X-ray microcomputed tomography (microCT) has become an established method of testing and analyzing additively manufactured parts in recent years, being especially useful and accurate for d...

X-Ray microcomputed tomography in additive manufacturing : a review of the current technology and applications

Active infrared thermography is a fast and accurate non-destructive evaluation technique that is of particular relevance to the aerospace industry for the inspection of aircraft and helicopters’ primary and secondary structures, aero-engine parts, spacecraft components and its subsystems. This review provides an exhaustive summary of most recent active thermographic methods used for aerospace applications according to their physical principle and thermal excitation sources. Besides traditional optically stimulated thermography, which uses external optical radiation such as flashes, heaters and laser systems, novel hybrid thermographic techniques are also investigated. These include ultrasonic stimulated thermography, which uses ultrasonic waves and the local damage resonance effect to enhance the reliability and sensitivity to micro-cracks, eddy current stimulated thermography, which uses cost-effective eddy current excitation to generate induction heating, and microwave thermography, which uses electromagnetic radiation at the microwave frequency bands to provide rapid detection of cracks and delamination. All these techniques are here analysed and numerous examples are provided for different damage scenarios and aerospace components in order to identify the strength and limitations of each thermographic technique. Moreover, alternative strategies to current external thermal excitation sources, here named as material-based thermography methods, are examined in this paper. These novel thermographic techniques rely on thermoresistive internal heating and offer a fast, low power, accurate and reliable assessment of damage in aerospace composites.

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Recent Advances in Active Infrared Thermography for Non-Destructive Testing of Aerospace Components.

Optically and non-optically excited thermography for composites: A review

Directed energy deposition (DED) additive manufacturing: Physical characteristics, defects, challenges and applications

Porosity prediction: Supervised-learning of thermal history for direct laser deposition

Selective laser melting (SLM) is an additive manufacturing technique which has the capability to produce complex metal parts with almost 100% density and good mechanical properties. Despite the potential benefits of SLM technology, there are technical challenges relating to the qualification and certification of the manufactured parts that limits its application in safety-critical industries, such as aerospace. Material porosity in SLM parts is detrimental for aerospace applications since it compromises structural integrity and could result in premature structural failure of parts. This paper describes the application of the non-destructive X-ray computed tomography (XCT) method to characterize the internal structure to enhance the understanding of the process parameters on material porosity and thus provide quality control of the SLM AlSi10Mg parts. An efficient and reliable XCT image processing procedure that involves image enhancement and ring artefact removal prior to image segmentation is pre...

Measurement and characterization of porosity in aluminium selective laser melting parts using X-ray CT

The x-ray radiographic testing method is often used for detecting defects as a non-destructive testing method (NDT). In many cases, NDT is used for aircraft components, welds, etc. Hence, the backgrounds are always more complex than a piece of steel. Radiographic images are low contrast, dark and high noise image. It is difficult to detect defects directly. So, image enhancement is a significant part of automated radiography inspection system. Histogram equalization and median filter are the most frequently used techniques to enhance the radiographic images. In this paper, the adaptive histogram equalization and contrast limited histogram equalization are compared with histogram equalization. The adaptive wavelet thresholding is compared with median filter. Through comparative analysis, the contrast limited histogram equalization and adaptive wavelet thresholding can enhance perception of defects better.

Image enhancement for radiography inspection

Radiographic testing method is often used for detecting defects as a non-destructive testing method. In this paper, an automatic computer-aided detection system based on Support Vector Machine (SVM) was implemented to detect welding defects in radiographic images. After extracting potential defects, two group features: texture feat ures and morphological features are extr acted. Afterwards SVM criteria and receiver operating characteristic curves are used to select features. Then Top 16 best features are used as inputs to a designed SVM classifier. The behavior of the proposed classification method is compared with various other classification techniques: k-means, linear discriminant, k-nearest neighbor classifiers and feed forward neural network. The results show the efficiency proposed method based on the support vector machine.

Recognition of welding defects in radiographic images by using support vector machine classifier

The radiographic testing method is often used as a nondestructive testing method for detecting welding defects. Because of the degraded quality and the small size of the defects, X-ray films are sometimes difficult to inspect. The interpretation of such images is often affected by a human operator's subjectivity. Digital image-processing techniques allow the interpretation to be automated. A key step in the automated-interpretation process is the segmentation of indications from the background. In this article, a robust method is presented to segment the radiographic image. In our algorithm, first adaptive wavelet thresholding and adaptive histogram equalization techniques are used to improve the quality of the radiographic image. Then, the radiographic image is divided into three parts, namely black, gray, and white parts using three-level thresholding based on maximum fuzzy entropy. The procedure to find the optimal thresholds is implemented by a genetic algorithm, which can overcome the comput...

Radiographic Image Segmentation for Weld Inspection Using a Robust Algorithm

The X-ray radiographic testing method is often used for detecting defects as a non-destructive testing method (NDT). In many cases, NDT is used for aircraft components, welds, etc. Hence, the backgrounds are always more complex than a piece of steel. It is difficult to detect defects using conventional image processing methods. In this paper, we propose a genetic algorithm to find the optimal thresholds to segment X-ray images. In our algorithms, after obtaining the X-ray image, we firstly use adaptive histogram equalization technique and wavelet thresholding to improve the quality of the radiographic image. Then the image is divided into three parts, namely dark, gray and white part. The fuzzy region of their member functions can be determined by maximizing fuzzy entropy. The procedure to find the optimal combination of all the fuzzy parameters is implemented by genetic algorithm, which can overcome the computational complexity problem. The experimental results show that our proposed method gives good performance for X-ray image.

Brian Stephen Wong

Papers

Measurement and characterization of porosity in aluminium selective laser melting parts using X-ray CT

Image enhancement for radiography inspection

Recognition of welding defects in radiographic images by using support vector machine classifier

Radiographic Image Segmentation for Weld Inspection Using a Robust Algorithm

X-ray image segmentation based on genetic algorithm and maximum fuzzy entropy