Improving delamination resistance of carbon fiber reinforced polymeric composite by interface engineering using carbonaceous nanofillers through electrophoretic deposition: An assessment at different in-service temperatures

In this article, modification of carbon fiber surface by carbon based nanofillers (multi-walled carbon nanotubes [CNT], carbon nanofibers, and multi-layered graphene) has been achieved by electrophoretic deposition technique to improve its interfacial bonding with epoxy matrix, with a target to improve the mechanical performance of carbon fiber reinforced polymer composites. Flexural and short beam shear properties of the composites were studied at extreme temperature conditions; in-situ cryo, room and elevated temperature (−196, 30, and 120°C respectively). Laminate reinforced with CNT grafted carbon fibers exhibited highest delamination resistance with maximum improvement in flexural strength as well as in inter-laminar shear strength (ILSS) among all the carbon fiber reinforced epoxy (CE) composites at all in-situ temperatures. CNT modified CE composite showed increment of 9% in flexural strength and 17.43% in ILSS when compared to that of unmodified CE composite at room temperature (30°C). Thermomechanical properties were investigated using dynamic mechanical analysis. Fractography was also carried out to study different modes of failure of the composites.     

Localized swelling of Fe-Cr-Ni alloy rollers in high-temperature applications

Localized swelling has been observed in 24Cr-24Ni-Nb steel transportation rollers used in the normalizing furnace of a plate mill after prolonged service at high temperature. Due to high localized thermal and mechanical stresses, the chromia layer formed on the roller surface ruptures, exposing the roller substrate to furnace oxygen. Oxidation of second-phase carbides results in the formation of carbon monoxide at very high partial pressure. This leads to formation of voids, leading in turn to localized swelling of the roller material.     

Fabrication and Characterisation of Rutile-TiO_2 Coatings on Metallic Alloy Substrates

RUTILE-TJO2is a versatile material possessing manyinteresting physical,chemical,optical and dielectricproperties[l].It is also a good tribological material thatcan offer low friction and low wear rate[2,3].However,the application of rutile in the tribological field hasbeen limited to titanium alloys only so far,for example,by thermal oxidation of titanium alloys[4,5].Very fewinvestigations have been directed to the tribologicalapplications of rutile films on substrates other thantitanium al…     

Microstructure and properties of flame sprayed tungsten carbide coatings

This article reports on feasibility experiments carried out with oxy-acetylene spray system with various oxygen to fuel ratios using two different tungsten carbide powders and powder feeding methods, to evaluate the newly developed fused WC, synthesised by transferred arc thermal plasma method. Transferred arc thermal plasma method is more economical and less energy intensive than the conventional arc method and results in a fused carbide powder with higher hardness. The microstructure and phase composition of powders and coatings were analysed by optical and scanning electron microscopy, energy dispersive spectroscopy and X-ray diffraction. Carbon content of the powders and coatings were determined to study the decarburisation of the material during spraying process. Coatings were also characterised by their hardness and abrasive wear. The effects of metallurgical transformation and phase content are related to wear performance. The results demonstrate that the powders exhibit various degree of phase transformation during the spray process depending on the type of powder, powder feeding and spray parameters. The carbon loss during the spray process in excess of 45% resulted in reduced hardness and wear resistance of the coatings. Coatings with high amount of WC and W₂C along with FeW₃C showed higher wear resistance. Thus, coatings of high wear resistance can be produced using fused tungsten carbide powder with WC and W₂C phases, which can be economically synthesised by thermal plasma transferred arc method.     

Updating a 1950s-generation aluminum smelter

Hindalco’s aluminum electrolysis cells were initially installed in 1962, and the technology was based on 1950s-generation pots. Although Hindalco expanded its aluminums melting capacity from 20,000 tonnes per year to 175,000 tonnes per year, the basic design of the pots remained unchanged. In view of energy price increases, and to keep pace with the latest developments in aluminum smelting technology, Hindalco undertook efforts to modernize its facilities. In spite of numerous constraints, the Hindalco smelter has been able to achieve performance nearly equivalent to that of 1980s-generation pots by retrofitting new technologies. This has resulted in considerable savings in electrical energy consumption and raw materials usage.     

Combining the evidences of temporal and spectral enhancement techniques for improving the performance of Indian language identification system in the presence of background noise

Language Identification has gained significant importance in recent years, both in research and commercial market place, demanding an improvement in the ability of machines to distinguish between languages. Although methods like Gaussian mixture models, hidden Markov models and neural networks are used for identifying languages the problem of language identification in noisy environments could not be addressed so far. This paper addresses the performance of automatic language identification system in noisy environments. A comparative performance analysis of speech enhancement techniques like minimum mean squared estimation, spectral subtraction and temporal processing, with different types of noise at different SNRs, is presented here. Though these individual enhancement techniques may not yield good performance with different types of noise at different SNRs, it is proposed to combine the evidences of all these techniques to improve the overall performance of the system significantly. The language identification studies are performed using IITKGP-MLILSC (IIT Kharagpur-Multilingual Indian Language Speech Corpus) databases which consists of 27 languages.     

Pedestrian Detection and Tracking Using Deformable Part Models and Kalman Filtering

Abstract： Pedestrian detection techniques are important and challenging especially for complex real world scenes. They can be used for ensuring pedestrian safety, ADASs （advance driver assistance systems） and safety surveillance systems. In this paper, we propose a novel approach for multi-person tracking-by-detection using deformable part models in Kalman filtering framework. The Kalman filter is used to keep track of each person and a unique label is assigned to each tracked individual. Based on this approach, people can enter and leave the scene randomly. We test and demonstrate our results on Caltech Pedestrian benchmark, which is two orders of magnitude larger than any other existing datasets and consists of pedestrians varying widely in appearance, pose and scale. Complex situations such as people occluded by each other are handled gracefully and individual persons can be tracked correctly after a group of people split. Experiments confirm the real-time performance and robustness of our system, working in complex scenes. Our tracking model gives a tracking accuracy of 72.8% and a tracking precision of 82.3%. We can further reduce false positives by 2.8%, using Kalman filtering.     

Transfer in variable-reward hierarchical reinforcement learning

Transfer learning seeks to leverage previously learned tasks to achieve faster learning in a new task. In this paper, we consider transfer learning in the context of related but distinct Reinforcement Learning (RL) problems. In particular, our RL problems are derived from Semi-Markov Decision Processes (SMDPs) that share the same transition dynamics but have different reward functions that are linear in a set of reward features. We formally define the transfer learning problem in the context of RL as learning an efficient algorithm to solve any SMDP drawn from a fixed distribution after experiencing a finite number of them. Furthermore, we introduce an online algorithm to solve this problem, Variable-Reward Reinforcement Learning (VRRL), that compactly stores the optimal value functions for several SMDPs, and uses them to optimally initialize the value function for a new SMDP. We generalize our method to a hierarchical RL setting where the different SMDPs share the same task hierarchy. Our experimental results in a simplified real-time strategy domain show that significant transfer learning occurs in both flat and hierarchical settings. Transfer is especially effective in the hierarchical setting where the overall value functions are decomposed into subtask value functions which are more widely amenable to transfer across different SMDPs.     

Applicability of feed-forward and recurrent neural networks to Boolean function complexity modeling

In this paper, we present the feed-forward neural network (FFNN) and recurrent neural network (RNN) models for predicting Boolean function complexity (BFC). In order to acquire the training data for the neural networks (NNs), we conducted experiments for a large number of randomly generated single output Boolean functions (BFs) and derived the simulated graphs for number of min-terms against the BFC for different number of variables. For NN model (NNM) development, we looked at three data transformation techniques for pre-processing the NN-training and validation data. The trained NNMs are used for complexity estimation for the Boolean logic expressions with a given number of variables and sum of products (SOP) terms. Both FFNNs and RNNs were evaluated against the ISCAS benchmark results. Our FFNNs and RNNs were able to predict the BFC with correlations of 0.811 and 0.629 with the benchmark results, respectively.     

Viscoelastic material design with negative stiffness components using topology optimization

An application of topology optimization to design viscoelastic composite materials with elastic moduli that soften with frequency is presented. The material is a two-phase composite whose first constituent is isotropic and viscoelastic while the other is an orthotropic material with negative stiffness but stable. A concept for this material based on a lumped parameter model is used. The performance of the topology optimization approach in this context is illustrated using three examples.