Abstract
In this paper, the effect of different alloying elements on the ultimate tensile strength of Al-Mg2Si composites is theoretically studied. The feed forward back propagation neural network with sigmoid function is used. The extensive experimental results taken from literature are modeled and mathematical formula is presented in explicit form. In addition, it is observed that magnesium and copper have a stronger effect on the ultimate tensile strength of Al-Mg2Si composites comparison to other alloying elements. The proposed model shows good agreement with test results and can be used to find the ultimate tensile strength of Al-Mg2Si composites.
Highlights
In the production of composite materials, aluminum (Al), magnesium (Mg), titanium (Ti) and nickel (Ni) alloys are commonly used as metal matrix
This work proposes an approach for ultimate tensile strength in prediction of Al-Mg2Si composites containing different alloying elements
The back propagation NNs are used for the training process and the proposed NN model shows good agreement with experimental results
Summary
In the production of composite materials, aluminum (Al), magnesium (Mg), titanium (Ti) and nickel (Ni) alloys are commonly used as metal matrix. Al and its alloys are the most commonly used matrix material in the production of metal matrix composites (MMCs). The composites are manufactured with the diffusion bonding, power metallurgy and casting ( known as liquid metal infiltration) processes [1]. MMCs are widely used in various industries, especially in the automotive, energy and aerospace applications, as they have excellent mechanical properties. The need to reduce emissions while enhancing performance has driven manufacturers to use more Al in industry. This effort has been accompanied by the development of new Al alloys tailored for these applications
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