Dresden 2020 – scientific programme
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MM: Fachverband Metall- und Materialphysik
MM 11: Topical Session: Data Driven Materials Science - Machine Learning for Damage Prediction
MM 11.1: Topical Talk
Monday, March 16, 2020, 15:45–16:15, BAR 205
From microscopic models of damage accumulation in Ni-base superalloys to the failure probability of gas turbine components — •Tilmann Beck1, Benedikt Engel2, Nadine Moch3, Lucas Mäde4, Sebastian Schmitz4, and Hanno Gottschalk3 — 1TU Kaiserslautern, Kaiserslautern, Germany — 2University of Nottingham, Nottingham, UK — 3Bergische Universität Wuppertal, Wuppertal, Germany — 4Siemens AG Gas & Power, Berlin, Germany
Conventionally cast (CC) Ni-base superalloys subjected to cyclic mechanical loading exhibit considerable scatter in fatigue lifetime. This is caused by i) a very coarse crystal structure ii) an extremely pronounced elastic anisotropy with Young's moduli (T = 850°C) of approx. E = 100 GPa in [001] and up to 250 GPa in [111] lattice direction and (iii) the fact that fatigue cracks are predominantly initiated in type {111} <110> slip systems of the fcc lattice.
A modeling approach is presented considering anisotropy of E and the Schmid factor m of the {111} <110> slip systems. Based on this, and EBSD analyzes of the actual grain orientation distribution, it is possible to (i) identify crystal grains prone to fatigue cracking and (ii) to explain the major part of the scatter in fatigue lifetime. Using Monte-Carlo simulations of grain orientations, frequency distributions of E and m were determined and collapsed into a damage parameter which quantifies the grain orientation dependent scatter in fatigue life. Using probabilistic approaches based on Weibull's weakest link concept, a model was developed for prediction of the influence of component size and inhomogeneous load distributions on the fatigue lifetime.