The Effect of Particulate Reinforcement Addition to Latent Heat and Solid Fraction During Solidification of Titanium Carbide Particulates Reinforced Aluminium Alloy Matrix Composite Produced by Vortex Mixing-Sand Casting Technique

In this study the effect of particulate reinforcement addition to latent heat generation and solid fraction during solidification of metal matrix composite is investigated. Vortex mixing - sand casting technique is employed to produce the specimens. Solidification data during the casting process is acquired and studied using Fourier thermal analysis (FTA) to calculate the latent heat generation and solid fraction. In this study latent heat and fraction solid are obtained by performing calculations based on FTA. The results show that when volume fraction of particulate reinforcement is increased, the fraction solid rate is faster and the latent heat generation during solidification decreased. It is concluded that as more particulate reinforcement is added, it promotes faster solidification during the casting process

Production and solidification analysis of titanium carbide particulates reinforced aluminium alloy matrix composite by vortex mixing - sand casting technique

In this study, production and solidification analysis of metal matrix composite (MMC) consisting of titanium carbide particulates reinforced aluminium-11.8% silicon alloy matrix are done. A combination of vortex mixing - sand casting technique is used as the manufacturing method to produce the specimens. Thermal measurement during the casting process is captured and solidification graphs are plotted to represent the solidification characteristic. The result shows that as volume fraction of particulates reinforcement is increased, solidification time becomes faster. Particulates reinforcement promotes solidification which will support finer grain size of the casting specimen and in turn produce better mechanical property. Hardness test is performed and it confirms that hardness number increases as more particulates are added to the MMC system

Casting quality model for casted aluminium silicon carbide based on non-linear thermal expansion

This paper presents the design and development of a model for predicting the casting quality of aluminium silicon carbide based on the first-order shear deformation theory (FSDT) and thermal expansion behaviour. The coefficient of thermal expansion (CTEs) of casted aluminium silicon carbide fibre reinforced material was significantly influenced by the thermal stresses and interfaces between matrix and fibres. The thermal expansion behaviour of the casted aluminium silicon carbide fibre reinforced composite relies on the thermal expansion of the fibres, and influenced by the onset of interfacial strength and residual stress state. The validation shows a good agreement with surface roughness. In order to determine the performance of the model, the analysis of variance (ANOVA) was presented by using SPSS. The performance of casting quality model shows the correlation at the high level of accuracy 99.9% with confidence level of 95% between the experiment and the model

Casting technology: sustainable metal forming process

Metal casting is a process where molten metal is poured by gravity or injected with pressure into a mould cavity to produce the desired product. Most cast products are in finished goods form, which require minimal level of machining and surface finishing to achieve the desired tolerance and surface quality. Many industrial parts and components are produced by the method of casting, including engine blocks, crankshafts, automotive components, railroad equipment, plumbing fixtures, power tools, very large components for hydraulic turbines and so on. In terms of the theoretical application, two pertinent parameters, i.e. flow and thermal aspects, are explained in detail. The advancement from conventional to advanced materials has pushed casting technology into a competitive environment based on product requirements. Further, Computer Aided Design and Computer Aided Manufacturing (CAD/CAM) together with machine technology have also been introduced to the foundry or casting industry. This lecture will cover the development of Advanced Manufacturing Technology (AMT) applied in metal casting processes. Selected works on casting processes and technology for conventional and advanced materials are reviewed, and studies on metal matrix composites for engineering products are discussed. Other than process simulation technology, advances in mould and die design technology are also being applied in casting product development. With these technologies, the casting process will be maintained and sustained as an important and relevant component of the metal forming process

Mechanical Properties and Morphological Analysis of Copper Filled Aluminum Alloy Hybrid Matrix Composite

This paper presents the characterization of LM6 aluminum alloy with varying copper addition. LM6 is a soft, light-weight and corrosion resistant metal. Due to these characteristics, the material was selected to be added with copper to identify improved properties. The amount of copper addition was varied from 0%wt with intervals of 3%wt for every alloying run. Vibration casting, or vibration molding, was conducted. The vibration process is said to give a better result in terms of the alloy's grain size and arrangement. Mechanical testing and microstructure analysis were performed to prove the theory. Specimens with various amounts of copper were successfully produced and tested. The LM6 alloy specimen casted without copper and with vibration casting at 20 Hz had the highest tensile strength and percentage of elongation, while the LM6 alloy specimen casted with 9%wt of copper without mechanical vibration casting had the best mechanical properties based on the overall results and criteria. The percentage of copper addition that produced the optimum properties was found to be 9%wt of copper without vibration molding (hardness 46.2HRB, 125 MPa)

Rough Set Granularity in Mobile Web Pre-Caching

Mobile Web pre-caching (Web prefetching and caching) is an explication of performance enhancement and storage limitation ofmobile devices

Effect of cutting parameters on cutting temperature of TiAL6V4 alloy

A Finite Element Modeling (FEM) and Simulation was Used to Investigate the Effect of Tool Rake Angle, Cutting Speed and Feed Rate on the Cutting Temperature of Tial6v4 Alloy. the Purpose of this Study was to Find Proper Cutting Parameters for Machining of Titanium Alloy where Cutting Temperature was Lowest. A FEM Based on ABAQUS Software which Involves Jonson-Cook Material Model and Coulomb’s Friction Law was Applied to Simulate an Orthogonal Cutting Process. in this Simulation Work, a Range of Tool Rake Angle from 0° to 10°, a Range of Cutting Speed from 300 m/min to 600 m/min and a Range of Feed Rate between 0.1 Rev/mm and 0.25 Rev/mm were Investigated. the Simulation Results Indicated that Increase in Rake Angle Reduces Cutting Temperature while Increasing Cutting Speed and Feed Rate Increase the Cutting Temperature

Management system prototype for intelligent mobile cloud computing for big data

The current challenge of mobile devices is the storage capacity that has led service providers to develop new value-added mobile services. To address these limitations, mobile cloud computing, which offers on-demand is developed. Mobile Cloud Computing (MCC) is developed to augment device capabilities, facilitating to mobile users store, access to a big dataset on the cloud. Even so, given the limitations of bandwidth, latencies, and device battery life, new responses are required to extend the use of mobile devices. This paper presents a novel design and implementation of developing process on intelligent mobile cloud storage management system, also called as Intelligent Mobile Cloud Computing (IMCC) for android based users. IMCC is important for cloud storage user to make their data effectively and efficiently for saving the user time. IMCC provided convenience for user to use multiple cloud storage using one application and easy for users to store their data to any cloud storage. The result shows using IMCC it only took 8 seconds to access the data, which is faster compared with traditional MCC, it took 23.33 seconds. IMCC reduce 65.71% of latency occur using the MCC in managing a user data. The developed IMCC prototype is accessible through the Google Play Store

Tools in data science for better processing

Analysing the data is an important part of a research in data science. There are many tools that can be used in analysing a data set to get the experiment results for classification, clustering and others. However, the researchers are concerned about how to increase the efficiency in analysing a data set. In this paper, three open source tools which are the Waikato Environment for Knowledge Analysis (WEKA), Konstanz Information Miner (KNIME) and Salford Predictive Modular (SPM) were compared to identify the better processing tools in evaluating the presented data. All of these tools have their own different characteristics. WEKA can handle pre-processing of data and then analyses it based on different algorithms. It is suitable to be used for classification, regression, clustering, association rules, and visualisation. The algorithms can be applied directly to a data set or called from its own Java code. KNIME is more inclined towards producing graphical view, while SPM is a highly accurate and ultra-fast analytics which also data mines platforms for any sizes, complexity or organisation. The results illustrate the tools capability in analysing data sets and evaluators in an efficient and effective manner

Effect of cutting parameters on tool-chip interface temperature in an orthogonal turning process

The aim of this paper is to investigate the effect of cutting speed and uncut chip thickness on cutting performance. A Finite Element Method (FEM) based on the ABAQUS explicit software which involves Johnson-Cook material mode and Coulombs friction law was used to simulate of High Speed Machining (HSM) of AISI 1045 steel. In this simulation work, feed rate ranging from 0.05 mm/rev to 0.13 mm/rev and cutting speed ranging from 200 m/min to 600 m/min at three different cutting speeds were investigated. From the simulation results it was observed that increasing feed rate and cutting speed lead to increase temperature and stress distribution at tool/chip interface. The results obtained from this study are highly essential to predict machining induced residual stresses and thermo-mechanical deformation related properties on the machined surface