Effect of low-cycle fatigue on the hot ductility of plain carbon steel

In a continuous casting steelmaking operation, the surface of a slab is under a condition that can be characterized as high-temperature, low-cycle fatigue in which the tensile and compressive stress is repeatedly developed. For this reason, for the evaluation of the hot ductility of a slab, considering the fatigue deformation is more feasible before a tensile or compressive test. In this study, the effects of low-cycle fatigue on the hot ductility of steels with a carbon content of 0.06–0.8 wt.% are investigated at various temperatures. For a carbon content of 0.06%, there were no significant differences between the RA values from a simple tensile test and those from a tensile test after fatigue deformation. The tendency of ductility deterioration with fatigue deformation is evident in 0.1 %C steel, and is due to the deformation-induced ferrite film that forms around the prior austenite grain. Conversely, high carbon steel containing 0.8 %C did not show a recovery of hot ductility in a low temperature region, and the specimen on which the tensile was measured after fatigue showed a higher hot ductility in the low temperature region, which is thought to result from the pearlite refinement effects. As the results obtained in this work showed noticeable differences in the hot ductility of carbon steel through the test conditions, it is suggested that for more accurate data, fatigue deformation be adopted in which the temperature range in an unbending operation is determined in the steelmaking factory.     

Boron distribution in a low-alloy steel

Boron distribution in a low-alloy steel (15B26:0.25C-0.29Cr-0.03Ti-0.028Al-0.0016B) has been characterized employing Fission Track Etching (FTE) method. The characteristics of boron distribution with variation of cooling rate after austenitization and through case-hardened depth after carburization were analyzed. Hardenability of 15B26 steel was also evaluated through Jominy-end-quench test and the results are as follows: It was observed that, in austenitized 15B26 steel, boron was distributed uniformly over the whole area of specimen with a little segregation along the austenite grain boundaries at higher cooling rates and boron precipitates were formed in the intergranular as well as transgranular regions at lower cooling rates. Jominy equivalents (HRC 35) of 15B26 steel were fairly increased between the Jominy temperatures of 820°C and 850°C, which might result from the increase of the amount of soluble boron in austenite due to the dissolution of borocarbides between 820°C and 850°C. In carburized 15B26 steel, the different through thickness features of boron distribution from the carburized surface were found; coarse nodular boron precipitates up to the depth of 150 μm; uniform distribution of dissolved boron between 150~650 μm; and segregation of boron atoms along grain boundaries in the regions deeper than 650 μm.     

Galvanic coupling effect on corrosion behavior of Al alloy-matrix composites

Galvanic coupling effect on the corrosion of SiC-reinforced aluminum alloy-matrix composites was investigated in a sodium chloride solution. The potentiodynamic polarization measurement indicated that pitting potentials of metal matrix composites (MMCs) and AA2124 matrix alloy were similar, and pitting potential of MMCs was almost same as corrosion potential, while pitting susceptibility of MMCs was higher than that of AA2124 alloy. Galvanic current by formation of galvanic couple between SiCw and matrix reveals very low value because of large cathodic polarization of SiC. However, by increasing potential of matrix to pitting potential by this galvanic couple and thus, forming pits easily at the weak passive film near SiC reinforcing phase preferentially, it is concluded that pitting susceptibility of MMCs increases highly than AA2124 alloy of matrix composition.     

Microstructure and mechanical property of irradiated Zr−2.5Nb pressure tube in Wolsong unit-1

With the aim of assessing the degradation of Zr−2.5Nb pressure tubes operating in the Wolsong unit-1 nuclear power plant, characterization tests are being conducted on irradiated Zr−2.5Nb tubes removed after 10-year operation. The examined tube had been exposed to temperatures ranging from 264 to 306°C and a neutron fluence of 8.9×10²¹ n/cm² (E>1 MeV) at the maximum. Tensile tests were carried out at temperatures ranging from RT to 300°C. The density of a-type and c-type dislocations was examined on the irradiated Zr-2.5Nb tube using a transmission electron microscope. Neutron irradiation up to 8.9×10²¹ n/cm² (E>1 MeV) yielded an increase in a-type dislocation density of the Zr−2.5Nb pressure tube to 7.5×10¹⁴ m⁻², which was highest at the inlet of the tube exposed to the low temperature of 275°C. In contranst, the c-component dislocation density did not change with irradiation, keeping an initial dislocation density of 0.8×10¹⁴ m⁻² over the whole length of the tube. As expected, the neutron irradiation increased mechanical strength by about 17–26% in the transverse direction and by 34–39% in the longitudinal direction compared to that of the unirradiated tube at 300°C. The change in the mechanical properties with irradiation is discussed in association with the microstructural change as a function of temperature and neutron fluence.     

Page segmentation using minimum homogeneity algorithm and adaptive mathematical morphology

Document layout analysis or page segmentation is the task of decomposing document images into many different regions such as texts, images, separators, and tables. It is still a challenging problem due to the variety of document layouts. In this paper, we propose a novel hybrid method, which includes three main stages to deal with this problem. In the first stage, the text and non-text elements are classified by using minimum homogeneity algorithm. This method is the combination of connected component analysis and multilevel homogeneity structure. Then, in the second stage, a new homogeneity structure is combined with an adaptive mathematical morphology in the text document to get a set of text regions. Besides, on the non-text document, further classification of non-text elements is applied to get separator regions, table regions, image regions, etc. The final stage, in refinement region and noise detection process, all regions both in the text document and non-text document are refined to eliminate noises and get the geometric layout of each region. The proposed method has been tested with the dataset of ICDAR2009 page segmentation competition and many other databases with different languages. The results of these tests showed that our proposed method achieves a higher accuracy compared to other methods. This proves the effectiveness and superiority of our method.     

Usability Principles for Augmented Reality Applications in a Smartphone Environment

Through the rapid spread of smartphones, users have access to many types of applications similar to those on desktop computer systems. Smartphone applications using augmented reality (AR) technology make use of users' location information. As AR applications will require new evaluation methods, improved usability and user convenience should be developed. The purpose of the current study is to develop usability principles for the development and evaluation of smartphone applications using AR technology. We develop usability principles for smartphone AR applications by analyzing existing research about heuristic evaluation methods, design principles for AR systems, guidelines for handheld mobile device interfaces, and usability principles for the tangible user interface. We conducted a heuristic evaluation for three popularly used smartphone AR applications to identify usability problems. We suggested new design guidelines to solve the identified problems. Then, we developed an improved AR application prototype of an Android-based smartphone, which later was conducted a usability testing to validate the effects of usability principles.     

Exaggerating Character Motions Using Sub‐Joint Hierarchy

Motion capture cannot generate cartoon‐style animation directly. We emulate the rubber‐like exaggerations common in traditional character animation as a means of converting motion capture data into cartoon‐like movement. We achieve this using trajectory‐based motion exaggeration while allowing the violation of link‐length constraints. We extend this technique to obtain smooth, rubber‐like motion by dividing the original links into shorter sub‐links and computing the positions of joints using Bézier curve interpolation and a mass‐spring simulation. This method is fast enough to be used in real time.     

A Hidden‐picture Puzzles Generator

A hidden‐picture puzzle contains objects hidden in a background image, in such a way that each object fits closely into a local region of the background. Our system converts image of the background and objects into line drawing, and then finds places in which to hide transformed versions of the objects using rotation‐invariant shape context matching. During the hiding process, each object is subjected to a slight deformation to enhance its similarity to the background. The results were assessed by a panel of puzzle‐solvers.     

Mining partial periodic correlations in time series

Recently, periodic pattern mining from time series data has been studied extensively. However, an interesting type of periodic pattern, called partial periodic (PP) correlation in this paper, has not been investigated. An example of PP correlation is that power consumption is high either on Monday or Tuesday but not on both days. In general, a PP correlation is a set of offsets within a particular period such that the data at these offsets are correlated with a certain user-desired strength. In the above example, the period is a week (7 days), and each day of the week is an offset of the period. PP correlations can provide insightful knowledge about the time series and can be used for predicting future values. This paper introduces an algorithm to mine time series for PP correlations based on the principal component analysis (PCA) method. Specifically, given a period, the algorithm maps the time series data to data points in a multidimensional space, where the dimensions correspond to the offsets within the period. A PP correlation is then equivalent to correlation of data when projected to a subset of the dimensions. The algorithm discovers, with one sequential scan of data, all those PP correlations (called minimum PP correlations) that are not unions of some other PP correlations. Experiments using both real and synthetic data sets show that the PCA-based algorithm is highly efficient and effective in finding the minimum PP correlations. Zhen He is a lecturer in the Department of Computer Science at La Trobe University. His main research areas are database systems optimization, time series mining, wireless sensor networks, and XML information retrieval. Prior to joining La Trobe University, he worked as a postdoctoral research associate in the University of Vermont. He holds Bachelors, Honors and Ph.D degrees in Computer Science from the Australian National University. X. Sean Wang received his Ph.D degree in Computer Science from the University of Southern California in 1992. He is currently the Dorothean Chair Professor in Computer Science at the University of Vermont. He has published widely in the general area of databases and information security, and was a recipient of the US National Science Foundation Research Initiation and CAREER awards. His research interests include database systems, information security, data mining, and sensor data processing. Byung Suk Lee is associate professor of Computer Science at the University of Vermont. His main research areas are database systems, data modeling, and information retrieval. He held positions in industry and academia: Gold Star Electric, Bell Communications Research, Datacom Global Communications, University of St. Thomas, and currently University of Vermont. He was also a visiting professor at Dartmouth College and a participating guest at Lawrence Livermore National Laboratory. He served on international conferences as a program committee member, a publicity chair, and a special session organizer, and also on US federal funding proposal review panel. He holds a BS degree from Seoul National University, MS from Korea Advanced Institute of Science and Technology, and Ph.D from Stanford University. Alan C. H. Ling is an assistant professor at Department of Computer Science in University of Vermont. His research interests include combinatorial design theory, coding theory, sequence designs, and applications of design theory.     

Replication of nanostructures on microstructures by intermediate film mold inserted hot embossing process

The present study proposes a simple method to replicate nano/micro combined multiscale structures using an intermediate film mold and anodic aluminum oxide (AAO) nanomold in hot embossing process. The proposed method is simply to add an intermediate film mold with microscale thru-hole patterns to the ordinary mold system, on which nanostructures are patterned, in the hot embossing process. The intermediate film mold is inserted between polymer substrate and AAO nanomold. During the hot embossing process, the polymer first fills microscale thru-hole patterns in the intermediate film mold and subsequently fills nanopores in AAO nanomold, resulting in the nano/micro combined structures. The intermediate film molds, which have microscale thru-hole patterns were fabricated by micro-milling, laser ablation, etching methods and/or LIGA process. The nano/micro combined structures were successfully replicated by the proposed method.