Gerald Zauner

Quantitative Determination of Porosity by Active Thermography

We present a new approach based on Active Thermography by which it is possible to produce images of porosity in Carbon Fibre Reinforced Plastics that correspond striking well with results obtained by ultrasonic testing. We applied the method of Pulsed Thermography by using flashes of light for the generation of heat. The evolution of surface temperatures which depends on the diffusivity of the sample and on the sample’s geometry could be well fitted by means of a heat conduction model. Images of porosity are generated by means of an evaluation by which the influence of the light intensity distribution on the data is eliminated. Thus corrections with respect to the lateral distribution of the heat generation are not necessary, nor are emissivity corrections. Corrections due to the influence of geometry, however, had to be taken into account. The quantitative evaluation of the porosity is based on its linear relation to the diffusivity. Images of porosity obtained thermographically are compared with corresp...

The use of a high temperature wind tunnel for MT-SOFC testing—Part I: detailed experimental temperature measurement of an MT-SOFC using an avant-garde high temperature wind tunnel and various measurement techniques

The purpose of the first part of this study was to compare four different temperature measuring methods. The application of these tools for possible temperature monitoring or calibration of monitors of microtubular solid oxide fuel cells (MT-SOFCs) is explored. It was found that a thermographic camera is very useful to visualize the temperature gradient on the outside of a cell, while an electrochemical impedance spectroscopy method was useful for estimating the core temperature of a test cell. A standard thermocouple was also used in combination with the previous two methods. Furthermore, an inexpensive laser guided thermometer was also tested for MT-SOFC temperature measurement. This initial study has opened up a range of questions not only about the effect of the experimental apparatus on the measurement results but also about the radial temperature distribution through a MT-SOFC in a working mode. Both these topics will be further investigated in part II of this study through a computational fluid dynamics study. This should provide additional interesting information about any differences between testing single cells and those within a bundle of cells. The discussed results are expected to be mainly temperature related, which should have direct consequences on power output and optimized gas inlet temperatures.

Brain tumor classification using AFM in combination with data mining techniques.

Although classification of astrocytic tumors is standardized by the WHO grading system, which is mainly based on microscopy-derived, histomorphological features, there is great interobserver variability. The main causes are thought to be the complexity of morphological details varying from tumor to tumor and from patient to patient, variations in the technical histopathological procedures like staining protocols, and finally the individual experience of the diagnosing pathologist. Thus, to raise astrocytoma grading to a more objective standard, this paper proposes a methodology based on atomic force microscopy (AFM) derived images made from histopathological samples in combination with data mining techniques. By comparing AFM images with corresponding light microscopy images of the same area, the progressive formation of cavities due to cell necrosis was identified as a typical morphological marker for a computer-assisted analysis. Using genetic programming as a tool for feature analysis, a best model was created that achieved 94.74% classification accuracy in distinguishing grade II tumors from grade IV ones. While utilizing modern image analysis techniques, AFM may become an important tool in astrocytic tumor diagnosis. By this way patients suffering from grade II tumors are identified unambiguously, having a less risk for malignant transformation. They would benefit from early adjuvant therapies.

Wavelet-based subsurface defect characterization in pulsed phase thermography for non-destructive evaluation

Active infrared thermography is a method for non-destructive testing (NDT) of materials and components. In pulsed thermography (PT), a brief and high intensity flash is used to heat the sample. The decay of the sample surface temperature is detected and recorded by an infrared camera. Any subsurface anomaly (e.g. inclusion, delamination, etc.) gives rise to a local temperature increase (thermal contrast) on the sample surface. Conventionally, in Pulsed Phase Thermography (PPT) the analysis of PT time series is done by means of Discrete Fourier Transform producing phase images which can suppress unwanted physical effects (due to surface emissivity variations or non-uniform heating). The drawback of the Fourier-based approach is the loss of temporal information, making quantitative inversion procedures tricky (e.g. defect depth measurements). In this paper the complex Morlet-Wavelet transform is used to preserve the time information of the signal and thus provides information about the depth of a subsurface defect. Additionally, we propose to use the according phase contrast value to derive supplementary information about the thermal reflection properties at the defect interface. This provides additional information (e.g. about the thermal mismatch factor between the specimen and the defect) making interpretation of PPT results easier and perhaps unequivocal.

Data mining techniques for AFM- based tumor classification

The present paper deals with the application of atomic force microscopy (AFM) as a tool for morphological characterization of histological brain tumor samples. Data mining techniques will be applied for automatic identification of brain tumor tissues based on AFM images by means of classifying grade II and IV tumors. The rapid advancement of AFM in recent years turned it into a valuable and useful tool to determine the topography of surface nanoscale structures with high precision. Therefore, it is used in a variety of applications in life science, materials science, electrochemistry, polymer science, biophysics, nanotechnology, and biotechnology. Minkowski functionals are used (in particular the Euler- Poincaré characteristic) as a feature descriptor to characterize global geometric structures in images related to the topology of the AFM image. In order to improve classification accuracy on the one hand, but to infer interpretable information from AFM images for domain experts on the other hand, feature analysis and reduction will be applied. From a data mining point of view, Genetic Programming will be introduced as a sophisticated method for both feature analysis and reduction as well as for producing highly accurate and interpretable models. Support Vector Machines will be used for comparison reasons when talking about reachable model accuracy.

Application of wavelet analysis in active thermography for non-destructive testing of CFRP composites

Active infrared thermography is a non-destructive testing (NDT) technique used for non-contact inspection of components and materials by temporal mapping of thermal patterns by means of infrared imaging. Through the application of a short heat pulse, thermal waves of various amplitudes and frequencies are launched into the specimen allowing a signal analysis based on amplitude and phase information (pulsed phase thermography PPT). The wavelet transform (with complex wavelets) can be used with PPT data in a similar way as the classical Fourier transform however with the advantage of preserving time information of the signal which can then be correlated to defect depth, and in this way allowing a quantitative evaluation. In this paper we review the methodology of PPT and the associated signal analysis (Fourier analysis, wavelet analysis) to obtain quantitative defect depth information. We compare and discuss the results of thermal FEM simulations with experimental data and show the advantages of wavelet based signal analysis for defect depth measurements and material characterization.

Comparative defect evaluation of aircraft components by active thermography

Active Thermography has become a powerful tool in the field of non-destructive testing (NDT) in recent years. This infrared thermal imaging technique is used for non-contact inspection of materials and components by visualizing thermal surface contrasts after a thermal excitation. The imaging modality combined with the possibility of detecting and characterizing flaws as well as determining material properties makes Active Thermography a fast and robust testing method even in industrial-/production environments. Nevertheless, depending on the kind of defect (thermal properties, size, depth) and sample material (CFRP carbon fiber reinforced plastics, metal, glass fiber) or sample structure (honeycomb, composite layers, foam), active thermography can sometimes produce equivocal results or completely fails in certain test situations. The aim of this paper is to present examples of results of Active Thermography methods conducted on aircraft components compared to various other (imaging) NDT techniques, namely digital shearography, industrial x-ray imaging and 3D-computed tomography. In particular we focus on detection limits of thermal methods compared to the above-mentioned NDT methods with regard to: porosity characterization in CFRP, detection of delamination, detection of inclusions and characterization of glass fiber distributions.

Temperature mapping in heat treatment process with a standard color-video camera by means of image processing

Thermal imaging systems are usually based on IR-camera systems analyzing the infrared part of the electromagnetic spectrum to carry out temperature measurements. These systems are very accurate but also very expensive. A “low-cost” thermal-vision-system based on a standard CCD color video camera in combination with an image processing system is presented. The system was developed to visualize the temperature distribution inside a plasma-reactor. The temperature of the heat-treated work pieces lies in the range of 450°C to 650°C. By analyzing the thermal light emission of these objects a temperature map can be made to visualize and measure temperature differences of the reactor interior.

Machine vision based quality inspection of flat glass products

This application paper presents a machine vision solution for the quality inspection of flat glass products. A contact image sensor (CIS) is used to generate digital images of the glass surfaces. The presented machine vision based quality inspection at the end of the production line aims to classify five different glass defect types. The defect images are usually characterized by very little ‘image structure’, i.e. homogeneous regions without distinct image texture. Additionally, these defect images usually consist of only a few pixels. At the same time the appearance of certain defect classes can be very diverse (e.g. water drops). We used simple state-of-the-art image features like histogram-based features (std. deviation, curtosis, skewness), geometric features (form factor/elongation, eccentricity, Hu-moments) and texture features (grey level run length matrix, co-occurrence matrix) to extract defect information. The main contribution of this work now lies in the systematic evaluation of various machine learning algorithms to identify appropriate classification approaches for this specific class of images. In this way, the following machine learning algorithms were compared: decision tree (J48), random forest, JRip rules, naive Bayes, Support Vector Machine (multi class), neural network (multilayer perceptron) and k-Nearest Neighbour. We used a representative image database of 2300 defect images and applied cross validation for evaluation purposes.

Data mining supported analysis of medical atomic force microscopy images

Taking a look at actual developments in the field of bioinformatics, computer support in different fields of medicine and biology is an increasing field of interest. Especially for recognition and classification of various kinds of diseases, researchers already identified the usage of data mining techniques as supporting tool for computer supported analysis and diagnosis. In order to build such tools, highly accurate and machine-readable data is needed. Therefore, atomic force microscopy (AFM) is shown within this paper as measurement technology, that provides true 3-D data of any tissue and thus is able to build the fundament for further computational processing steps. Within two practical examples dealing with brain tumor tissue on the one hand and myocardial muscle tissue on the other hand, data mining supported analysis of AFM images will be demonstrated. The combination of data mining techniques with AFM measurements at nanoscale therefore forms a promising fundament for future computer enabled support systems in medicine and biology.