Applications of heat-diffusion equation for surface wrapping: hole detection and normal orientation

This paper presents a method for detecting holes during the surface wrapping process which cause surface leaks into the volume parts that shall not be meshed. The method solves a heat-diffusion equation on the background octree mesh, which is generated based on user-defined parameters, and its resolution corresponds to the resolution of the wrapper surface mesh. The heat problem is posed with the constant heat source in the volume, and the holes are detected as regions of high temperature gradients. The method detects both holes with open-boundary edges and semantic holes due to some missing parts. The sensitivity of the method is controlled via user-adjustable parameter which represents the ratio between the volume that shall not be meshed and the area of the hole. In addition, it is demonstrated that the method can be used to correct the orientation of normals in the surface mesh by utilising the property that high temperature is always found inside the volume. The potential of the method is presented on complex engineering examples.     

Prediction of the natural gas consumption in chemical processing facilities with genetic programming

Cinkarna Ltd. is a chemical processing company in Slovenia and the country’s largest manufacturer of titanium oxides (TiO₂). Chemical processing and titanium oxide manufacturing in particular requires high natural gas consumption, and it is difficult to accurately pre-order gas from suppliers. In accordance with the Energy Agency of the Republic of Slovenia regulations, each natural gas supplier regulates and determines the charges for the differences between the ordered (predicted) and the actually supplied quantities of natural gas. Yearly charges for these differences total 1.11 % of supplied natural gas costs (average 50,960 EUR per year). This paper presents natural gas consumption prediction and the minimization of associated costs. The data on daily temperature, steam boilers, sulfur acid and TiO₂ production was collected from January 2012 until November 2014. Based on the collected data, a linear regression and a genetic programming model were developed. Compared to the specialist’s prediction of natural gas consumption, the linear regression and genetic programming models reduce the charges for the differences between the ordered and the actually supplied quantities by 3.00 and 5.30 times, respectively. Also, from January until November 2014 the same genetic programming model was used in practice. The results show that in a similar gas consumption regime the differences between the ordered and the actually supplied quantities are statistically significant, namely, they are 3.19 times lower (t test, p < 0.05) than in the period in which the specialist responsible for natural gas consumption made the predictions.     

Efficient SAT-based bounded model checking for software verification

This paper discusses our methodology for formal analysis and automatic verification of software programs. It is applicable to a large subset of the C programming language that includes pointer arithmetic and bounded recursion. We consider reachability properties, in particular whether certain assertions or basic blocks are reachable in the source code, or whether certain standard property violations can occur. We perform this analysis via a translation to a Boolean circuit representation based on modeling basic blocks. The program is then analyzed by a back-end SAT-based bounded model checker, where each unrolling is mapped to one step in a block-wise execution of the program.     

Influence of nitriding on corrosion resistance of martensitic X17CrNi16‐2 stainless steel

Nitriding increases surface hardness and improves wear resistance of stainless steels. However, nitriding can sometimes reduce their corrosion resistance. In this paper, the influence of nitriding on the corrosion resistance of martensitic stainless steel was investigated. Plasma nitriding at 440 °C and 525 °C and salt bath nitrocarburizing were carried out on X17CrNi16‐2 stainless steel. Microhardness profiles of the obtained nitrided layers were examined. Phase composition analysis and quantitative depth profile analysis of the nitrided layers were preformed by X‐ray diffraction (XRD) and glow‐discharge optical emission spectrometry (GD‐OES), respectively. Corrosion behaviour was evaluated by immersion test in 1% HCl, salt spray test in 5% NaCl and electrochemical corrosion tests in 3.5% NaCl aqueous solution. Results show that salt bath nitrocarburizing, as well as plasma nitriding at low temperature, increased microhardness without significantly reducing corrosion resistance. Plasma nitriding at a higher temperature increased the corrosion tendency of the X17CrNi16‐2 steel.     

3-D/2-D registration of CT and MR to X-ray images

A crucial part of image-guided therapy is registration of preoperative and intraoperative images, by which the precise position and orientation of the patient's anatomy is determined in three dimensions. This paper presents a novel approach to register three-dimensional (3-D) computed tomography (CT) or magnetic resonance (MR) images to one or more two-dimensional (2-D) X-ray images. The registration is based solely on the information present in 2-D and 3-D images. It does not require fiducial markers, intraoperative X-ray image segmentation, or timely construction of digitally reconstructed radiographs. The originality of the approach is in using normals to bone surfaces, preoperatively defined in 3-D MR or CT data, and gradients of intraoperative X-ray images at locations defined by the X-ray source and 3-D surface points. The registration is concerned with finding the rigid transformation of a CT or MR volume, which provides the best match between surface normals and back projected gradients, considering their amplitudes and orientations. We have thoroughly validated our registration method by using MR, CT, and X-ray images of a cadaveric lumbar spine phantom for which "gold standard" registration was established by means of fiducial markers, and its accuracy assessed by target registration error. Volumes of interest, containing single vertebrae L1-L5, were registered to different pairs of X-ray images from different starting positions, chosen randomly and uniformly around the "gold standard" position. CT/X-ray (MR/ X-ray) registration, which is fast, was successful in more than 91% (82% except for L1) of trials if started from the "gold standard" translated or rotated for less than 6 mm or 17 degrees (3 mm or 8.6 degrees), respectively. Root-mean-square target registration errors were below 0.5 mm for the CT to X-ray registration and below 1.4 mm for MR to X-ray registration.     

Automated visual inspection of imprint quality of pharmaceutical tablets

Visual appearance is an important quality factor of pharmaceutical tablets. Moreover, it plays a key role in identification of tablets, which is needed to prevent mix-ups among various types of tablets. Since identification of tablets is most frequently done by imprints, good imprint quality, a property that makes the imprint readable, is of utmost importance in preventing mix-ups among the tablets. In this paper, we propose a novel method for automated visual inspection of tablets. Besides defect detection, imprint quality inspection is also considered. Performance of the method was evaluated on three different real tablet image databases of imprinted tablets. A “gold standard” was established by manually classifying tablets into a good and a defective class. The receiver operating characteristics (ROC) analysis indicated that the proposed method yields better sensitivity and specificity than the previous defect detection method.     

A protocol for evaluation of similarity measures for rigid registration

The accuracy and robustness of a registration method depend on a number of factors, such as imaging modality, image content and image degrading effects, the class of spatial transformation used for registration, similarity measure, optimization, and numerous implementation details. The complex interdependence of these factors makes the assessment of the influence of a particular factor on registration difficult, although it is often desirable to have some estimate of such influences prior to registration. The similarity measure used to create the cost function is one of the factors that most influences the quality of registration. Traditionally, limited information on the behavior of a similarity measure is obtained either by studying the quality of the final registration or by drawing plots of similarity measure values obtained by translating or rotating one image relative to the "gold standard." In this paper, we present a protocol for a more thorough, optimization-independent, and systematic statistical evaluation of similarity measures. This protocol estimates a similarity measure's capture range, the number, location and extent of local optima, and the accuracy and distinctiveness of the global optimum. To show that the proposed evaluation protocol is viable, we have conducted several experiments with nine similarity measures and real computed tomography and magnetic resonance (MR) images of a spine phantom, MR brain images, and MR and positron emission tomography brain images, for which "gold standard" registrations were available. We have also studied the impact of histogram bin size on the behavior of nine similarity measures. The proposed evaluation protocol is useful for selecting the best similarity measure and corresponding optimization method for a particular application, as well as for studying the influence of sampling, interpolation, histogram bin size, partial image overlap, and image degradation, such as noise, intensity inhomogeneity, and geometrical distortions on the behavior of a similarity measure.     

Influence of deep cryogenic treatment of high speed steel substrate on TiAlN coating properties

Deep cryogenic treatment in combination with classic heat treatment shows a significant improvement in wear resistance of high speed steel tools. The aim of this research was to investigate how the microstructure of the substrate tool steel material, which was altered by deep cryogenic treatment and plasma nitriding, influences the properties of TiAlN coating. The microstructure, topography and composition of the TiAlN coating were investigated using field‐emission scanning electron microscope, atomic force microscopy, XRD, and glow discharge optical emission spectroscopy. The coating adhesion was measured using the scratch test. The sliding wear resistance and the force required to break the coating were determined with the ball‐on‐flat method. Resistance to microabrasion was measured by free ball abrasion test. The results show that deep cryogenic treatment combined with plasma nitriding influence the adhesion of the TiAlN coating to the high speed steel substrate. Wear resistance tests show better wear resistance of deep cryogenic treated samples in comparison with conventionally heat treated ones.