Knowledge-driven digital twin manufacturing cell towards intelligent manufacturing
By Guanghui Zhou, Zhi Li, Kai Ding, Chuang Wang. International Journal of Production Research, 2019
353 citations recorded by the source corpus.
Rapid advances in new generation information technologies, such as big data analytics, internet of things (IoT), edge computing and artificial intelligence, have nowadays driven traditional manufacturing all the way to intelligent manufacturing. Intelligent manufacturing is characterised by autonomy and self-optimisation, which proposes new demands such as learning and cognitive capacities for manufacturing cell, known as the minimum implementation unit for intelligent manufacturing. Consequently, this paper proposes a general framework for knowledge-driven digital twin manufacturing cell (KDTMC) towards intelligent manufacturing, which could support autonomous manufacturing by an intelligent perceiving, simulating, understanding, predicting, optimising and controlling strategy. Three key enabling technologies including digital twin model, dynamic knowledge bases and knowledge-based intelligent skills for supporting the above strategy are analysed, which equip KDTMC with the capacities of self-thinking, self-decision-making, self-execution and self-improving. The implementing methods of KDTMC are also introduced by a thus constructed test bed. Three application examples about intelligent process planning, intelligent production scheduling and production process analysis and dynamic regulation demonstrate the feasibility of KDTMC, which provides a practical insight into the intelligent manufacturing paradigm.
Subfields: Automotive Engineering, Industrial and Manufacturing Engineering
View Paper: https://www.semanticscholar.org/paper/215fe734814d35627a36b41b9b51036ccc185157
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In Industrial and Manufacturing Engineering
Citations in the corpus
- A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics (2021, methodology): “Deep learning (DL) computer vision models, including residual and convolutional neural networks were deployed to recognize eventual quality issues during the automatic production and machining features of parts [103,104], which could be further utilized to enhance the quality and efficiency of assem”