Artificial intelligence (ai)-powered ultrasonic imaging of defects in solids

An apparatus for detecting a defect in an object, comprising:

• an electronic processor configured to:
• characterize an excitation force profile by determining a temporal and spatial distribution of the excitation force profile;
• match a measurement point's location from a surface of the object to a corresponding location in a background mesh, wherein the background mesh has a fixed reference geometry;
• map a damage profile of a training data object onto a volume of the training data object's background mesh to generate a binary classification for each of a plurality of grid elements of the background mesh;
• convert the binary classification into a continuous probability value based on a distance between each of the plurality of grid elements and a damage location within the background mesh to generate a training matrix;
• train an artificial neural network (ANN) using a training dataset including the training matrix;
• receive measurement data of points from the surface of the object corresponding to locations in the background mesh;
• and
• predict, via the trained ANN, the defect as a group of damaged elements within the background mesh based on the measurement data.

A non-transitory computer-readable medium storing instructions for detecting a defect in an object, wherein the instructions when executed by a computer, cause the computer to:

• characterize an excitation force profile by determining a temporal and spatial distribution of the excitation force profile;
• match a measurement point's location from a surface of the object to a corresponding location in a background mesh, wherein the background mesh has a fixed reference geometry;
• map a damage profile of a training data object onto a volume of the training data object's background mesh to generate a binary classification for each of a plurality of grid elements of the background mesh;
• convert the binary classification into a continuous probability value based on a distance between each of the plurality of grid elements and a damage location within the background mesh to generate a training matrix;
• train an artificial neural network (ANN) using a training dataset including the training matrix;
• receive measurement data of points from the surface of the object corresponding to locations in the background mesh;
• and
• predict, via the trained ANN, the defect as a group of damaged elements within the background mesh based on the measurement data.