Rose Hills Foundation Fellow
University of Southern California (USC)
Epstein Department of Industrial and Systems Engineering
Unrestricted registration of 3D scanned point clouds of 3D printed parts to CAD models can lead to alignments that underestimate a part's geometric deviations. This line of research illustrates the magnitude of this bias, and provides a recommended methodology for minimizing these issues.
Efficiently Registering Scan Point Clouds of 3D Printed Parts for Shape Accuracy Assessment and Modeling (Journal) (Preprint)
Dimensional deviations between a 3D printed part and its intended shape can lead to costly scrap and rework. This line of research demonstrates a method for quantifying, predicting and prescriptively compensating for a part's errors before they happen using past print data from different shapes and machine learning.
Geometric Accuracy Prediction and Improvement for Additive Manufacturing Using Triangular Mesh Shape Data (Journal)
Geometric Accuracy Prediction for Additive Manufacturing Through Machine Learning of Triangular Mesh Data (Proceedings) (Preprint)
Assessing the Use of Binary Blends of Acrylonitrile Butadiene Styrene and Post-Consumer High Density Polyethylene in Fused Filament Fabrication (Journal) (Preprint)
A Simplified Benchmarking Model for the Assessment of Dimensional Accuracy in FDM Processes (Journal) (Preprint)
The use of cyber-physical systems to integrate data collection, storage, analysis, decision making, and action in additive manufacturing can enable the improvement of accuracy for printed parts based on knowledge that is automatically generated from previous prints.
Intelligent Accuracy Control Service System for Small-Scale Additive Manufacturing (Journal) (Preprint)
Optimizing the Expected Utility of Shape Distortion Compensation Strategies for Additive Manufacturing (Proceedings - Open Access)
A Digital Twin Strategy for Major Failure Detection in Fused Deposition Modeling Processes (Proceedings - Open Access)