Microstructure-Based Fatigue Strength Estimation for Design and Qualification of Heavy-Section Ductile Iron Castings

Modern cast irons, such as high silicon ductile cast iron EN-GJS-500-14, are increasingly considered in heavy-section structural components, e.g., used in wind turbines. Increasing demands towards lightweight design and controllability of the component's quality require a description of the local microstructure gradients and resulting mechanical properties. Thus, this work presents a fundamental concept for the micromechanical extension of modern design guidelines for heavy-section castings of ductile cast iron exemplarily demonstrated for the grade EN-GJS-500-14.
The prediction of the local microstructure is based on the systematic correlation of casting process simulation and metallographic microstructure characterization. A neural network is trained to predict the local formation of graphite precipitates. The available microstructure descriptors for ductile cast iron are extended using the two-point statistic, whose applicability is demonstrated for experimental and artificial micrographs.
The microstructure-dependent fatigue strength is experimentally determined by thermistor-based temperature monitoring during a load increase test. The monitored temperature is evaluated using a Palmgren-Miner-based damage evaluation concept. The applicability of the methodology is demonstrated by comparing the results to statistical-experimental S-N-curves.
Simulative-synthetic S-N-curves are computed using a finite element implementation of the Tanaka-Mura model. The simulative-synthetic S-N-curves are compared to experimental S-N-curves, such that model validity and model sensitivity are demonstrated.
This work presents a systematic integration for the consideration of local microstructure gradients and resulting mechanical properties in the design of heavy-section castings.