Computer simulations are increasingly being used in the automotive industry to evaluate the state of stress in cylinder blocks during casting and heat treat processes. With recent advancements, it is now possible to model casting and quenching processes as well as residual stress and high cycle fatigue. However, calculating the final stress in cylinder blocks requires the integration of several software tools with different meshing topologies, numerical methods, data structures, and post-processing capabilities. The intent of this research is to develop an integrated virtual engineering environment that combines casting simulation, computational fluid dynamics, and finite element methods in order to simulate the manufacturing process from the beginning of casting, through water quenching heat treatment, to engine dynamometer testing. The computational environment is built on three CAE tools, Magmasoft, AVL Fire, and Abaqus, and required considerable amounts of research and development to validate each numerical method and the tools that facilitate data exchange between them.

The cooling history of carburized heat-treated gears plays a significant role in developing microstructure, hardness, and residual stress in the tooth that influences the fatigue performance of the gear. Evaluating gear carburizing heat treatment should include a microstructure and hardened depth evaluation. This can be done on an actual part or with a test piece. The best practice for a test piece is to use a section size that closely approximates the cooling rate at the gear flank of the actual gear. This study furthers work already presented showing the correct test piece size that should be used for different gear modules (tooth thicknesses). Metallurgical comparisons between test pieces, actual gears, and FEA simulations are shown.

The objective of this work is to develop the material and numerical models needed to simulate the carburizing process of an automotive gear. The paper discusses the factors that influence calculation time and accuracy and presents important equations and material property data. It describes how the simulation predicts local carbon content based on diffusion and how quenching computation provides information on stress states and residual stresses. It also explains how to account for the effects of grain growth, volume variation due to phase changes, and transformation plasticity.

The work presented in this paper addresses a data gap that continues to be a hinderance to users of precipitation modeling tools, particularly those based on Langer-Schwartz theory. Thermodynamic and kinetic data required for precipitation models can be obtained from CALPHAD databases, but interfacial energies between the bulk and precipitate phases are not available for many alloy systems. In this work, a number of matrix-precipitate interfacial energies have been determined for influential precipitates in alloys of industrial importance, for example, carbides in Grade 22 low-alloy steels, delta phase in Ni 625 and 718, S-phase in Al 2024, and Q’ and β’’ in Al 6111.