Machine-Learning Digital Twin of Overlay Metal Deposition for Distortion Control of Panel Structures


    Authored by Mahyar Asadi, Michael Fernandez, Majid Tanbakuei Kashani, and Mathew Smith; and presented at the International Federation of Automatic Control Virtual Conference.

    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. 

    Read the complete paper here.

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