Learning and Recalibration With Small Sets of Shapes for 3D Printing Poster.pdf
pdf
Calibration, or accuracy control, of cyber-physical additive manufacturing systems relies on predictive models for geometric shape deformation. However, learning predictive models is made difficult by the wide variety of possible process conditions and shapes. In addition, resource constraints limit the manufacture of test shapes, which further impedes learning of deformation models for new shape varieties. A methodology that can make full use of data collected on different shapes and reduce the haphazard aspect of traditional learning techniques is necessary in this context.
Submitted by Qiang Huang
on
pdf
Calibration, or accuracy control, of cyber-physical additive manufacturing systems relies on predictive models for geometric shape deformation. However, learning predictive models is made difficult by the wide variety of possible process conditions and shapes. In addition, resource constraints limit the manufacture of test shapes, which further impedes learning of deformation models for new shape varieties. A methodology that can make full use of data collected on different shapes and reduce the haphazard aspect of traditional learning techniques is necessary in this context.
Submitted by Qiang Huang
on