Online Program
| Saturday, February 22 | |
| CS24 Using Graphs in Decisionmaking and Quality Control |
Sat, Feb 22, 10:45 AM - 12:15 PM
Bayshore VII |
Posterior Predictive Checks for Interference in a 3D Printing Experiment (302710)Tirthankar Dasgupta, Harvard University Department of StatisticsQiang Huang, University of Southern California Daniel J. Epstein Department of Industrial and Systems Engineering *Arman Sabbaghi, Harvard University Department of Statistics Jizhe Zhang, University of Southern California Daniel J. Epstein Department of Industrial and Systems Engineering Keywords: additive manufacturing, posterior predictive checks, quality control, Rubin Causal Model, Stable Unit-Treatment Value Assumption Additive manufacturing, or 3D printing, is a promising manufacturing technique marred by product deformation due to material solidification in the printing process. Control of printed product deformation can be achieved by a compensation plan. However, little attention has been paid to interference in compensation, which is thought to result from the inevitable discretization of a compensation plan. We investigate interference with an experiment involving the application of discretized compensation plans to cylinders. Our treatment illustrates a principled framework for detecting and modeling interference by means of graphical posterior predictive checks, which facilitate the study of printed product deformation under discretized compensation plans. Properly defining experimental units and understanding interference are critical for quality control in complex manufacturing processes. Our application of experimental design and posterior predictive checks provides a step in that direction for 3D printing.
|
|
