Capability to detect and localize typical defects of laser powder bed fusion (L-PBF) process: an experimental investigation with different non-destructive techniques

The two analyzed samples are part of a collection of different samples made of AISI 316L, produced by means of the L-PBF technique, changing the process parameters and the defects shape in different regions of the specimens. Figure 1a shows the surface of the investigated specimens that is as the surface appears after the AM process, without post-treatments. The laser power of 275 W and the scanning speed of 700 mm/s were the main process parameters used to print these specimens. The cuboid geometry was built up with an alternating meander stripe scanning pattern, where the orientation was rotated by 90° from layer to layer, considering also more details that can be found in [14]. For the sample indicated as PK007 (Fig. 1a), buried regions were produced increasing the energy input by reducing the scanning speed down to 300 mm/s (275 W; 300 mm/s). Instead, for the sample PK010 (Fig. 1b), lack of fusion diffused porosity was induced within the same regions, same intended shape and positions reducing the energy density by decreasing the laser power during the process (150 W; 700 mm/s). In this way, in both cases, a deviation from the optimum process parameters has been obtained in four predefined regions of the specimen, and for specific layers of the material. No further heat treatment was performed to preserve the induced defects or to improve the quality of the final surface. The specimens were cut in different parts (4 parts for PK007 and 2 parts for PK010) to perform the µCT investigation (Fig. 1).

The intended shapes and 3D dimensions of the induced defects are depicted in Fig. 2 and reported in Table 1, together with the related process parameters.

The specimens were investigated by micro-computed tomography (µCT) to assess the shape and size of the internal structures non-destructively. A custom-made µCT scanner equipped with a Nikon X-ray tube XT 225 with rotating target (Nikon Metrology NV, Leuven, Belgium) and a 4 k flat panel detector (Perkin Ellmer, Santa Clara, USA) was used to scan the specimens. The energy settings were a voltage of V = 201 kV and a current of A = 550 µA. A 0.75 mm thick silver plate was used to harden the beam. To improve the signal-to-noise ratio, the detector was binned to 2 k, and the acquisition time of 2 s was averaged 6 times for all of the 1500 projections. The achieved voxel size was (30.8 µm)³. This enables the quantitative analysis of voids larger than (60 µm)³. For data analysis, the commercial software VG Studio MAX 3.3 (Volume Graphics GmbH, Heidelberg, Germany) was used. A threshold of 8 voxels was set as a lower threshold for the void analysis (Fig. 3).

The used thermographic set-up is shown in Fig. 4. The tests were carried out in reflection mode, with the heating source and the IR camera placed in front of the sample, on the same side (1—Fig. 4). A diode laser system LDM (500-20 by Laserline GmbH), with a wavelength of 942 nm, was used to stimulate the samples at one surface, with a pattern surface of 39 × 39 mm² (2—Fig. 4). The focal distance between sample and laser optics is 60 cm, that is the optimum for this instrumentation to reach a homogeneous heating (sample size 35 × 35 mm² < laser spot size).

Different experimental tests for both samples were performed changing the laser pulse duration and the laser power with the aim to find, experimentally, the optimum test conditions. In fact, the energy density provided to stimulate the sample depends on the laser power and pulse duration (rectangular pulse), once its shape has been defined. Moreover, the effective adsorbed energy is different from the previous one and it depends on the sample surface conditions. Various considerations in this regard are reported in D’Accardi et al. [22], with some relevant indications reported also here as summary in Table 2, with the indication of the absorptivity and the achieved energy density. The final set-up specifications, from an energy point of view, are reported also in the same Table 2 and were the same in both cases for both samples.

The Infratec ImageIR 8300 IR camera (3—Fig. 4) was used to acquire the thermographic tests, with a cooled detector sensitive to the middle infrared wave range (MWIR 3–5 μm), NETD < 25 mK (30 °C) in full frame 640 × 512 pixels, with acquisition parameters reported in Table 2.

The conventional eddy current instrument ELOTEST B1 V4 [Rohmann GmbH, Frankenthal (Germany)] has been used for the ET investigation. The AC excitation current (max. 100%) was 200 mA effectively. To determine the most reliable results, a set of seven probes with five different frequencies was tested. The best testing results were obtained with the probe AN 16, a BAM development based on two coils wound around two cylindric ferrite cores (diameter 1.6 mm) in an absolute arrangement. This probe comprises a high spatial resolution together with a high penetration depth. The latter is influenced by the electrical conductivity of the specimens which is about 1.3 MS/m for the additively manufactured parts. The standard depths of penetration δ are 1.1 mm for a frequency of 200 kHz and 0.7 mm for a frequency of 500 kHz, which means that the strength of the exciting magnetic field is decreased down to 37% of the exciting magnetic field value at the surface at this depth. Hidden defects can be found in regions deeper than the standard penetration depths [33]; however, the size and geometry of defects and the applied sensing system limit the detectibility of deep lying defects. The defects’ depth and size of the investigated specimens (see Table 1) are within the possible testing region. However, probe AN 16 has a directional dependence due to its configuration of coils and cores inside the probe. To overcome this behavior, an absolute probe, A 05, having a core diameter of 0.5 mm has been used. This probe has a unidirectional behavior; however, the sensitivity is reduced leading to a decreased SNR.

The specimens were fixed between four plates of steel under an x–y planar manipulator (see Fig. 5). To protect the probes and the surface of the specimens, a plastic sheet (0.1 mm thick) was placed on top of the specimens and plates. The probes were moved in contact with the plastic sheet.

The spatial resolution for both directions was 0.087 mm and then, a test area of 22 × 22 mm² took about 10 min and an area of 42 × 42 mm took about 24 min.

The PK007 specimen was cut into four pieces to reduce the path length for the µCT acquisition, and then, increase the signal-to-noise ratio. Figure 6a, b show the results of the 3D reconstructed data for all quarters of PK007. In contrast, Fig. 6c presents a cross-sectional view of the lower right quarter piece along the line in Fig. 6b, while the blue lines indicate the nominal geometry of defects, that are the region with varied deposition parameters.

The colored volume scale corresponds to the pore volume of separated voids, i.e., each color represents a typical pore size, here mainly in the range between 0.2 and 0.8 mm. As depicted in Fig. 6, the porosity in the overexposed sections is not evenly distributed and in some regions appears as large connected voids. Except for the small square, some clustering of voids was observed at the center of the induced defect areas. As it can be seen in Fig. 6b, the projection of all voids into the z–y plane seems to form the intended nominal shape of the induced defect areas. The view on the cross-sectional in Fig. 6c reveals the complex shape of pores in the z–x plane and a not constant in-depth position with respect to the imposed defects depth. Please note that the original sample thickness of 3 mm was slightly reduced by removing the final sample from the base plate.

Using the CT data (3D rendering), the following values of porosity were obtained for each region (large square, medium square, small square). It is clear that this value does not include those pores outside the original addressed imposed defects regions (Table 3).

These values are really high in comparison to other reports [34], but not unusual [35]. It can be concluded, that the manufactured specimen includes clearly defective areas as intended.

Figure 7 provides a more detailed view on the morphology of the defect II, which will be used later as a reference defect for comparing the different NDT methods (see Sect. 4).

Further analysis of the PK007 microstructure was reported in D’Accardi et al. [22].

PK010 was only cut into two pieces. The scheme of the presentation the CT results is the same as in Fig. 6. As depicted in Fig. 8, the shape and spatial distribution of the segmented voids differ significantly from PK007 although the geometric dimensions of the low-exposed respective regions were the same. In the PK010 sample, mainly the left and bottom border of the imposed regions seem to form larger connected voids, while the remaining part of the regions is characterized by diffuse distributed and not connected pores. This effect at the margins is due a poor positioning accuracy within the preprocessing software for the manufacturing process which is obviously not well suited to handle these specific processes with varying parameters. Therefore, the cross-sectional view also shows a short line-shaped cluster region located at the lower section edge (partially covered by the blue line) and few wide-spread single pores.

The estimated porosities for all regions are listed in Table 4.

As demonstrated previously, the work [6] showed the strong influence of laser power on the obtained porosity for AISI 316 steel and reported really high porosity values at lower energies due to lack of fusion. However, a direct comparison is not possible because in Ref. [8] a pulsed laser was applied and the authors provided spatial porosity instead of volume related values. In this work, in the case of the PK010, the obtained porosity is mainly concentrated at the edge regions, thus the real volume porosity (without margin) is much lower. However, the obtained specific edge geometry provides another type of defects as original intended which is perhaps also significant for quality assessments.

As in the case of the PK007 sample, only one quarter was selected for the comparison of the NDT methods: the region indicated as defect IV. Figure 9 contains a more detailed view of the segmented data within this specimen part.

Both components X and Y of the complex eddy current signal were recorded. The lift-off signal was adjusted by means of a phase rotation that it occurs in the X-component (standard procedure in eddy current testing). For the representation, we only used the amplitudes of the Y-channel which mostly contain the hidden defect signals. In Fig. 16, the results of all defects I–IV of sample PK007 for two different testing frequencies (left: f = 200 kHz and right: f = 500 kHz) using probe AN 16 are shown. Due to reduced skin depth of eddy currents for higher test frequencies, the detected signal amplitudes of the defects decrease in the case of 500 kHz compared to 200 kHz.

In the case of the PK010 sample, which was measured before cutting into pieces, the results are shown in Fig. 17 again for frequencies 200 kHz and 500 kHz.

Due to the directional dependence of probe AN 16, further scans were performed using probe A 05 for PK010. In Fig. 18, the scan for test frequency 500 kHz is presented. Here, the l-shape of defect IV and I which correlates with CT results is visible due to the characteristics of probe A 05.

The results related to the two techniques TT and ET are reported in Fig. 19, considering the specified defects and the most significant results. The TT result corresponds to Fig. 12b and the ET result to Fig. 16b, with a zoom in correspondence of the defect II.

For completeness, the ROED is also shown in both images.

Although CNR values of the defect from TT and ET results are comparable, the ET results appear more smooth in general. This can be explained by the effective width of the used coil (in the range of the coil diameter 1.6 mm). In case of a grainy surface with typical grain sizes between 0.1 and 1 mm, this sensor acts like a low-pass filter and blurs fine details. Despite the resulting spatial resolution was high enough to indicate the four separate clusters detected by CT, the two in the middle has a quite compact shapes, while the other two are more fissured e.g., their surface to volume ratio is higher (see Fig. 7).

In contrast, the TT could only clearly identify the two middle clusters, while the left one is barely indicated and the right one not detectable against the background. The authors can conclude that TT seems less capable for detecting these fissured structures, in comparison to ET. However, considering the phase data obtained with a higher frequency, at least the left cluster could also be detected by TT, as shown in Fig. 19a. In other words, it is necessary to investigate all the frequencies for reconstructing and characterizing volumetric defects with the PPT data [23].

Regarding the sample PK010, the discussion has been focused on the defect IV in the upper left quarter, selecting for TT the frequency of 0.37 Hz (a zoom of the Fig. 13b), and for ET a part of the result already shown in Fig. 18.

For the PK010 sample, the laser power was reduced which led to regions with a lack of fusion as artificial defects. Both methods give similar defect geometries. The horizontal lower edge is blurred in comparison to the CT results, and both methods reveal a substructure of this edge not recognizable in the CT result (see Fig. 9a). Furthermore, ET and TT results present a narrowing of or even a gap within the left vertical edge not provided by CT.

In addition to the cluster-related signals, the ET data present a characteristic signal pattern which is fluctuating across the surface of the test samples. This pattern is more visible for the PK010 than for PK007 where ET signals are smoothed. This behavior can be explained by the use of the testing coils. For the PK007 sample, the used coil diameter of probe AN16 was 16 mm and 5 mm in case of probe A05 (PK010). The reduced diameter for A05 leads to an enhanced spatial resolution and, therefore, a more blotchy signal pattern due to material’s inhomogeneity. The origin of this inhomogeneity is not fully understood and basis of further investigation. At this moment, it is not clear whether the granular surface structure or currently unknown fluctuations in the material parameters are the cause for the ET background pattern.

In addition, the right choice of the applied ET set-up and which coil system should be used plays an important role. In the case of spherical defects, the probe with a directional dependency is beneficial due to its better detectability leading to enhanced CNR values. However, if the shape of defects is non-spherical, an absolute probe with a non-directional behavior should be used.

Table 5 shows the maximum CNR values for both the selected defects, referred to the results reported in Figs. 19 and 20 and considering an area of 3 × 3 pixels around the pixel with the highest CNR value. The CNR values confirm similar results for the two techniques, with slightly higher values for the TT in the case of keyhole defects.

To obtain the in-plane dimension of the defected area, a binarization analysis of TT and ET data, adopting a decision threshold values criterion [40] has been performed. In this way, the detectable and undetectable defects were expressed as 1 and 0 (hit/miss data). In particular, the generic pixel (x, y) with normalized contrast CNR has been identified as a defect if the inequality (Eq. 2) occurred:where Th is the threshold value, usually an integer between 1 and 3.

Given the differences between the two adopted techniques, the threshold values can obviously be different. As already said, for the ET data, two different probes were used for detecting defects in the samples PK007 and PK010. In view of this, and as it can be seen in Figs. 19 and 20, the ET data of the two samples are characterized by a different signal-to-noise ratio that involves a different CNR value. In this regard, two different values of Th have been chosen for binarizing the ET data of samples PK007 and PK010 (3 and 2 respectively). For TT data, the same threshold value of 2 has been used for obtaining the binary maps related to the two samples.

In Figs. 21 and 22, the binarized maps are reported for PK007 and PK010 and for both techniques. To show the effect of the Th value on the binary data, also the result for Th = 3 is reported (Fig. 21b).

The ET results seem to reveal better the contour of the four clusters related to the cross defect with respect to the TT data in which the defected areas appear more shaded (Fig. 21).

Different results for ET comparing PK007 and PK010 are due to the use of two different probes, as already discussed in the previoues section. In comparison with TT results (Th = 2), ET result presents fewer false positives, with a slightly more precise contour of the defect. This is partly due to the high frequency chosen for the TT phase data characterized by a lower signal-to-noise ratio than low-frequency data. However, as already shown in the previous section, the higher frequencies phase data allow to detect and reconstruct the L-shape of the defect as detected by the µCTs (Fig. 13a).

In the case of the sample PK007, ET seems to provide better results than TT in terms of area actually defective, if compared to the μCT results. In particular, a slight overestimation of the defect area is obtained for both techniques, allowing to indicate the right and left clusters with the choice of the lower threshold value for TT. Referring to the TT technique, the threshold value equals to 3 (the same adopted for the ET), leads to underestimate the area of the defect, which is not a good practice for an NDT inspection.

In Table 6, a quantitative comparison between the two techniques is reported. In particular, both the in-plane defect area and the percentual of defected areas with respect to the nominal size of the ROED are depicted.

An estimation of the detection limits is possible for both techniques knowing the actual defect dimensions from the μCT data. In case of keyhole mode defects, the detection limit can be directly assessed considering the minimum detectable cluster size. A different approach has been used for the lack of fusion defects. In this case, we focused on the cluster regions located at the edges of the imposed defects and then, the detection limit was regarded in relation to the micropore concentration provided by Fig. 8. In this way, the concentration of 0.8 micropores/mm³ seems to be sufficient to detect defect III with a width of 0.3 mm, but not to detect defect II with lower values around 0.5 micropores/mm³. Table 7 provides an overview of the estimated detections for both defect types.

It is important to underline as these limits strictly refer to the adopted set-up, instrumentation, test parameters and data processing. However, the numbers can provide the first indication about typical detection limits of the two techniques.