Abstract
Digital twin (DT) and artificial intelligence (AI) technologies have grown rapidly in recent years and are considered by both academia and industry to be key enablers for Industry 4.0. As a digital replica of a physical entity, the basis of DT is the infrastructure and data, the core is the algorithm and model, and the application is the software and service. The grounding of DT and AI in industrial sectors is even more dependent on the systematic and in-depth integration of domain-specific expertise. This survey comprehensively reviews over 300 manuscripts on AI-driven DT technologies of Industry 4.0 used over the past five years and summarizes their general developments and the current state of AI-integration in the fields of smart manufacturing and advanced robotics. These cover conventional sophisticated metal machining and industrial automation as well as emerging techniques, such as 3D printing and human–robot interaction/cooperation. Furthermore, advantages of AI-driven DTs in the context of sustainable development are elaborated. Practical challenges and development prospects of AI-driven DTs are discussed with a respective focus on different levels. A route for AI-integration in multiscale/fidelity DTs with multiscale/fidelity data sources in Industry 4.0 is outlined.
Highlights
Industry 4.0 and smart manufacturing are crucial fundamentals of modern industry and the national economy
Challenges and development prospects of artificial intelligence (AI)-driven digital twin (DT) in smart manufacturing and advanced robotics are discussed with a respective focus on different levels
Reinforcement learning (RL), such as deep Q-network (DQN) and deep reinforcement learning (RL), were employed as a substitute for heuristic optimization and supervised approaches in various investigations, where the major task is normally mathematically formalized as a Markov decision process (MDP), with the objective of autonomously achieving the global optimal economic and logistic key performance indicators (KPI) in a factory or logistic simulation environment [96,97,98,99] (EG-factor)
Summary
Industry 4.0 and smart manufacturing are crucial fundamentals of modern industry and the national economy. Achieving holistic sustainability commonly requires a balance within the financial, environmental, social and governance dimensions, i.e., FESG factors [5] This increases the costs of manufacturing enterprises and simultaneously raises severe challenges for their organizations and processes. To further contribute to developing and landing of these general-purpose technologies (GPT) in smart manufacturing and advanced robotics, the following research questions (RQ) are proposed in conducting this survey: RQ1: What are the current research and concrete case solutions on DTs?. This paper endeavors to address this research gap by revisiting current developments on DTs from a domain-specific perspective, analyzing implemented AI methods in each subarea, sorting out the role they play in sustainable development, and summarizing practical challenges in various application fields. 3. Challenges and development prospects of AI-driven DTs in smart manufacturing and advanced robotics are discussed with a respective focus on different levels. A route for AI-integration in multiscale/fidelity DTs with multiscale/fidelity data sources along the product lifecycle is outlined
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