Cognitive Digital Twin for Manufacturing Systems
Mohammad Abdullah Al Faruquea, Deepan Muthirayanb, Shih-Yuan Yuc and Pramod P. Khargonekard
Department of Electrical Engineering and Computer Science University of California, Irvine, California, USA
aalfaruqu@uci.edu
bdmuthira@uci.edu
cshihyuay@uci.edu
dpramod.khargonekar@uci.edu
ABSTRACT
A digital twin is the virtual replica of a physical system. Digital twins are useful because they provide models and data for design, production, operation, diagnostics, and autonomy of machines and products. Hence, the digital twin has been projected as the key enabler of the Visions of Industry 4.0. The digital twin concept has become increasingly sophisticated and capable over time, enabled by many technologies. In this paper, we propose the cognitive digital twin as the next stage of advancement of a digital twin that will help realize the vision of Industry 4.0. Cognition, which is inspired by advancements in cognitive science, machine learning, and artificial intelligence, will enable a digital twin to achieve some critical elements of cognition, e.g., attention (selective focusing), perception (forming useful representations of data), memory (encoding and retrieval of information and knowledge), etc. Our main thesis is that cognitive digital twins will allow enterprises to creatively, effectively, and efficiently exploit implicit knowledge drawn from the experience of existing manufacturing systems and enable the transfer of higher performance decisions and control and improve the performance across the enterprise (at scale). Finally, we present open questions and challenges to realize these capabilities in a digital twin.
Keywords: Digital Twin, Manufacturing Systems, Cyber- Physical Manufacturing Systems, Cognitive Systems, Industry 4.0.
