I-Corps: Additive Manufacturing Quality Control Software

The broader impact/commercial potential of this I-Corps project is the development of a new framework for image-guided quality control of additive manufacturing. At the point of production, operators will be alerted to product defects so that they can take corrective action instantly. In turn, managers responsible for product quality can investigate the root causes of defects and make improvements to their production processes. Ultimately, by improving product quality, US advanced manufacturers may decrease expenses associated with poor-quality parts and increase their competitiveness by ensuring only the highest quality products are delivered to their customer. This I-Corps project is based on the development of a novel method for monitoring and controling the dynamic image profiles of their products based on multiplex networks. This work is focused on creating a sensor-based, nonlinear dynamics methodology for real-time system informatics, monitoring, and control. Advanced sensing of industrial systems has given rise to in-situ imaging that allows for real-time monitoring and control of complex processes. For instance, high-speed cameras are often situated above powder beds in additive manufacturing (AM) machines to capture layer-by-layer images for process monitoring. This work will design and develop a new framework of image-guided quality control for additive manufacturing. This novel method may put US manufacturers at an advantage. In applying artificial intelligence deep neural network algorithms, the team addresses the complex structures in the high-dimensional image streams for in-situ monitoring and control of manufacturing processes. This process enables real-time quality inspection, defect mitigation, and process improvement.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.