Electrical conductivity of the graphene nanoplatelets coated natural and synthetic fibres using electrophoretic deposition technique

Herein, electrically conductive natural and synthetic yarns through electrophoretic deposition (EPD) technique were fabricated. A parametric study on the conductivity enhancement of the yarns is carried out by Taguchi method. Using this method, the desirable conditions are determined by studying the effects of important parameters on the electrical conductivity of the yarns in the EPD coating process. Based on the L₁₈ design of experiments table, the preferred combination of factors to obtain the highest electrical conductivity of the yarns is found by Taguchi analysis. In addition, the Pareto ANOVA analysis is conducted to identify the major contributing factors on the electrical conductivity of the yarns. Characterisation techniques, such as scanning electron microscopy (SEM), Fourier transformed infrared spectroscopy (FTIR) in attenuated total reflectance (ATR) mode, and thermogravimetric analysis (TGA) are utilised for better understanding the microstructure and physical properties. When powered by only 3 V, the maximum temperature of a Joule heated conductive sample based on natural fibre yarns reached 102°C in less than 25 s.     

High coercivity magnetic multi-wall carbon nanotubes for low-dimensional high-density magnetic recording media

Fe-embedded multi-wall carbon nanotubes (MWCNTs) were fabricated using Fe-catalyst by the chemical deposition method. Microscopic characterizations showed that the well-aligned MWCNTs were ～ 80 mm in length, with outer diameter of 20–50 nm and inner diameter of 10–20 nm. Magnetic properties were characterized in temperatures of 5 K and 305 K, which revealed that the MWCNTs exhibited high coercivity of 2600 Oe at 5 K and 732 Oe at 305 K. These values are much higher than that of bulk iron (～ 0.9 Oe) and Fe/Co/Ni nanoparticles or nano-wire arrays (～ 200–500 Oe) at the room temperature. This high coercivity and the structure of single-domain Fe nanoparticles isolated by anti-ferromagnetic MWCNTs make it a promising candidate for low-dimensional high-density magnetic recording media.     

Failure Investigation of Spline-Shaft of an Under Slung Crane

Investigation of the causes of premature failure of a spline-shaft used in the hoist gear box assembly of an under slung crane has been presented. The investigation consisted of visual inspection, chemical analysis, characterization of microstructures by optical and scanning electron microscopes, energy dispersive spectroscopy (EDS), and hardness measurement. Visual observation of the fracture surface of the shaft revealed multiple cracks all of which initiated at the sharp corners of the spline. Fractography of the shaft showed striations confirming fatigue. The chemical composition of the shaft was found to be close to EN-24 grade of hardened and tempered steel which is specified as per IS 5517. Microstructural examination showed branched-out cracks and few of them were associated with oxide layer or scale which was confirmed by EDS analysis. Since the shaft was not exposed to high temperature in service, formation of scale along the crack suggested that it was pre-existing in the component. Base matrix of the shaft revealed bainitic microstructure. Hardness values were found to be lower than that obtained for EN-24 grade in hardened and tempered condition. Analyses of the results suggest that the component suffered from fatigue under reversed torsional loading, which initiated at pre-existing cracks in the component.     

Improvements to the SMO algorithm for SVM regression

This paper points out an important source of inefficiency in Smola and Scholkopf's (1998) sequential minimal optimization (SMO) algorithm for support vector machine regression that is caused by the use of a single threshold value. Using clues from the Karush-Kuhn-Tucker conditions for the dual problem, two threshold parameters are employed to derive modifications of SMO for regression. These modified algorithms perform significantly faster than the original SMO on the datasets tried.     

Design of optimum actively compensated networks

This paper describes an active compensation technique which is readily applicable to all active-RC networks, independently of the circuit topology. This procedure considers the network in its totality and derives general active compensation conditions for a generic active-RC network employing 2 or 3 operational amplifiers. Furthermore, the remaining degrees of freedom of the circuits are used to optimize the performance of the actively compensated circuits to further extend its operating frequency range. Simulation and experimental results are also reported.     

Rotational molding cycle time reduction through surface‐enhanced molds,Part B: Experimental study

Rotational molding is a process used to manufacture hollow plastic products, and has been heralded as a molding method with great potential. Because of long production cycle times, which are limited by the time required to heat‐up and cool‐down the mold and the product, its productivity is hampered. To address this issue, exterior mold modification techniques (i.e. the application of extended and rough surfaces) have been employed to enhance heat transfer to and from molds, ultimately reducing cycle times. Extended surfaces have the potential to enhance heat transfer by increasing the surface area. Roughness elements are utilized in conjunction with turbulent flows, also producing significant increases in heat transfer rates. Experimental results presented here demonstrate very significant cycle time reductions through the use of surface‐enhanced molds. The experimental savings are in the order of 18 and 28%, whereas the predicted cycle time reductions are around of 21 and 32% for roughness‐enhanced and pin‐enhanced molds, respectively. Although the prediction methods have been unable to forecast the exact experimental cycle times very accurately, they have proved to be useful for predicting the approximate cycle time reductions and the relative rankings of the plain and the surface‐enhanced molds. POLYM. ENG. SCI., 47:1420–1429, 2007. © 2007 Society of Plastics Engineers     

Tool wear prediction models during end milling of glass fibre-reinforced polymer composites

Composite products are often subjected to secondary machining processes as integral part of component manufacture. However, rapid tool wear becomes the limiting factor in maintaining consistent machining quality of the composite materials. Hence, this study demonstrates the development of an indirect approach in predicting and monitoring the wear on carbide tool during end milling using multiple regression analysis (MRA) and neuro-fuzzy modelling. Although the results have indicated that acceptable predictive capability can be well achieved using MRA, the application of neuro-fuzzy yields a significant improvement in the prediction accuracy. It is apparent that the accuracies are pronounced as a result of nonlinear membership function and hybrid learning algorithms. Using the developed models, a timely decision for tool re-conditioning or tool replacement can be achieved effectively.     

Cohesion: A concept and framework for confident association discovery with potential application in microarray mining

The minimal frequency constraint in classical association mining algorithms turns out to be a challenging bottleneck in discovery of large number of infrequent associations that can be potential in knowledge content. A lower choice for threshold frequency not only incurs huge cost of pattern explosion but also cuts reliability of discovered knowledge. The goal of the present paper is to devise a new framework addressing two necessities. The first is discovery of confident associations unconstrained to classical minimal frequency. The second is to ensure quality of the discovered rules. We propose a new property among items, terming it cohesion, and develop cohesion-based scalable algorithms for confident association discovery. In order to assess quality of rules in terms of knowledge content, we propose two new measures, accuracy and predictability based on documented associations. Experiments with market-basket data as well as microarray data establish superiority of cohesion-based technique both in terms of amount and quality of discovered knowledge.     

High gas dependence for power generation in Thailand: The vulnerability analysis

Thailand uses 74% of its natural gas supply for power generation and 70% of its power comes from gas-based technology. High dependence on natural gas in power generation raises concerns about security of electricity supply that could affect competitiveness of Thai manufacturing and other industries at the global level. The effect of fuel dependence on security of electricity supply has received less emphasis in the literature. Given this gap, this research examines the economic impact of high dependence on natural gas for power generation in Thailand by analyzing the effect of changes in fuel prices (including fuel oil and natural gas) on electricity tariff in Thailand. At the same time, the research quantifies the vulnerability of the Thai economy due to high gas dependence in power generation. Our research shows that for every 10% change in natural gas price, electricity tariff in Thailand would change by 3.5%. In addition, we found that the gas bill for power generation consumed between 1.94% and 3.05% of gross domestic product (GDP) between 2000 and 2004 and in terms of GDP share per unit of energy, gas dependence in power generation is almost similar to that of crude oil import dependence. We also found that the basic metal industry, being an electricity intensive industry, is the most affected industry. Additionally, we find that volatility of gas price is the main factor behind the vulnerability concern. The research accordingly simulates two mitigation options of the problem, namely reducing gas dependence and increasing efficiency of gas-fired power plants, where the results show that these methods can reduce the vulnerability of the country from high gas dependence in power generation.