Rose Hills Foundation Fellow
University of Southern California (USC)
Department of Industrial and Systems Engineering
Research Focus: application of artificial intelligence for predicting and compensating for error in additive manufacturing processes
My research uses past manufacturing accuracy data in addition to 3D triangular mesh shape data to generate machine learning models that can predict the inaccuracies of a 3D print before it is produced, allowing for prescriptive compensation of CAD models. This can reduce the frequency of failed and out of tolerance 3D prints in addition to improving the quality of completed prints.
This ongoing research seeks to increase the accuracy of 3D printers using manufacuting accuracy data and machine learning.
This research demonstrated the use of binary blends of recycled HDPE and ABS as a more sustainable and cost saving FDM filament.
This project led to the development of a simplified benchmarking object to efficiently measure the accuracy of a 3D printer.
This research focused on how undergraduate research experiences can shape the ethical and global awareness of undergraduate students.