Microstructure and mechanical properties of hot extruded Mg–Al–Mn–Ca alloy produced by rapid solidification powder metallurgy

The microstructure and mechanical properties of hot extruded Mg–Al–Mn–Ca alloy was investigated. Both rapid solidified powders and cast billets were extruded at 573, 623 and 673 K to optimize the processing conditions for obtaining better mechanical response. Powder was consolidated to prepare the extrusion billets using both cold compaction and Spark Plasma Sintering at 473 K. The tensile properties of the extruded alloy were then evaluated and correlated to the observed microstructure. The results show that the use of rapid solidified powder could lead to effective grain refinement, which in turn resulted in the improved mechanical response, especially compared to the extruded conventional cast material.     

Probabilistic simulation of rock quality designation (RQD)

Rock quality designation (RQD) parameter distributions are obtained by Monte Carlo simulation techniques to describe the excellent, good, fair, poor and very poor rock percentages within a given rock mass. In the simulations, the intact length distributions are chosen either as the negative exponential or logarithmic-normal distribution. Rock quality designation is dependent on a predetermined level of truncation and therefore the simulation procedure is repeated for different levels and their effects are presented in various charts. RQD distributions are given in detail for different average number of discontinuities. The logarithmic normal distribution is unique in the sense that it preserves in the simulation the variance of intact lengths independently from the mean value. The greater the variance the better is the rock quality.     

Highly porous mullite ceramics from engineered alkali activated suspensions

Air may be easily incorporated by vigorous mechanical stirring, with the help of surfactants, of activated geopolymer‐yielding suspensions. The cellular structure is stabilized by the viscosity increase caused by curing reactions, configuring an “inorganic gel casting”. The present paper is aimed at extending this approach to mullite foams, obtained by the thermal treatment of engineered alkali activated suspensions. “Green” foams were first obtained by gel casting of a suspension for Na‐geopolymer enriched with reactive γ‐Al₂O₃ powders. Sodium was later extracted by ionic exchange with ammonium salts. In particular, the removal of Na⁺ ions was achieved by immersion in ammonium nitrate solution overnight, with retention of the cellular structure. Finally, the ion‐exchanged foams were successfully converted into pure mullite foams by application of a firing treatment at 1300°C, for 1 hour. Preliminary results concerning the extension of the concept to mullite three‐dimensional scaffolds are presented as well.     

Reduced-gate convolutional long short-term memory using predictive coding for spatiotemporal prediction

Spatiotemporal sequence prediction is an important problem in deep learning. We study next-frame(s) video prediction using a deep-learning-based predictive coding framework that uses convolutional LSTM (convLSTM) modules. We introduce a novel rgcLSTM architecture that requires a significantly lower parameter budget than a comparable convLSTM. By using a single multifunction gate, our reduced-gate model achieves equal or better next-frame(s) prediction accuracy than the original convolutional LSTM while using a smaller parameter budget, thereby reducing training time and memory requirements. We tested our reduced gate modules within a predictive coding architecture on the moving MNIST and KITTI datasets. We found that our reduced-gate model has a significant reduction of approximately 40% of the total number of training parameters and a 25% reduction in elapsed training time in comparison with the standard convolutional LSTM model. The performance accuracy of the new model was also improved. This makes our model more attractive for hardware implementation, especially on small devices. We also explored a space of 20 different gated architectures to get insight into how our rgcLSTM fits into that space.     

Monitoring multistage processes with autocorrelated observations

In multistage manufacturing processes, autocorrelations within stages over time are prevalent and the classical control charts are often ineffective in monitoring such processes. In this paper, we derive a linear state space model of an autocorrelated multistage process as a vector autoregressive process, and construct novel multivariate control charts, CBAM and Conditional-based MEWMA, for detecting the mean changes in a multistage process based on a projection scheme by incorporating in-control stage information. When in-control stages are unknown, finding in-control stages is a challenging issue due to the autocorrelations over time and the sequential correlations between stages. To overcome this difficulty, we propose a conditional-based selection that chooses stages with strong evidences of in-control stage using the cascading property of multistage processes. The information of selected stages is effectively utilised in obtaining powerful test statistics for detecting a mean change. The performance of the proposed charts is compared with other existing procedures under different scenarios. Both simulation studies and a real example show the effectiveness of the conditional-based charts in detecting a wide range of small mean shifts compared with the other existing control charts.     

Effect of ionomer on clay dispersions in polypropylene‐layered silicate nanocomposites

In this study, polypropylene (PP)/clay nanocomposites containing different concentrations of ethylene‐methacrylic acid ionomer (i.e. Surlyn®) were prepared, and the effect of ionomer on clay dispersion was studied via WAXD, rheology, SEM, and TEM. The role of the ionomer in the nanocomposites was compared with that of maleic anhydride grafted PP (PP‐g‐MA), which has been widely used as a compatibilizer in making PP/clay nanocomposites. With an increase in the concentration of compatibilizer, the position of d₀₀₁ peak of OMMT shifted toward a lower angle for PP‐g‐MA system, while the position remained almost unchanged for Surlyn system, in which a larger interlayer spacing (d₀₀₁) was found with respect to the former. In rheology, the addition of the ionomer led to a gradual increase in both moduli and complex viscosity, and the nonterminal behavior at low frequency was observed in both systems. In addition, the ternary hybrid containing 20 wt % Surlyn achieved the largest enhancement in relative viscosity, which was more than that of the nanocomposite prepared from pure Surlyn or pure PP, presumably indicative of the existence of strong interaction between the components. Finally, SEM and TEM micrographs demonstrated that exfoliated structure was preferred for PP/Surlyn/OMMT hybrids, while intercalated morphology for PP/PP‐g‐MA/OMMT. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci 104: 4024–4034, 2007     

Liver Segmentation Technique for CT Images Based on Statistical Analysis

This article presented the automatic diagnosis of liver pathologies and its 3D volume rendering. The first and fundamental step in all these studies is the automatic liver segmentation, which is still an open problem. In this thesis, two automatic methods are described to segment the liver from abdominal CT image data. The first step is image enhancement. Secondly, texture analysis is using standard statistical measures, finally it is a rough mask to segment the liver.