About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Symposium
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First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Presentation Title |
Teaching the Machine: Characterizing Speckle Patterns for Virtual Demonstrations
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Author(s) |
Jennifer Ruddock |
On-Site Speaker (Planned) |
Jennifer Ruddock |
Abstract Scope |
Learning from Demonstration models trained in virtual environments are a potential pathway to teach brittle robotic programming to perform dynamic, artisanal processes. Virtual demonstrations allow humans to share natural motions with robotic systems. The spray application of speckle-patterns used in digital image correlation provides a representative stochastic process, typically applied by human experts, and inherently difficult to automate -- especially on non-planar geometries. Critical to developing an accurate virtual environment is the rapid and accurate linkage between process parameters and the applied speckle pattern. In this work, we developed image processing tools for analysis and characterization of speckle patterns applied using a robotic spray and six process parameters. The speckle pattern features were then modeled with a support vector machine and verified by predicting the process parameters for desired spray pattern features. The results provide a method for rapid characterization and enable a more accurate simulation needed for virtual demonstrations. |
Proceedings Inclusion? |
Undecided |