Band 80, Heft 6

This publication analyzes the influence of cooling rate on the solid solution state during and after die casting of the AlSi9Cu3 alloy and, based on simulation and experimental results, predicts the R p0.2 yield strength as a function of cooling rate. The subject of the investigation is a four-cylinder in-line engine cast using the laminar die casting process. The implementation of a thermocouple in the casting mold enables in-situ measurement of the temperature-time behavior during the casting process, from filling the mold to demolding the component. Based on this measurement, the heat transfer coefficient between the casting mold and the melt is validated in the Magmasoft® casting simulation. The temperature-time behavior after demolding is reproduced using a specific test setup. The temperature measurements taken enable the existing casting simulation in Magmasoft® to be extended to the quenching process outside the casting mold. The correlation of the results of the tensile tests from the crankcase with the determined cooling rates enables the prediction of the R p0.2 yield strength as a function of the temperature-time behavior for the application of laminar die casting.

By influencing the properties of the surface and subsurface through deep rolling, the fatigue life of components can be significantly increased. However, this is largely dependent on the temperatures that occur during the process. To gain a deeper understanding of the thermal influence during hard turning, it is necessary to analyze the thermal influence decoupled from the machining process. For this reason, a method has been developed that allows the inductive heating of components to specifically study the influence of deep rolling at different temperatures. Based on simulations, the inductive heating of bearing rings was designed and implemented in a machine tool. The method allows targeted local and time-defined heating through knowledge of the coupling power and temperature control during inductive heating in a machine tool. Surface temperatures ranging from T O = 100 °C to 400 °C can be set.

Energy costs are the biggest expense factor in industrial heat treatment. Increasing efficiency and saving energy are therefore the focus of the further development of heat treatment plants. The increasing share of renewable energy in the power grid offers the opportunity to use cheap electricity with low CO 2 emissions. The fluctuating availability of renewable energy must be countered by intelligent process planning. To make this possible, numerical models are presented that can be used to forecast the power requirements of heat treatment processes. In particular, the modeling of thermal interactions within the heating chamber is explained. Targeted model simplifications enable short calculation times and practice-oriented parameterization. The results are validated using real process data. It is shown how simulation can be used in process planning to reduce emissions and costs and to make heat treatment processes more sustainable.