Heat extrusion processing ANN optimization and microstructure of spray forming TiCP/ZA35 composites

Abstract

The effects of heat extrusion processing of spray forming TiCp/ZA35 composites on extrusion ratio, extrusion specific pressure, extrusion temperature and extrusion rate had been studied by artificial neural network (ANN). The artificial neural network model was created for heat extrusion processing. The input parameters of the ANN model were extrusion ratio, extrusion specific pressure, extrusion temperature and extrusion rate. The output of the ANN model was ultimate tensile strength. The model can be used for the prediction of properties of spray forming TiCp/ZA35 composites as functions of processing parameters. It can also be used for the optimization of the processing parameters. The ANN results are in good agreement with experimental phenomena, the biggest relative error and coincidence rate is less than 1.8% and 0.986. The optimized heat extrusion ratio, extrusion specific pressure, extrusion temperature and extrusion rate are 22415 MPa, 315 ℃ and 8 mm·s−1 respectively, and the tensile strength of spray forming TiCp/ZA35 composites is 486.7 MPa. The reinforcement phase MnAl6 whisker or particle is precipitated in the grains due to the indirect aging treatment of composites by hot extrusion. Dispersion strengthen and dislocation strengthen contribute a combination factor to increase the room temperature mechanical properties of the hot extruded TiCp/ZA35 composites, which is 38.3% higher than that of TiCp/ZA35 composites without heat extrusion.

Keywords

• spray forming ticp/za35 composites
• heat extrusion
• artificial neural network(ann)
• optimization
• strengthen mechanism