Method and system for determining ply-by-ply damage in a composite structure

A computing system that for determining ply-by-ply damage in a three-dimensional (3D) composite structure, the computing system comprising:

• one or more instruction storage devices for storing instruction code;
• and
• one or more processors in communication with the one or more instruction storage devices, wherein execution of the instruction code by the one or more processors causes the computing system to perform operations comprising:
• receiving a 3D geometric model at a computing system, wherein the 3D geometric model specifies geometric aspects of the 3D composite structure, and wherein the 3D geometric model facilitates performance of finite element analysis (FEA);
• applying each material failure model (MFM) of a plurality of MFMs to the 3D geometric model using FEA logic of the computing system, wherein the plurality of MFMs are configured to predict different types of damage to the 3D composite structure due to an applied force or stress, and wherein each MFM relates the applied force or stress to a particular type of damage to a material;
• generating a respective damage prediction image for each ply of the 3D composite structure using the FEA logic and for each MFM of the plurality of MFMs;
• inputting a plurality of damage prediction images from the plurality of MFMs into an input layer of a deep learning model implemented by machine learning logic of the computing system;
• and
• generating an aggregate prediction of damage to the 3D composite structure using the deep learning model, wherein:
• the aggregate prediction is based on the plurality of damage prediction images,
• the aggregate prediction comprises a 3D composite image, and
• the 3D composite image identifies a respective damage location and a respective extent of the damage for each ply of the 3D composite structure.

A non-transitory computer-readable medium that stores instruction code that facilitates determining ply-by-ply damage in a three-dimensional (3D) composite structure, wherein execution of the instruction code by one or more processors of a computing system causes the computing system to perform operations comprising:

• receiving a 3D geometric model at a computing system, wherein the 3D geometric model specifies geometric aspects of the 3D composite structure, and wherein the 3D geometric model facilitates performance of finite element analysis (FEA);
• applying each material failure model (MFM) of a plurality of MFMs to the 3D geometric model using FEA logic of the computing system, wherein the plurality of MFMs are configured to predict different types of damage to the 3D composite structure due to an applied force or stress, and wherein each MFM relates the applied force or stress to a particular type of damage to a material;
• generating a respective damage prediction image for each ply of the 3D composite structure using the FEA logic and for each MFM of the plurality of MFMs;
• inputting a plurality of damage prediction images from the plurality of MFMs into an input layer of a deep learning model implemented by machine learning logic of the computing system;
• and
• generating an aggregate prediction of damage to the 3D composite structure using the deep learning model, wherein:
• the aggregate prediction is based on the plurality of damage prediction images,
• the aggregate prediction comprises a 3D composite image, and
• the 3D composite image identifies a respective damage location and a respective extent of the damage for each ply of the 3D composite structure.