(403g) Artificial Intelligence-Assisted Full-Map Understanding of Strain Sensing Devices and Designated Predictions for Soft Robotic Systems
AIChE Annual Meeting
Wednesday, November 18, 2020 - 9:30am to 9:45am
More specifically, in our material systems, at least 33,700 non-repeating recipes are available for strain sensing devices fabrication although we limit the step length in each variables of material recipes. With such a complex system and very limited understanding, there are no available in silico simulation methods to interpret or predict the sensing devices. In addition, it is nearly impossible to screen the whole design space via Edison approach because the sensing needs are customized every time. Herein, we have proposed a data-driven framework to exploratively learn the full-map of the strain sensing devices. The data is collected with thirteen active learning rounds which investigate both the mean minimal distance (L2)between suggestion points and existing data points, and the numerical uncertainty of well-trained models during each round. After a representative dataset is constructed, optimization strategies including GA combination, customized loss function, and data augmentation are used for improving the model performance. This best model has achieved a low average scattered numerical error ((Predicted value - Real value)/Real maximum value in device) of 24%.