As an indispensable link of the life-cycle of AM, end-part quality control in Cyber-Physical Additive Manufacturing Systems (CPAMS) is made difficult by enormous differences in product designs/varieties. Statistical monitoring of additive manufacturing (AM) processes faces major challenge due to the nature of one-of-a-kind manufacturing. This posters puts forth a prescriptive SPC scheme to monitor shape deformation from shape to shape. Only a limited number of test shapes are required to establish control limits.

A significant challenge in dimensional accuracy control of cyber-physical additive manufacturing systems (CPAMS) is the specification of geometric shape deviation models. The current practice of constructing tailor-made deviation models for each combination of computer- aided design model, additive manufacturing (AM) process, and process setting is impractical and inefficient for general application in CPAMS. We present a new framework and class of  Bayesian neural networks for automated and efficient deviation model building in CPAMS.

Abstract: Despite their importance within the energy sector, buildings have not kept pace with technological improvements and particularly the evolution of intelligent features. A primary obstacle in enabling intelligent buildings is their highly distributed and diverse nature.

Abstract: Cyber-Physical Systems (CPS) that modify themselves to improve performance or repair damage often recast the modular relationships among system components that enable Verification and Validation (V&V). We focus on in-flight control adaptation of damaged Flapping-Wing Micro Air Vehicles (FW-MAV).

Abstract: Growing demands on our civil infrastructure have heightened the need for smart structural components and systems whose behavior and performance can be controlled under a variety of loading scenarios such as high winds and earthquakes.