no significant systematic deviations. Since the positions on each step surface show a relatively large stochastic scattering (up to 20%), the uncertainties are most likely caused by the general surface condition or uncertainties in the fitting model. The measurement technique based on confocal fluorescence microscopy was thus shown to be capable of determining the geometry parameter step height for microstructures submerged in thick fluid layers >100 \( \upmu{\text{m}} \), which demonstrates the suitability of the model-based approach for in situ application. However, the sources of the uncertainty of \( 8.8\,\upmu{\text{m}} \) need to be further characterized in order to reduce it to the desired \( 1 \,\upmu{\text{m}} \). In order to find the lower boundary of uncertainty for the confocal microscopy-based geometry measurement of submerged micro-structures, a determination of the measurement uncertainty with the approximation of the signal model is necessary.

To obtain the fundamental uncertainty of the surface position \( \sigma_{{z_{0} }} = \sqrt {{\text{Var}}\left( {z_{0} } \right)} \) from the measurement with the non-linear least squares approximation method, the covariance matrix of the estimatorbased on the fit function \( I_{F} \left( z \right) \) (see Eq. 5.27) needs to be calculated. Applying an uncertainty propagation calculation to the least squares estimator gives the following relation for the estimator’s covariance matrix [Kay93]:where \( H_{{I_{F} }} \) denotes the Jacobian matrix with the partial derivatives of the approximation function with respect to \( {{\hat{\uptheta }}}_{{I_{F} }} \) at each position \( z_{i} \) of the measured fluorescence intensity signal \( \tilde{I}_{F} \left( {z_{i} } \right) \), and \( {\text{C}}_{{\tilde{I}_{F} }} \) the covariance matrix whose main diagonal contains the variance \( \sigma_{{I_{\text{F}} }}^{2} \) of each \( z_{i} \) of the fluorescence signal \( \tilde{I}_{F} \left( {z_{i} } \right) \), since the covariance between the individual values is assumed to be zero.

To calculate the surface position uncertainty \( \sigma_{{z_{0} }} \) of the measurement using the non-linear regression of the model function \( I_{F} \left( z \right) \) (see. Eq. 5.27), the average variance of the measured fluorescence signal around the model curve, i.e. \( \sigma_{{I_{F} }}^{2} = 1.26 \cdot 10^{9} \) (in units of detected photons), is used. The calculation results in an uncertainty of 8.56 μm for the surface position \( z_{0} \) and 3.75 μm if propagated into an uncertainty for the step height (based on 23 surface position measurements, according to the results from Fig. 5.26). The uncertainty of the experimental result of 8.8 μm is still larger than the calculated uncertainty by a factor of 2.3, suggesting varying conditions during the measurement, such as the form of the fluid surface or the micro topography of the specimen. To analyze the effects of the parameters fluid depth \( d_{f} = z_{1} - z_{0} \) and concentration-dependent attenuation coefficient \( \epsilon \), the uncertainty \( \sigma_{{z_{0} }} \) is determined as a function of the fluid depth \( d_{f} \) for different attenuation coefficients \( \epsilon \), shown in Fig. 5.27. The calculation reveals, that for each \( \epsilon \), a fluid depth exists where the uncertainty of the surface position \( \sigma_{{z_{0} }} \) is at a minimum (marked as a black cross).

The minimum decreases with higher attenuation coefficients \( \epsilon \) and lower fluid depths \( d_{f} \), which allows a suitable concentration (i.e. \( \epsilon \)) to be chosen for any particular application-dependent fluid depth to achieve optimal uncertainty. A Monte-Carlo (MC) simulation of the surface position determination (fitting an artificial signal with equivalent noise 10,000 times) was used to verify the results of the uncertainty calculations. To determine the theoretical limit of the achievable uncertainty, the variance \( \sigma_{{I_{F} }}^{2} \) of the fluorescence intensity is decreased to its theoretical minimum. For this, an ideal, shot-noise limited signal with a Poisson distributed variance is considered, resulting in a surface position uncertainty of \( \sigma_{{z_{0} }} = 0.07\,\upmu{\text{m}} \) for the 23 position measurements of the experiment (see Fig. 5.26). As a result, the shot-noise limited uncertainty is two orders of magnitude lower than the currently achieved measurement uncertainty. However, if the variance of the fit residuum is considered, the theoretically achievable uncertainty of the surface position for a fluid thickness of \( 2.6\,{\text{mm}} \) amounts to \( \sigma_{{z_{0} }} = 8.56\,\upmu{\text{m}} \), which corresponds to a step height uncertainty of \( \sigma_{h} = 3.75\,\upmu{\text{m}} \). These results demonstrate the untapped potential of the measurement technique and suggest that the step height uncertainty of the model-based measurement approach is only limited by the natural variation of the surface or effects not considered in the model (model uncertainties). Hence, a sub-micrometer precision seems feasible with the proposed confocal fluorescence microscopy technique, if the model uncertainties are decreased and the signal-to-noise ratio is maximized in future investigations.

Acquiring the mechanical properties of micro samples demands additional awareness concerning the effects of microstructure, surface, and alignment influences. In both tensile and fatigue tests of thin sheets, the edges of samples have to be very smooth and without micro notches to prevent them from early failure [Köh10b]. It is even more important to observe a correct alignment of the samples than for common samples, since eccentricity in the driving direction can lead to wrinkling and accelerate failure [Hon03]. Frames to place them into the testing device [Hon03] can additionally support especially sensitive samples. The ratio between the sample dimensions and the spatial extent of micro structural features causes size effects on, for example, the yield strength.

The strongest influence is caused by the inhibition of dislocation movement due to surface effects and a lack of dislocation sources. For sample sizes between 1 and 10 µm, this effect causes a strong increase of the yield strength. Furthermore, strain gradient plasticity effects influence the yield strength, due to additionally generated geometrically necessary dislocations (GND). Assuming a length scale of possible interactions with mobile dislocations between 10 nm and 100 µm, the plastic deformation in micro samples can be described satisfactorily [Vol09]. The surface grain model [Kal96] takes into account the difference between the influence of the surface grains and the influence of core grains on the yield strength. The plastic deformation of surface grains occurs more easily than for core grains due to a smaller contribution to strain hardening. The effect is measureable in sample sizes less than 20 times the average grain size [Kal96]. Among the different approaches to calculation, Köhler et al. [Köh13] summarized the yield strength dependencies from structural features. They show that for micro metal forming applications, the temperature and strain rate dependence on the yield strength can be neglected. For samples with less than 20 grains in the cross-section, an increasing yield strength effect in comparison to single crystals occurs due to the different orientation of grains towards the applied stress and the lack of strain continuity across grain boundaries. The increase can be calculated by a crystal orientation factor m_or [Tay56]. For samples with more than 20 grains in the cross-section, the Hall–Petch relation works well to calculate the grain size influence. An additional term taking the grain boundary resistance (GBR) into account effects a further increase of yield strength. Kim et al. [Kim07], using an extended Hall–Petch relation, provided a combination of both.

Since the local microstructure gains a higher influence on the mechanical properties, the natural scatter in the local microstructure results in larger scatter in the mechanical properties (see Sect. 1.​4.​2.​2 Scatter).

The first results from tensile tests on thin DC01 sheets with a chemical composition Fe-0.027C-0.047Al-0.005Si-0.190Mn-0.007P-0.009S (mass%) and a thickness of about 50 μm demanded an appropriate model to explain the unexpected dependence on the grain size [Köh10a]. The first received results are displayed in Fig. 5.29. The high yield strength in the cold worked state can easily be explained by the high dislocation density due to cold working in the forming process. The dislocation density was reduced by heat treatment for 15 min at 850 °C in a salt bath from 10¹⁰ to about 2 × 10⁸ m⁻². An elongation of the holding time at the heat treatment temperature caused no further reduction of the dislocation density.

To explain the deviance towards the Hall–Petch relation due to the small number of grains in the cross-section of the tensile test samples, a model inspired by Janssen [Jan07] was applied successfully [Köh11a]. A simple Taylor approach assigning different yield strengths to the core and surface provides a satisfactory description of the yield strength of the sheets:with α representing the volume fraction of the surface grains. The yield strengths \( \sigma_{{{\text{p}}0.2}}^{\text{surface}} \) and \( \sigma_{{{\text{p}}0.2}}^{\text{core}} \) in the Hall–Petch relation are extended by the hardness-dependent contribution \( c \cdot \left( {HV - 150} \right) \) (see Eqs. 5.32 and 5.33), wherein c is a material-dependent parameter. A hardness of 150 HV is assumed for the soft annealed state of the mild steel. The differing grain sizes d and grain boundaries resistances of surface and core grains, k^surface and k^core, as well as the friction stress σ₀ are explicitly taken into account:

Using the measured values and values from the literature [Hut63], the relations could be completed for DC01sheets to:

With this newly developed relation, the dependence of the 0.2% yield strength of micro sheets on the grain size could be predicted with a satisfactory accuracy [Köh11a].

The characterization of the first thin films generated by physical vapor deposition required the simultaneous consideration of the test result and the microstructure [Sto10]. Typical deposition effects, like hillocks, explained the unusually high differences in the results of nominally equal batches. The enhancement of the process quality resulted in more homogeneous tensile test results, suitable to adjust the process parameters of the sputter process [Kov17].

A customized clamping technique for micro wires was successfully applied on the incrementally forged samples. As expected, the specimen failure occurred predominantly within the gauge length. In the tensile test results, the scatter was tolerable, though the results were sometimes unexpected.

Rotary swaging of micro wires produced from 304 stainless steel causes the transformation-induced formation of martensite [Kuh15]. The fraction of martensite increases with decreasing feed rate in the forming process. Though the hardness increase is in accordance with the martensite fraction, the tensile test results show an almost diametrical tendency [Köh17]. The ultimate strength and the yield strength increase slightly with the increasing feed rate (Fig. 5.30). The increase can be explained by the strength hardening of the samples due to the forming process. It should be mentioned that the scatter of the strength values of these samples is comparatively small.

In contrast to the tensile test results, the results of the fatigue test on rotary swaged micro wires show a considerable influence of the feed rate [Köh17]. With 1 and 3 mm/s feed rates, the results show an increase of the endurance limit σ_e with an enormous scatter, which is represented by the scattering parameter T = σ_e,10%/ σ_e,90%. With 5 mm/s, the scatter and the endurance limit decrease significantly in accordance with the decrease in hardness (Fig. 5.31). The scatter of the results is significantly higher than in the initial material. A comparison of these results with results gained on macro swaged samples showed that, though the scatter of the results is much lower, the mean value of the macro swaged samples lies well below the results for the micro swaged samples [Gan96]. This result can be explained by the statistical influence of the appearance of failure on the fatigue values. The less commonly a critical failure appears in the material, the more the scatter and the mean value for the endurance limit σ_e increase, and the more samples have to be tested to receive a loadable result.

An additional interesting point is discovered in the origin of the failure of the fracture (Table 5.3). Stereo microscopy and additional scanning electron microscopic (SEM) investigations of the fracture surface showed that the fracture mainly does not start from a non-metallic inclusion or from the surface, as normally expected, but from under the surface.

A comparison of the results of the hardness measurements, metallographic investigations and the failure origins position in the fractured samples showed that the fracture starts predominantly in areas with an increased martensite fraction and the highest hardness values. In Fig. 5.32 the micrographs of the wires are superimposed with the position of the highest probability of failure (colored rings = mean value of failure probability; surrounding dotted rings = mean value +/− standard deviation) and the hardness measured across the cross-section. Residual stress measurements revealed additionally an increase of residual stresses in the martensitic phase below the surface. The position of the maximum residual stresses correlates with the fracture initiation position of the dynamically tested samples (Fig. 5.33).

Köhler et al. [Köh18] published the results of the comparison of preparation techniques for EBSD measurements. The most interesting result was that the indexability of grains depends not only on the quality of preparation but also on the degree of deformation of the grains due to the forming process. Any distortion to the crystal lattice within the diffracting volume produces a lower quality due to more diffuse diffraction patterns. This enables the parameter to provide a qualitative description of the strain distribution in the microstructure, if the underlying structures can still be dissolved in principle [War94]. However, severe deformation can also cause ultra-fine grains, which can also be the reason for insufficient resolution of the diffraction patterns. An example of the effect of severe deformation on the image quality is shown in Figs. 5.34 and 5.35. Figure 5.34 shows the image quality and the EBSD image of the microstructure of a 304 stainless steel wire in the initial state before forming. The image quality is very good and there are only a few dots along the grain boundary that cannot be indexed correctly. Figure 5.35 shows the same steel wire after rotary swaging with a degree of deformation φ = −1.46. The grains have been deformed differently according to their alignment to the main shear stresses. Between the deformed grains, large black areas appear, which are not indexable and which indicate a severe deformation or the formation of nano-grained structures.