A Study of the Optimum Reheating Process for A356 Alloy in Semi-Solid Forging

Semi-solid forging (SSF) is compared with conventional casting such as gravity die-casting and squeeze casting. A product without inner defects can be obtained from semi-solid forming with a globular microstructure. Generally speaking, SSF is composed of reheating, forging and ejecting processes. In the reheating process, the materials are heated up to the temperature between the solidus and liquidus line at which the materials exists in the form of liquid–solid mixture. The process variables such as reheating time, reheating temperature, reheating holding time, and induction heating power have much effect on the quality of the reheated billets. It is difficult to consider all the variables at the same time to predict the quality. In this paper, Taguchi method, regression analysis, and neural network were applied to analyse the relationship between processing conditions and solid fractions. A356 alloy was used, and the learning data were extracted by the reheating experiments. The results of a neural network were in good agreement with experimental results. Polynominal regression analysis was formulated by using the test data from the neural network. An optimum processing condition was derived to minimise the grain size, solid fraction standard deviation, and to maximise the average specimen temperature. Consideration is given to the reheating process of the raw material and results are presented of appropriate process variables for correct solid fraction, specimen temperature, and grain size.