Necat Altinkok

Processing of Al2O3/SiC ceramic cake preforms and their liquid al metal infiltration

Abstract In order to prepare ceramic preforms, chemical processes were used rather than using mixing of ceramic powders to obtain porous Al 2 O 3 /SiC ceramic foams. A slurry was prepared by mixing aluminium sulphate and ammonium sulphate in the water, and silicon carbide powder was added into the slurry so that a uniform mixture of Al 2 O 3 /SiC cake could be produced. The resulting product was (NH 4 ) 2 SO 4 ·Al 2 (SO 4 ) 3 ·24H 2 O plus silicon carbide particles (SiC p ) after dissolving chemicals in the water. This product was heated up in a ceramic crucible in the furnace. With the effect of heat it foamed and Al 2 O 3 /SiC cake was obtained. Resulting Al 2 O 3 grains were arranged in a 3D honeycomb structure and the SiC particles were surrounded by the alumina grains. Consequently, homogeneous powder mixing and porosity distribution were obtained within the cake. The morphology of the powder connections was networking with flake like particles. These alumina particles resulted in large amounts of porosity which was desired for ceramic preforms to allow liquid metal flow during infiltration. The resulting high porous ceramic cake (preform) was placed in a sealed die and liquid aluminium was infiltrated by Ar pressure. The infiltration was achieved successfully and microstructures of the composites were examined.

Use of Artificial Neural Network for Prediction of Mechanical Properties of α-Al2O3 Particulate-reinforced Al–Si10Mg Alloy Composites Prepared by using Stir Casting Process

In this article, the tensile strength, hardening behavior, and density properties of different α-Al2O3 particle size (μm)-reinforced metal matrix composites (MMCs), produced by using stir casting process, are predicted by designing a backpropagation (BP) neural network that used gradient-descent learning algorithm. Artificial neural network (ANN) is an intelligent technique that can solve nonlinear problems by learning from the samples. Therefore, some experimental samples are prepared at first to train the ANN to provide (to estimate) tensile strength, hardening behavior, and density properties of the MMCs produced for any given α-Al2O3 particle size (μm). The most important point is that after the ANN has been trained using some experimental samples, it gives approximately correct outputs for some of the experimental inputs that have not been used in the training. First, to prepare the training and test (checking) set of the network, some results are experimentally obtained and recorded in a file on a computer. In the experiments, α-Al2O3 particles are supplied commercially. α-Al2O3 ceramic powder of a varying particle size of 10 vol% is prepared, and then this ceramic powder with different α-Al2O3 particle sizes is added to Al–Si10Mg alloy in melt condition by stir casting process. The effect of reinforced particle size on the tensile strength, hardness resistance, and density properties of α-Al2O3-reinforced MMCs have been investigated. Mechanical tests reveal that tensile strength and hardness resistance of the α-Al2O3 ceramic powder composites decrease with increasing reinforced α-Al2O3 particle size. Then, neural network is trained using the prepared training set, also known as the learning set. In the preparation of the ANN training module, the aim of the use of the model is to predict the tensile strength, hardening behavior, and density properties for any given α-Al2O3 particle size by using some experimental results. Different α-Al2O3 particle sizes (μm) are used as the input, and tensile strength, hardening behavior, and density properties are used as outputs in the neural network training module. The tensile strength, hardening behavior, and density properties of the produced MMCs are estimated for different α-Al2O3 particle sizes using neural network efficiently instead of time-consuming experimental processes. At the end of the training process, the test data are used to check the system accuracy. Simulation results confirm the feasibility of this approach and show a good agreement with experimental results for a wide range of MMCs produced.

Microstructure and tensile strength properties of aluminium alloys composites produced by pressure-assisted aluminium infiltration of Al2O3/SiC preforms

In this study, a process for the production of aluminium alloy metal– matrix composites (MMCs) by the liquid metal infiltration route was investigated. This process was based on Al2O3/SiC ceramic cake in different SiC that was obtained by chemical process rather than mixing of ceramic powders to obtain porous Al2O3/SiC ceramic foam. This product was heated up in ceramic crucible in the furnace. It was foamed by the effect of heat and as a result Al2O3/SiC ceramic cake was produced. Resulting Al2O3 grains had 3D honeycomb structure and SiC particles were in the alumina grains. Consequently, homogeneous powder mix and porosity were obtained within the cake. The morphology of the powder connections was networking with flaky particles. These flaky alumina particles provided huge amount of porosity, which was desired for ceramic preforms to allow liquid metal flow during infiltration. Resulting high porous ceramic cake (preform) was placed in a sealed die and liquid aluminium was infiltrated by Ar pressure. The obtained structure indicated that alumina and SiC particles were uniformly distributed with the Al-matrix. In addition, the flaky alumina particles were bonded chemically onto the surfaces during foaming process. The infiltration was achieved successfully; microstructure and tensile strength properties of the composites were examined.

The Wear Behaviour of Dual Ceramic Particles (Al2O3/SiC) Reinforced Aluminium Matrix Composites

Al2O3/SiC powder mix was prepared by reacting of aqueous solution of aluminium sulphate, ammonium sulphate and water containing SiC particles at 1200°C. 10 wt% of this dual ceramic powder with different sized SiC particles was added to a liquid matrix alloy during mechanical stirring between solidus and liqudus under inert conditions. The wear behaviour of the dual ceramic reinforced aluminium matrix composites was investigated using pin-on-disc test at room temperature under dry conditions. It was found that dual and bimodal particle reinforcement decreased wear loss especially when SiC powder with larger grain size was used. Microstructural examination showed that as well as coarse SiC particle reinforcement, a fine alumina particle reinforcement phase was observed within the aluminium matrix (A332). The improvement in wear resistance of the dual ceramic reinforced aluminium matrix composites (AlMCs) could be attributed to the ability of the larger SiC particles to carry a greater portion of the applied load, as well as to the function of the larger SiC particles in protecting the smaller alumina particles from being gouged out during the wear process. Furthermore, the incorporation of dual and bimodal particles increased hardness of the composites with respect to the composite with fully small sized particles.

Optimization of Mechanical Properties of Hybrid Al2O3/SiCp Reinforced Composites Produced by Pressure-Assisted Aluminum Infiltration

This study investigates Al2O3/SiCp-Al composites fabricated by pressure-assisted liquid–metal infiltration techniques and modified alumina/SiC particle preforms. The infiltration was achieved successfully; microstructure and mechanical properties of the composites were examined. An experimental design based on the Taguchi technique was used to acquire the data and investigate the mechanical properties of the hybrid composites. An orthogonal array and analysis of variance were employed to investigate the influence of test parameters such as infiltration temperature and infiltration pressure. It was found that the infiltration temperature was the most effective factor in increasing the mechanical properties of hybrid Al2O3/SiCp composite.

Compressive Behavior of Al2O3—SiC Ceramic Composite Foams Fabricated by Decomposition of Aluminum Sulfate Aqueous Solution

In recent years, a new class of ceramic foams with porosity levels up to 95% has been produced by the chemical method with aeration of a suspension containing foaming agents. The method of foamed suspensions has originated from the chemical reactions of water-soluble salts. In this study, sintered cellular ceramic composites with varying degrees of reticulation are prepared using coarse and fine silicon carbide (SiC) powder. A new process has been developed in order to produce SiC particles containing alumina (Al2 O3) foam by chemical route. An aqueous solution of aluminum sulfate and ammonium sulfate plus SiC particles is fired to prepare ceramic foams up to 1200°C in a ceramic crucible. During heating, the viscous suspension is foamed, sulfate ions are volatilized, and an Al 2O3—SiC composite cake is obtained. The resulting Al2O3 are in networking morphology with flaky struts whereas SiC particles are encapsulated in the flaky alumina. These 3D connections of the struts are responsible for macropores and Al2O3 and SiC grains within the struts are responsible for micropores. Thus, a ceramic composite foam containing bimodal pore distribution is obtained. After sintering at 1550°C, compression tests are applied for the ceramic composite foams. High compressive strength is achieved after post sintering. Both sintering temperature and addition of SiC particles increase the compressive strength of the foam. Both SEM image and XRD analyses are carried out to examine composite microstructures and properties.

Determination of optimum particle size of Al2O3/SiCp reinforced hybrid composites materials in wear testing

Purpose – The purpose of this study is to optimize input parameters of particle size and applied load to determine minimum weight loss and friction coefficient for Al2O3/SiC particles-reinforced hybrid composites by using Taguchi’s design methodology. Design/methodology/approach – The experimental results demonstrate that the applied size is the major parameter influencing the weight loss for all samples, followed by particle size. The applied load, however, was found to have a negligible effect on the friction coefficient. Moreover, the optimal combination of the testing parameters was predicted. The predicted weight loss and friction coefficient for all the test samples were found to lie close to those of the experimentally observed ones. Findings – The optimum levels of the control factors to obtain better weight loss and friction coefficient were A8 (particle size, 60 μm) and B1 (applied load, 20 N), respectively. Taguchi’s orthogonal design was developed to predict the quality characteristics (weight...