US 12,488,256 B2
Predictive modeling for forged components
Michael George Glavicic, Indianapolis, IN (US); Chong M. Cha, Indianapolis, IN (US); Weizhou Li, Indianapolis, IN (US); and Sean Warrenburg, Indianapolis, IN (US)
Assigned to Rolls-Royce Corporation, Indianapolis, IN (US)
Filed by Rolls-Royce Corporation, Indianapolis, IN (US)
Filed on Jul. 1, 2021, as Appl. No. 17/305,227.
Prior Publication US 2023/0004829 A1, Jan. 5, 2023
Int. Cl. G06N 20/20 (2019.01); G01M 13/00 (2019.01); G06N 5/04 (2023.01); G06N 20/00 (2019.01)
CPC G06N 5/04 (2013.01) [G01M 13/00 (2013.01); G06N 20/00 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A computing device comprising:
a memory; and
one or more processors configured to:
extract training features from data representative of a plurality of surface geometry zones for a plurality of training forged components, wherein each training forged component from the plurality of training forged components is associated with data representative of a respective subset of surface geometry zones from the plurality of surface geometry zones;
for each training forged component:
obtain a plurality of computational fluid dynamics results for the respective subset of geometry zones by modeling application of a quench process to the respective training forged component according to quench parameters, wherein each geometry zone from the respective subset of geometry zones is associated with a respective subset of computational fluid dynamics results from the plurality of computational fluid dynamics results; and
determine, based on the plurality of computational fluid dynamics results, a respective model for identifying a respective set of fitting parameters;
obtain, based on each respective set of fitting parameters, a plurality of training model results representative of one or more mechanical properties;
store, in the memory, a trained machine learning model that associates the training features to the plurality of training model results;
store, in the memory, data representative of a target forged component having a target surface geometry zone that differs from all of the plurality of surface geometry zones for the plurality of training forged components;
perform a geometrical analysis with respect to the data representative of the target forged component to extract target features;
apply the trained machine learning model to the target features to obtain predicted model results for the target forged component, the predicted model results representative of the application of the quench process to the target forged component according to the quench parameters; and
output the predicted model results.