Solidification and Strength Behavior of A356 Al Alloy Wheels.

Alloy wheels must meet rigorous durability and safety tests to meet structural demand during use. The use of aluminum (Al) alloyed with silicon (Si) stands out due to the good relationship between density and strength and also its esthetic appeal, which is made possible by the suitable alloy machinability. The use of A356 alloy processed through low-pressure die casting and T6 heat treatment comes from the need for suitable mechanical properties and ease of large-scale cyclic processing. The wheel sector's high competitiveness demands accelerated development with less time-to-market and better performance. The use of numerical cast simulations helps predict the thermal and microstructural parameters of the alloy resulting from the solidification process inside a metallic mold. Understanding the solidification thermal parameters is of great importance for predicting either the phase morphologies or the dendritic length-scale of the casting, since these features have a direct impact on the alloy's strength. The present study aims to develop a computational simulation tool using commercial casting software to predict the tensile properties of as-cast and heat-treated automotive wheels. To achieve this objective, directional solidification (DS) experiments with the A356 alloy were carried out in order to determine equations relating the solidification time and cooling rate along the casting. The generated casting by the DS was further divided into parts, heat-treated and untreated, and each half part was examined for both tensile testing and the determination of secondary dendrite arm spacing (λ2). This allowed the development of key experimental equations relating λ2 with solidification time and tensile properties with λ2. In the end, a coupled simulation tool underwent testing using real tensile test data from wheels before and after T6 treatment. This testing involved two methods of correlating tensile properties with λ2: Hall-Petch-type (HP) and Ludvig model approaches. The HP model more accurately represents the strength data from various wheel regions, with a small average error of approximately 5.6% for ultimate strength. [ABSTRACT FROM AUTHOR]