Studies on grain refinement and alloying additions on the microstructure and mechanical properties of Mg-8Zn-4Al alloy

MAGNESIUM ALLOYS have strong potential for weight reduction in a wide range of technical applications because of their low density compared to other structural metallic materials. Therefore, an extensive growth of magnesium alloys usage in the automobile sector is expected in the coming years to enhance the fuel efficiency through mass reduction. The drawback associated with the use of commercially cheaper Mg-Al based alloys, such as AZ91, AM60 and AM50 are their inferior creep properties above 100ºC due to the presence of discontinuous Mg17A112 phases at the grain boundaries. Although rare earth-based magnesium alloys show better mechanical properties, it is not economically viable to use these alloys in auto industries. Recently, many new Mg-Al based alloy systems have been developed for high temperature applications, which do not contain the Mg17Al12 phase. It has been proved that the addition of a high percentage of zinc (which depends upon the percentage of Al) to binary Mg-Al alloys also ensures the complete removal of the Mg17Al12 phase and hence exhibits superior high temperature properties.ZA84 alloy is one such system, which has 8%Zn in it (Mg-8Zn-4Al-0.2Mn, all are in wt %) and shows superior creep resistance compared to AZ and AM series alloys. These alloys are mostly used in die casting industries. However, there are certain large and heavy components, made up of this alloy by sand castings that show lower mechanical properties because of their coarse microstructure. Moreover, further improvement in their high temperature behaviour through microstructural modification is also an essential task to make this alloy suitable for the replacement of high strength aluminium alloys used in automobile industry. Grain refinement is an effective way to improve the tensile behaviour of engineering alloys. In fact, grain refinement of Mg-Al based alloys is well documented in literature. However, there is no grain refiner commercially available in the market for Mg-Al alloys. It is also reported in the literature that the microstructure of AZ91 alloy is modified through the minor elemental additions such as Sb, Si, Sr, Ca, etc., which enhance its high temperature properties because of the formation of new stable intermetallics. The same strategy can be used with the ZA84 alloy system to improve its high temperature properties further without sacrificing the other properties. The primary objective of the present research work, “Studies on grain refinement and alloying additions on the microstructure and mechanical properties of Mg-8Zn-4Al alloy” is twofold: 1. To investigate the role of individual and combined additions of Sb and Ca on the microstructure and mechanical properties of ZA84 alloy. 2. To synthesis a novel Mg-1wt%Al4C3 master alloy for grain refinement of ZA84 alloy and investigate its effects on mechanical properties.

Mn2þ-induced room-temperature ferromagnetism and spin-glass behavior in hydrothermally grown Mn-doped ZnO nanorods

The magnetic properties of Mn-doped ZnO (ZnO:Mn) nanorods grown by hydrothermal process at a temperature of 200 8C and a growth time of 3 h have been studied. The samples were characterized by using powder X-ray diffraction with Rietveld refinement, scanning electron microscopy, energy-dispersive X-ray analysis and SQUID magnetometry. Mn (3 wt%) and (5 wt%)-doped ZnO samples exhibit paramagnetic and ferromagnetic behavior, respectively, at room temperature. The spin-glass behavior is observed from the samples with respect to the decrease of temperature. At 10 K, both samples exhibit a hysteresis loop with relatively low coercivity. The room-temperature ferromagnetism in 5 wt% Mn-doped ZnO nanorods is attributed to the increase in the specific area of grain boundaries, interaction between dopant Mn2þ ions substituted at Zn2þ site and the interaction between Mn2þ ions and Zn2þ ions from the ZnO host lattice

Photoluminescence Studies Of Microwave Assisted Synthesized Zno Micro Structures

ZnO micro particles in the range 0.4-0.6 μm were synthesized by microwave irradiation method. The XRD analysis reveals that the sample is in the wurtzite phase with orientation along the (101) plane. SAED pattern of the sample reveals the single crystalline nature of the micro grains. TEM images show the formation of cylindrical shaped ZnO micro structures with hexagonal faces. The optical phonon modes were slightly shifted in the Raman spectrum,attributed to the presence of various crystalline defects and laser induced local heating at the grain boundaries. A broad transmission profile was observed in the FTIR spectrum from 1550-3400 cm-1 which falls in the atmospheric transparency window region. PL spectrum centered at 500 nm with a broad band in the region 420-570 nm comprised of different emission peaks attributed to transition between defect levels. Various emission levels in the sample were expliained with a band diagram

Tunnel-Based Artificial Neural Network Technique to Calculate the Resonant Frequency of a Thick-Substrate Microstrip Antenna

The mathematical formulation of empirically developed formulas Jirr the calculation of the resonant frequency of a thick-substrate (h s 0.08151 A,,) microstrip antenna has been analyzed. With the use qt' tunnel-based artificial neural networks (ANNs), the resonant frequency of antennas with h satisfying the thick-substrate condition are calculated and compared with the existing experimental results and also with the simulation results obtained with the use of an IE3D software package. The artificial neural network results are in very good agreement with the experimental results

AN ARTIFICIAL NEURAL NETWORK FOR SONAR TARGET DETECTION

Neural Network has emerged as the topic of the day. The spectrum of its application is as wide as from ECG noise filtering to seismic data analysis and from elementary particle detection to electronic music composition. The focal point of the proposed work is an application of a massively parallel connectionist model network for detection of a sonar target. This task is segmented into: (i) generation of training patterns from sea noise that contains radiated noise of a target, for teaching the network;(ii) selection of suitable network topology and learning algorithm and (iii) training of the network and its subsequent testing where the network detects, in unknown patterns applied to it, the presence of the features it has already learned in. A three-layer perceptron using backpropagation learning is initially subjected to a recursive training with example patterns (derived from sea ambient noise with and without the radiated noise of a target). On every presentation, the error in the output of the network is propagated back and the weights and the bias associated with each neuron in the network are modified in proportion to this error measure. During this iterative process, the network converges and extracts the target features which get encoded into its generalized weights and biases.In every unknown pattern that the converged network subsequently confronts with, it searches for the features already learned and outputs an indication for their presence or absence. This capability for target detection is exhibited by the response of the network to various test patterns presented to it.Three network topologies are tried with two variants of backpropagation learning and a grading of the performance of each combination is subsequently made.

Nutritional responses in Indian white shrimp Fenneropenaeus indicus to varying protein: energy combinations in compounded artificial feeds

Department of Marine Biology, Microbiology and Biochemistry, Cochin University of Science and Technology

Identification of spectral lines of elements using artificial neural networks

Artificial neural networks (ANNs) are relatively new computational tools that have found extensive utilization in solving many complex real-world problems. This paper describes how an ANN can be used to identify the spectral lines of elements. The spectral lines of Cadmium (Cd), Calcium (Ca), Iron (Fe), Lithium (Li), Mercury (Hg), Potassium (K) and Strontium (Sr) in the visible range are chosen for the investigation. One of the unique features of this technique is that it uses the whole spectrum in the visible range instead of individual spectral lines. The spectrum of a sample taken with a spectrometer contains both original peaks and spurious peaks. It is a tedious task to identify these peaks to determine the elements present in the sample. ANNs capability of retrieving original data from noisy spectrum is also explored in this paper. The importance of the need of sufficient data for training ANNs to get accurate results is also emphasized. Two networks are examined: one trained in all spectral lines and other with the persistent lines only. The network trained in all spectral lines is found to be superior in analyzing the spectrum even in a noisy environment.

Grain refinement studies on Mg and Mg-Al based alloys

Demand on magnesium and its alloys is increased significantly in the automotive industry because of their great potential in reducing the weight of components, thus resulting in improvement in fuel efficiency of the vehicle. To date, most of Mg products have been fabricated by casting, especially, by die-casting because of its high productivity, suitable strength, acceptable quality & dimensional accuracy and the components produced through sand, gravity and low pressure die casting are small extent. In fact, higher solidification rate is possible only in high pressure die casting, which results in finer grain size. However, achieving high cooling rate in gravity casting using sand and permanent moulds is a difficult task, which ends with a coarser grain nature and exhibit poor mechanical properties, which is an important aspect of the performance in industrial applications. Grain refinement is technologically attractive because it generally does not adversely affect ductility and toughness, contrary to most other strengthening methods. Therefore formation of fine grain structure in these castings is crucial, in order to improve the mechanical properties of these cast components. Therefore, the present investigation is “GRAIN REFINEMENT STUDIES ON Mg AND Mg-Al BASED ALLOYS”. The primary objective of this present investigation is to study the effect of various grain refining inoculants (Al-4B, Al- 5TiB2 master alloys, Al4C3, Charcoal particles) on Pure Mg and Mg-Al alloys such as AZ31, AZ91 and study their grain refining mechanisms. The second objective of this work is to study the effect of superheating process on the grain size of AZ31, AZ91 Mg alloys with and without inoculants addition. In addition, to study the effect of grain refinement on the mechanical properties of Mg and Mg-Al alloys. The thesis is well organized with seven chapters and the details of the studies are given below in detail.

Document Image Segmentation and Compression using Artificial Neural Networks and Evolutionary Methods

The work is intended to study the following important aspects of document image processing and develop new methods. (1) Segmentation ofdocument images using adaptive interval valued neuro-fuzzy method. (2) Improving the segmentation procedure using Simulated Annealing technique. (3) Development of optimized compression algorithms using Genetic Algorithm and parallel Genetic Algorithm (4) Feature extraction of document images (5) Development of IV fuzzy rules. This work also helps for feature extraction and foreground and background identification. The proposed work incorporates Evolutionary and hybrid methods for segmentation and compression of document images. A study of different neural networks used in image processing, the study of developments in the area of fuzzy logic etc is carried out in this work

Artificial neural network modeling for surface roughness prediction in cylindrical grinding of Al‐SiCp metal matrix composites and ANOVA analysis

Metal matrix composites (MMC) having aluminium (Al) in the matrix phase and silicon carbide particles (SiCp) in reinforcement phase, ie Al‐SiCp type MMC, have gained popularity in the re‐cent past. In this competitive age, manufacturing industries strive to produce superior quality products at reasonable price. This is possible by achieving higher productivity while performing machining at optimum combinations of process variables. The low weight and high strength MMC are found suitable for variety of components