Abstract
Abstract Cyber-manufacturing relies on smart digital-twins of manufacturing processes that can quickly act for making a wise decision. However, the cognitive computing part of the digital-twin becomes time-intensive beyond the requirement of a smart system when it uses simulation tools that solve governing constitutive equations in the form of partial differential equations (PDE). On the other hand, many artificial intelligence (AI) and machine learning (ML) solutions rely on a large data set that does not exist in many manufacturing systems. We build a hybrid digital-twin that takes advantage of an ML-based digital-twin for quick response while gaining fidelity through adaptive learning with a PDE-based digital-twin. We use our hybrid digital-twin for active exploration of various overlay metal deposition patterns in real-time. This tool enables engineers to explore and compare many patterns they need to assess metal deposition scenarios with no delay for computational time.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.