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Activity Number: 487 - Design and Analysis of Computer Experiments for Complex Systems
Type: Invited
Date/Time: Wednesday, August 2, 2017 : 10:30 AM to 12:20 PM
Sponsor: Section on Physical and Engineering Sciences
Abstract #322219
Title: Sequential learning of deformation models in additive manufacturing through calibration of simulation models
Author(s): Tirthankar Dasgupta* and Ying Hung
Companies: Rutgers University and
Keywords: Physical experiments ; Calibration ; Meta-models ; Deformation
Abstract:

Successful prevention of geometric shape deformation of products manufactured by additive manufacturing depends on building predictive deformation models. However, resource constraints impose severe restrictions on the number of test shapes of a particular type, making the use of meta models inevitable. To build such meta models of deformation with good predictive power, calibration of existing models with data from physical experiments is necessary. We propose a sequential procedure for designing physical experiments and calibrating an ensemble of existing simulation models with data obtained from such experiments.


Authors who are presenting talks have a * after their name.

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