3D Heat-Map Development based on Fault Diagnosis Data

This work focuses on generation a framework to employ a set of 3D coordinates, as the input dataset to the model, and generate the 3D heat map based on the 3D shape. The generated 3D heatmap aims to define the most probable areas for fault categories on the 3D surface. To develop such a system, the 3D shape is printed and the 3D coordinates of simulated faults are recorded using a tool tracker. Then, a machine learning platform is employed to use the 3D fault datasets as the input and produce the probabilities of different fault categories on the given location. Finally, the 3D heat map is generated to efficiently visualize the 3D shape with the most probable areas of fault categories. Consequently, the manufacturer can localize the probabilities of fault categories on a given 3D shape.

Intern: 
Eman Nejad
Faculty Supervisor: 
Jonathan Wu
Province: 
Ontario
Partner: 
Partner University: 
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