High gradient magnetic separation of china clays

High gradient magnetic separation has proved to be a successful method for removing micron size colouring bodies from china clays. Clays from Chotanagpur, Bihar have been beneficiated to improve its brightness. The effect of field, flow rate and dispersing agents has been studied on both the run of mine (rom) clay and the washed clay. Significant improvement in the brightness of clay has been obtained.     

Photoconducting a-Si:H films grown at high deposition rates by pulsing a VHF 100 MHz discharge

In a novel experiment hydrogenated amorphous silicon films were deposited by modulating the very high frequency (VHF) (100 MHz) discharges, at low frequency (2 Hz) with a nonzero low power level, using pure as well as 25% hydrogen and 25% helium diluted silane as the source gases. During these studies deposition rate is found to depend on the dwell time as in the case of RF pulsed plasma CVD reported earlier by the authors. The films were characterised for optical bandgap, dark and photoconductivity, hydrogen content, microstructure factor, Urbach energy and defect density. The results indicate that, unlike the RF pulsed plasma case, there is an order of magnitude improvement in the photoconductivity of the material due to pulsing the VHF discharges. Urbach energy and defect density studies also indicate an improvement in the film quality. The improvements are more pronounced in diluted silane deposited films. Controlled ion bombardment (of high flux and lower energy) and the resulting ion bombardment induced preparation of the growth surface in the VHF discharges are believed to be the main factors contributing to the observed results. Thus, a more favourable sheath characteristics as obtained during pulsed VHF discharge conditions over RF (13.56 MHz). Silane discharges holds the key to obtain high growth rate deposition of a-Si:H films of acceptable opto-electronic quality     

Src-like adaptor protein (Slap) is a negative regulator of mitogenesis

The Src-like adaptor protein (Slap) is a recently identified adaptor protein containing Src homology 3 (SH3) and SH2 domains. Slap is found in a wide range of cell types and was shown to interact with the Eck receptor tyrosine kinase in a yeast two-hybrid interaction screen [1]. Here, we found that Slap is expressed in NIH3T3 cells and could associate with the activated platelet-derived growth factor (PDGF) receptor. Using mutated versions of the PDGF receptor and phosphopeptide competition experiments, we determined that Slap has the highest affinity for the Src-binding site of the PDGF receptor. Our inability to produce cell lines that stably expressed Slap suggested that Slap inhibited cell growth. We further investigated this issue by transiently expressing Slap by microinjection. Overexpression of Slap by this method inhibited DNA synthesis induced by PDGF and serum, whereas overexpression of the adaptor proteins Grb2 and Shc did not. Finally, microinjection of a Slap antibody into NIH3T3 cells that had been stimulated with suboptimal doses of growth factors potentiated the effects of the growth factors. These data suggest that, unlike other adaptor proteins, Slap is a negative regulator of signalling initiated by growth factors.     

Abrasive Wear Characteristics of Silicon Carbide Particle Reinforced Zinc Based Composite

Herein, abrasive wear characteristics of SiCp dispersed zinc-aluminum based composites have been analyzed under high-stress condition. The wear tests were conducted on a Pin-on-Disc machine at a constant linear velocity of 1.57 m/s in the applied load range of 1-7 N while the abrasive platform used is 600 grit emery paper. A matrix alloy was also characterized under identical conditions to assess the influence of the dispersoid (SiC) particle on the wear behaviour. Wear rate, frictional heating and friction coefficient are the focused parameters of the study. The base alloy used has a dendrite structure comprising of α-dendrites surrounded by an α + η eutectoid and metastable ε phase in interdendritic regions. The composite shows similar features to those of the base alloy except the additional presence of the reinforcing SiC particles. The wear rate and friction coefficient decrease with increase in abrading distance while a reverse trend was observed in the case of frictional heating which gradually increases with the increase in abrading distance. Incorporation of SiC particles improves the wear resistance of the matrix alloy and increasing the percentage of SiC increases the frictional heating and reduces the friction coefficient of the test material. The wear mechanism has been understood through SEM examination of wear surface, subsurface, debris particles and degraded abrasive grit papers.     

Effect of Powder Metallurgy Process and its Parameters on the Mechanical and Electrical Properties of Copper-Based Materials: Literature Review

Powder Metallurgy and Metal Ceramics - As the world is shifting towards renewable sources of energy, the demand for copper is increasing due to its excellent electrical and corrosion resistance...     

An economic and reliable tool life estimation procedure for turning

The conventional methods of tool life estimation take a long time and consume a lot of work piece material. In this paper, a quicker method for the estimation of tool life is proposed, which requires less consumption of work piece material and tools. In this method the tool life is estimated by fitting a best-fit line on the data falling in the steady wear zone and finding the time till tool failure by extrapolation. Neural networks are used to predict lower, upper and most likely estimates of the tool life. Comparison between neural networks and multiple regression shows the superiority of the former. The paper also proposes a methodology for continuous monitoring of tool use in the shop floor and updating/obtaining the tool life estimates based on the shop floor feed back.     

A neural-network-based methodology for the prediction of surface roughness in a turning process

A neural-network-based methodology is proposed for predicting the surface roughness in a turning process by taking the acceleration of the radial vibration of the tool holder as feedback. Upper, most likely and lower estimates of the surface roughness are predicted by this method using very few experimental data for training and testing the network. The network model is trained using the back-propagation algorithm. The learning rate, the number of neurons in the hidden layer, the error goal, as well as the training and the testing dataset size, are found automatically in an adaptive manner. Since the training and testing data are collected from experiments, a data filtration scheme is employed to remove faulty data. The validation of the methodology is carried out for dry and wet turning of steel using high speed steel and carbide tools. It is observed that the present methodology is able to make accurate prediction of surface roughness by utilising small sized training and testing datasets.     

A neural network based methodology for the prediction of roll force and roll torque in fuzzy form for cold flat rolling process

Neural network models can be effectively used to predict any type of functional relationship. In this paper, a neural network model is used to predict roll force and roll torque in a cold flat rolling process, as a function of various process parameters. A strategy is developed to obtain a prescribed accuracy of prediction with a minimum number of data for training and testing. The effect of increasing the size of training and testing data set is also examined. After the prediction of most likely value, upper and lower bound estimates are also found with the help of the neural network. With these estimates, the predicted value can be represented as a fuzzy number for use in fuzzy-logic based systems.