Characterization of Zro2 Reinforced Al-12.5si Alloy Matrix Composite Fabricated Through Spray Forming Technique

Abstract

Aluminium and its alloys possess high stiffness, lightweight and high strength. They can provide solutions to optimize strength-weight ratio in aerospace and automobile industries. Among several methods to fabricate Aluminium metal matrix composites, spray deposition method is used for large-scale production due to its high deposition efficiency. In the first phase, stir cast processing route was employed to prepare the composite. Taguchi experimental plan with a set of parameters such as stir speed, stir time, ZrO2 % reinforcement, and casting temperature were studied to know their influence on the composite properties. Super ranking concept was adopted to optimize the key process parameters of stir casting. It has resulted in 25.02% and 5.64% increase in ultimate tensile strength and hardness, respectively, whereas the reduction in wear loss of composites was 37.68% compared to initial stir casting conditions. The hot-pressing technique was applied to the composites prepared according to optimized stir casting conditions. The hot-pressing parameters (pressure, temperature, and dwell time) were analysed to know the process insights on composite properties. The pressure is the most dominating factor followed by temperature on all the properties of composites. The optimal hot-pressing conditions were again obtained by Super ranking concept. The usage of these optimal conditions showed a 39.3% reduction in wear loss, 11.54% and 4.88% increase in ultimate tensile strength and hardness values, respectively, compared to initial hot-pressing condition. The comparison of properties exhibited by samples fabricated by initial, and optimal conditions of stir casting and hot-pressing technique have resulted in excellent enhancement of properties which was strongly justified with the analysis of resulted microstructures and worn surface morphologies. In second phase, the metal matrix composites were fabricated using 99.9 wt. % aluminium and silicon (the wt % 12.5Si ) is reinforced with the (5wt%, 10 wt% and 15wt%) ZrO2 powder particles by stir casting and spray deposition method. Mechanical properties, micro hardness and evolution of microstructure of AlSi alloy with three different wt. % of zirconium oxide as particulate reinforcement were studied. The microstructural results indicate that the rich interface among the metal matrix and AlSi-ZrO2 particles and depicts the agglomeration of reinforced phase resulting to poor wettability of ZrO2 and observed decohesion. The mechanical testing results indicate that the tensile strength increases with the percentage of ZrO2. Moreover, as cast composites exhibit reverse tendency in compressive and hardness values. The highest compressive values for as cast and hot-pressed composites xi` were 380 MPa and 337 MPa for the addition of 10% ZrO2. The highest tensile strength of 191.83 MPa was obtained for 5% ZrO2 as cast composite and 164 MPa for 15% ZrO2 hot pressed composite. It is to note that as cast composite method represented more homogenous data compared to the hot-pressed composites. Hot pressed samples exhibited the reduction in the porosity compared to the as cast. In the third phase, research aims to study the effect of flight distance as a potential key factor that changes the optimum percentage of AlSi-ZrO2 in terms of mechanical and microstructural properties. The alloy is sprayed at varying the flight distance from 320 mm to 480 mm. The alloys were prepared by spray deposition technique and effects on microstructural properties were investigated. The AlSi-ZrO2 alloy was subjected to hot isostatic pressing for reducing the porosity of the deposit from 14.4% to 8.2%. Series of experimental study were carried out in the laboratory by varying the flight distance from 320 mm to 480 mm for AlSi-ZrO2 alloy to characteristic loading. In this paper, an optimized artificial neural network using genetic algorithm are developed to predict the mechanical behaviour for AlSi-ZrO2 composites. Based on the experimental data, the ANN models were developed, trained and tested. The microstructure of the AlSi-ZrO2 alloy consisted of finely divided globular shaped eutectic Si uniformly distributed in the Al matrix. With addition of ZrO2 composition to AlSi alloy, the tensile strength and micro hardness increased from 123 MPa to 147 MPa and 48 HV to 72 HV. The preferred flight distance for the current study is found to be 420 mm. Microstructural images obtained at flight distance consist of co-existing primary Si phase and needle like eutectic Si. The physical properties, such as tensile strength, compressive strength, yield strength, micro hardness and porosity of sprayed AlSi-ZrO2 can hence be adjusted by setting the optimized flight distance. The developed ANN-GA method proved to be accurate, reduced time and efficient to predict the numerous samples, and it will help materials designers to design their future experiments effectively. In fourth phase, the wear behaviour and microstructural characterization of aluminium silicon alloy with a reinforced ZrO2 composite material with respect to various flight distances are investigated. The amounts of ZrO2 (5, 10 & 15 wt %) were added to Al-12.5Si alloy. The microstructural characterization of the developed composites was analysed using Scanning Electron Microscope (SEM) and Energy Dispersive Spectrum (EDS). The effect of flight distance (320 to 480 mm), applied load (30 to 50 N) and the influence of reinforced ZrO 2 (5, 10 and 15 wt %) were investigated using the design of experiment (5x3x5 mm). The findings of the study reveals that there is a remarkable improvement in wear behaviour when xii` surfactant functionalized MWCNT-in-oil is used. Finally, a wear map of the underlying wear mechanisms is also presented. This investigation showed that wear resistance of the developed Al-12.5Si alloy can be improved by the effect of optimized effect of flight distance and wt% of ZrO2. In addition, ANN-GA model were developed to predict the wear behaviour of Al-12.5Si with reinforced material of ZrO2, applied load and the effect of flight distance as inputs. The estimated values were compared with experimental tests and the results showed that a high degree of association (Correlation coefficient, R ranging from 0.91 to 0.96). Therefore, the develop model can be used to predict the behaviour of wear within the range of tests performed. Another important outcome of this research is the development of prediction model using ANN and genetic algorithm (GA) to assist in validation. This method is a combination of two soft-computing methods of ANN and GA. GA logic helps in the transformation of the human knowledge and the ANN helps in the learning process and reduces the rate of errors in the determination of rules in ANN logic.