Abstract:
Nowadays, Squeeze casting is considered as a convenient process for developing quality piston components. In this paper, casting methods such as squeeze casting and die casting techniques are used for compare the tensile behavior of Al-Si piston in the view of casted and heat-treated aspects. K-Nearest Neighbour (KNN) algorithm is used for predicting the tensile fracture of the squeeze casted Al-Si alloy. The proposed method is implemented in the MATLAB platform, and the tensile fracture in casting is compared with the experimental and predicted value. The scanning electron microscope analyzes the microstructural property and fractures analysis of the material. The maximum ultimate tensile strength of the casted and heat-treated specimen is 184 MPa and 297 MPa. The results indicate the proposed approach is an efficient method than the implemented Artificial neural network for predicting the tensile fracture in Aluminium-Silicon alloy materials.
