Evolutionary digital twin: A new approach for intelligent industrial product development |
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| Affiliation: | 1. State Key Laboratory of Complex Product Intelligent Manufacturing System Technology, Beijing Institute of Electronic System Engineering, Beijing, PR China;2. Beijing Complex Product Advanced Manufacturing Engineering Research Center, Beijing Simulation Center, Beijing, PR China;3. Science and Technology on Special System Simulation Laboratory, Beijing Simulation Center, Beijing, PR China;4. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, PR China;5. School of Economics and Management, University of the Chinese Academy of Sciences, Beijing, PR China;1. Department of Civil & Environmental Engineering, National University of Singapore, Block E1A, #07-03, No.1 Engineering Drive 2, Singapore 117576, Singapore;2. Future Cities Laboratory, Singapore-ETH Centre, 1 CREATE Way, CREATE Tower, #06-01, Singapore 138602, Singapore;3. Applied Computing and Mechanics Laboratory (IMAC), School of Architecture, Civil and Environmental Engineering (ENAC), Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland;1. School of Urban Economics and Management, Beijing University of Civil Engineering and Architecture, Beijing 100044, China;2. Department of Construction Management, Tsinghua University, Beijing 100084, China;1. Department of Mechanical Engineering, University of Alberta, Edmonton, Canada;2. Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, China;1. School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, China;2. National Engineering Research Center for Technological Innovation Method and Tool, Hebei University of Technology, Tianjin 300130, China;3. Department of Mechanical Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada |
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| Abstract: | To fulfill increasingly difficult and demanding tasks in the ever-changing complex world, intelligent industrial products are to be developed with higher flexibility and adaptability. Digital twin (DT) brings about a possible means, due to its ability to provide candidate behavior adjustments based on received “feedbacks” from its physical part. However, such candidate adjustments are deterministic, and thus lack of flexibility and adaptability. To address such problem, in this paper an extended concept – evolutionary digital twin (EDT) and an EDT-based new mode for intelligent industrial product development has been proposed. With our proposed EDT, a more precise approximated model of the physical world could be established through supervised learning, based on which the collaborative exploration for optimal policies via parallel simulation in multiple cyberspaces could be performed through reinforcement learning. Hence, more flexibility and adaptability could be brought to industrial products through machine learning (such as supervised learning and reinforcement learning) based self-evolution. As a primary verification of the effectiveness of our proposed approach, a case study has been carried out. The experimental results have well confirmed the effectiveness of our EDT based development mode. |
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| Keywords: | Evolutionary digital twin Intelligent industrial product Collaborative evolution Approximate world Multiple cyber spaces Simple evolution paradigm Model evolution paradigm |
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