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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

Montag, 16. März 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.