Net-shape parts of typically intricate and complex shapes obtained by powder metallurgy and additive manufacturing (AM) are employed in several key mass industry sectors, especially automotive, aerospace and medical. Their use is growing rapidly in preference to conventional casting because most PM processes produce parts in the desired precise final shape with little or no further machining requirement. Moreover the use of fine-grained (nano/micro scale), pre-alloyed homogeneous powders allows the manufacturing of parts with precisely set material properties and microstructures for increased strength. In conventional PM, powders are blended and compacted by cold pressing in a die and sintered in a furnace whereby the bonding between the particulates is further consolidated [1
]. A significant drawback, however, is that following powder compaction, unwanted features like porosity (Figure 1
) and cracks (Figure 2
) in the microstructure, may remain [3
] which can survive into the sintering phase, developing into critical flaws and defects. Similar defects can be observed in other additive manufacturing methods such as laser cladding and laser sintering, where the raw material is in the form of particulates, in addition to the ever present risk of delamination (Figure 3
) that can occur between the added layers [5
]. If undiscovered, these flaws may result in unwanted mechanical properties of parts that, can introduce a level of unreliability to the product. End-of-line inspection, not often performed, leads to the scrapping of 6–8% of components, yet fails to detect micro-sized defects, which can grow in service to produce major in-service failures and recalls.
In terms of conventional press and sinter PM, the production can be improved significantly by introducing such an inspection system to the process. It is understood that the majority of defects originate in the pressing phase [4
]. Optimisation of the pressing tool and the pressing process is, therefore, crucial for the final product quality. In most cases, the optimisation is based on a series of trials where the commonly used method to inspect the green parts is through destructive testing following sintering. These organisations use metallography as the main destructive testing method. As metallography, due to its nature, cannot provide accurate and reliable results in the full volume of the part [8
], the tool optimisation requires numerous repetitions. The current tool and process optimisation, therefore, results in high tooling costs and lengthy setting up periods, limiting the growth of this manufacturing technique [9
An Non-Destructive Testing prototype system has recently been developed which utilises the advantages of computed tomography to enable online (real time) quality assurance of powder metallurgy products [10
]. The system is capable of detecting bulk porosity, loss of material, delamination, and also captures microscopic cracks (surface breaking or volumetric) at any stage of the manufacturing process, therefore, potentially reducing production costs of material, energy, and time for the manufacturers by identifying faulty parts.
The system uses Computed Tomography technique to capture the data from the parts’ internal structure including any defect like features. From few hundred to couple of thousands of X-ray projection images are taken per part. These images are forwarded to a CT reconstruction tool that in our case uses the Filtered Back Projection technique and is developed by the Fraunhofer-Institut für Integrierte Schaltungen. The 3D reconstructed image is than fed into the automated defect recognition module. As the final step in the process, the ADR system enhances the quality of the 3D CT image in order to highlight the areas of potential defects. Following image processing these highlighted areas are separated from the background and being classified in comparison with pre-set data from the image library. Based on preliminarily stored data the ADR system is capable to make decision whether the part under inspection is “pass”, “fail” or “pass/fail with conditions”.
Although the whole process, from the data acquisition to the final decision making is integrated and can run without the need for any interactions from the operators, our current work is mainly focused on the defect recognition.
1.1. Computed Tomography in Comparison with Destructive Testing Methods
There are few efficient and reliable testing methods available for inspecting PM parts at early production stages (for example at the green state of pressing) due to the relatively weak adhesion between the particles. Until recently, destructive methods were used as the ultimate quality assurance of near net-shape production. Traditionally, sample parts are collected at the end of the manufacturing process and, following a pre-inspection preparation, taken into the laboratory for inspection. The preparation involves traditional metallography processes of sample cutting and polishing [12
] that requires a dedicated workshop facility. The sampling is only a small percentage, (1–2%), of the full production run and these samples are of course destroyed during the inspection [8
]. Additionally, the information is limited to the particular cross-section of material under inspection, which is a limitation as there may be defects elsewhere in the original part. Consequently, such destructive testing methods are not capable of providing accurate and reliable results for full coverage of parts, i.e., volumetric information from parts in-line with manufacturing.
Compared to destructive testing, computed tomography offers a valid and reliable alternative for in line quality control that is much quicker and simpler to implement. In CT the part is placed between the X-ray source and the detector and rotated through 360 degrees in two to four thousand inspection steps. The photons of the X-ray beam pass through the specimen and are captured by the detector. The number of captured photons, and therefore image quality, largely depends on the density of the material, the thickness of the parts under inspection and the energy of the X-rays. The captured data in the form of a 2D projection image are then forwarded to an image processing and Automated Defect Recognition unit where the contrast of the different features is algorithmically enhanced and the flawed image compared against a database of flawless images. The processing time by this method can be as little as a few seconds to a few minutes, providing the capability for mass production scanning. Additionally with X-rays it is possible to extend the inspection volume to 100% of the part allowing recognition of the full range of defect types.
1.2. Brief Theory of Computed Tomography; from Medical to Industrial
X-ray CT refers to the use of X-rays to digitally dissect a specimen and reveal its interior details [13
]. A 2D CT image or a CT slice typically corresponds to a certain small thickness slice of the object being scanned, while a 3D CT image is composed of stacked slices of an entire volume. Therefore, a 2D CT image comprises pixels
(picture elements) while a 3D CT image comprises voxels
(volume elements). A 2D CT image is obtained by projecting X-rays around the slice plane from a number of orientations and measuring their attenuation. The 2D distribution of X-ray attenuation in the slice plane is then reconstructed using a specialized algorithm. The grey levels in a 2D CT image correspond to the attenuation of X-rays attributed to each pixel
in the slice. The 3D distribution describing an entire volume can be created by acquiring a stacked series of 2D slices.
Since its widespread adoption for medical imaging, X-ray CT has been adapted to various industrial applications too [14
]; for example measuring dimensional and shape quality sometimes in the same time with checking material quality of parts. These parts are often made through machining, casting, thermo-forming, or other methods where measurement of internal dimensions is not possible [15
]. The industrial tasks involve imaging denser materials (metals) for a wide range of sizes and resolutions In medical CT systems, low dose X-ray sources with relatively low-energy (<140 keV), large (mm-scale) beam widths, and high-efficiency detectors are used for safety reasons [6
]. In industrial CT systems highly penetrating X-ray energies are required (min. 250 keV) for scanning high density materials (e.g., ferrous alloys), such as used in PM for manufacturing automotive gear parts [16
]. Enclosures are designed and built for ensuring the safe operation of the X-ray system [17
], tailored to the scale of the manufacturing operation and related component characteristics (size, material, and geometrical complexity). The application of purpose built enclosures allows the following characteristics for industrial CT imaging [13
]: (1) high penetration capability for dense materials with higher-energy X-rays; (2) enhanced resolution through the use of narrower X-ray beams and more densely packed X-ray detectors; (3) increased signal-to-noise ratio (SNR) through longer exposure times, without compromising health and safety in a manufacturing environment.
A major innovation in this research project is the development of algorithms for automated defect detection and recognition to identify the defect features of each composite component and categorise the defects according to their types. Using these algorithms, there is a little need for an inspector to interact with the system that will automatically characterise and classify the defects in the composite components to give information required by the end-user. A prerequisite of this automated software is the establishment of a reference database for defect recognition by similarity analysis.
The major difference between the presented ADR system and other CT based state-of-the-art industrial defect recognition techniques, is the use of similarity analysis. Most of the PM manufacturing techniques such as Press-Sinter, Laser Sintering, or Metal Injection Moulding are considered as net or near-net shape production as the finished product does not require further processing. The discussed ADR system is designed to compare the 3D CT images of PM parts with previously created and analysed reference images, so called Golden images. This comparison is only possible because these types of high precision manufacturing techniques are capable of producing such high precision output where the shape and dimensional difference between each part is negligible. Other industrial applications are based on signal analysis like wavelet or principal curvature analysis [18
]. It is mainly because the difference between each product is greater than the comparison of parts to a reference is not practical.
This paper presents the outline design of such an automated inspection system, including the establishment of the defect image library and the image processing steps required to identify and characterise defects in a PM part. Examples are given for the processing of real CT images of a PM valve ring and a set of gear parts with visible shape deformity differences.
4. Experimental Results
In this section, real CT images are processed with the procedure shown above. The configuration parameters of the CT scanner used can be found in Table 1
. A 3D CT image was acquired for a ferrous PM ring sample. A photograph of the ring is shown in Figure 5
a with its 3D CT image shown in Figure 5
b. To illustrate the effectiveness of the image processing procedure, two 2D slices were taken from the 3D CT image with a distinct defect identified by a red circle in each, as shown in Figure 6
. Note that the ‘golden image’ restriction relaxes here since the defects in the two 2D slices are at different locations and they can be the substitutional ‘golden image’ for each other. Details regarding this change in operation are provided below. Both 2D images are de-noised first using a Wiener filter and the resultant images are shown in Figure 7
, which shows that the filtered images are smoother than the images in Figure 6
, i.e., with reduced noise.
After image filtering, one crucial step is the image registration based on the intensity distribution and the subsequent image subtraction to highlight the defect [30
]. For comparison, the two overlapped images before and after image registration are shown in Figure 8
a,b, respectively. The red and green outlines in Figure 8
a represent the contours of the two different parts from Figure 7
when they are placed on top of each other. This difference is caused by the misalignment of the two images and is being removed through the image registration. Note that the red colour represents the part from Figure 7
, left, while the green represents the part from Figure 7
, right. In Figure 8
b, it can be seen that the two images are almost totally overlapped or matched. This means that a good accuracy has been achieved for image registration. On this basis, image subtraction can be carried out. As mentioned previously, one of the two images can be used as the ‘golden’ image for the other to calculate the respective change in grey level brought by the defect in the latter. To remove the influence of the defect in each ‘golden’ image, a truncation is made, i.e., when the value of a grey level after image subtraction is negative, it is set to zero. The two difference images after image subtraction are shown in Figure 9
In Figure 9
, defects are highlighted in each image, but with multiple artefacts present, i.e., a new background. The entropic thresholding described above is used to segment the defect in each image from the noise background. The binary images after segmentation are shown in Figure 10
. Some small residual spots are still visible in each image of Figure 10
. Note that the purple circle represents the part edge and green-coloured objects represent defects. To remove these artefacts, the opening morphological operation is implemented with a disk structure of size 80 pixels. The smoothed images are presented in Figure 11
with the defect identified in each image, ready to be characterised to form the basis for later defect recognition.
5. Automated Defect Recognition across the Whole 3D Reconstructed CT Image
A major achievement in developing the Automated Defect Recognition algorithm is that it is capable of processing the complete 3D CT reconstructed image all at once. This improvement results in a significant time saving in feature recognition compared to the previously detailed method of one-by-one processing of arbitrary 2D slices of a reconstructed 3D CT image.
shows the image of the purpose-made 3D printed gear part with and without a known external defect (crack on gear tooth) that was introduced for demonstration purposes.
Following X-ray CT imaging Figure 13
shows the reconstructed 3D images of the parts without (a) and with (b) defect. The defect area is circled in red. Figure 14
represents a horizontal slice of the reconstructed CT image; the part with the defect highlighted is on the right.
Through the image registration stage the CT images are perfectly aligned with each other allowing the extraction of the differences. Before registration the selected 2D slices are matched to each other; Figure 15
a indicates the differences between the two slices, caused mainly by the misalignment of the axis. The purple area represents the slice image of the part without the defect, green represents the slice image of the part with the defect, and the area in white is the area that is covered by both images. In Figure 15
b following registration the two images are perfectly aligned allowing the defect related difference to be highlighted.
Once the image registration is complete using the 2D slice images, feature recognition can be implemented at the full 3D level. Figure 16
a,b are isometric views of the registered 3D objects; the difference/defect area is highlighted in red. In Figure 16
c, the difference is extracted from the background allowing further operations such as volume measurement.
This paper briefly highlights the advantages and drawbacks of near net-shape production techniques. It also describes the system design for an automated X-ray CT scanning system for net-shape parts, the tree structure of defect image library and the image processing procedure, taken for automatic defect identification. Real CT images of PM and Additive Manufacturing parts with and without defects are processed using the proposed technique. The results show that defects in the images can be automatically segmented from the background, which lays the basis for effective feature extraction and defect recognition.
The major difference between the introduced ADR system and other industrial CT based NDT systems, such as the cited state of the art CT inspection techniques for composite materials and also electronics components, is that whilst those systems rely on signal analysis, the technique proposed in this paper is using similarity analysis.
Efforts are still needed to further investigate techniques for automatic image registration and segmentation to improve their robustness because of the diverse intensity distributions of the CT images encountered in practice. Although making the leap from 2D image slice processing to 3D CT reconstructed image processing as a whole has resulted in significant potential time savings on image processing (and therefore the inspection process), further optimisation is required to make it more attractive and applicable to more Powder Metallurgy and net-shape manufacturing methods such as 3D printing and hot isostatic pressing (HIP).