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.     

Integral method of analysis of fischer-tropsch synthesis reactions in a catalyst pellet

A generalized integral method is developed to analyze complex reactions in a catalyst pellet. This method is valid for any kinetics and takes into account both internal and external heat and mass transfer effects. The integral equations are solved for a Fischer-Tropsch kinetic model to obtain effectiveness factors. Isothermal multiplicities are observed for low values of the surface coverage parameter α(α = 1/K₁p_co,0), and low values of the parameter σ₂₁ (ratio of Thiele moduli for H₂ and CO). The effectiveness factor is mildly sensitive to the external resistances.     

Real-time adaptation of decision thresholds in sensor networks for detection of moving targets

This paper addresses real-time decision-making associated with acoustic measurements for online surveillance of undersea targets moving over a deployed sensor network. The underlying algorithm is built upon the principles of symbolic dynamic filtering for feature extraction and formal language theory for decision-making, where the decision threshold for target detection is estimated based on time series data collected from an ensemble of passive sonar sensors that cover the anticipated tracks of moving targets. Adaptation of the decision thresholds to the real-time sensor data is optimal in the sense of weighted linear least squares. The algorithm has been validated on a simulated sensor-network test-bed with time series data from an ensemble of target tracks.     

TEMPERATURE DISTRIBUTION DURING ELECTRO-DISCHARGE ABRASIVE GRINDING

The objective of this work is to develop a finite element method (FEM) based mathematical model to simulate the hybrid machining process of grinding and electric discharge machining (EDM), named as Electro-discharge abrasive grinding (EDAG), for temperature distribution in the workpiece. Two different finite element codes have been developed to calculate the temperature distribution due to grinding heat source and EDM heat source separately. The transient temperature field within workpiece due to cut-off grinding is determined due to moving rectangular heat source. Gaussian heat distribution of power within a spark has been considered in the calculation of temperature distribution due to EDM. Temperature distribution in the workpiece due to combined process is obtained by using superposition. The simulation shows a sudden rise in temperature at the spark location. The predicted results can be used for calculation of thermal stresses, which play a major role as far as high-quality product requirements are concerned.     

Modeling and simulation of magnetic abrasive finishing process

Magnetic abrasive finishing (MAF) is one of the advanced finishing processes, which produces a high level of surface quality and is primarily controlled by a magnetic field. In MAF, the workpiece is kept between the two poles of a magnet. The working gap between the workpiece and the magnet is filled with magnetic abrasive particles. A magnetic abrasive flexible brush (MAFB) is formed, acting as a multipoint cutting tool, due to the effect of the magnetic field in the working gap. This paper deals with the theoretical investigations of the MAF process. A finite element model of the process is developed to evaluate the distribution of magnetic forces on the workpiece surface. The MAF process removes a very small amount of material by indentation and rotation of magnetic abrasive particles in the circular tracks. A theoretical model for material removal and surface roughness is also proposed accounting for microcutting by considering a uniform surface profile without statistical distribution. Numerical experiments are carried out by providing different routes of intermittent motion to the tool. The simulation results are verified by comparing them with the experimental results available in the literature.