Knowledge-driven digital twin manufacturing cell towards intelligent manufacturing

TLDR

Rapid advances in big data analytics, IoT, edge computing, and AI have propelled traditional manufacturing toward intelligent manufacturing, which requires autonomous, self‑optimising cells with learning and cognitive capabilities. This paper proposes a knowledge‑driven digital twin manufacturing cell (KDTMC) framework that supports autonomous manufacturing through intelligent perception, simulation, understanding, prediction, optimisation, and control. KDTMC is built on a digital twin model, dynamic knowledge bases, and knowledge‑based intelligent skills, implemented in a test bed, giving it self‑thinking, decision‑making, execution, and improvement capabilities. Three application examples—intelligent process planning, production scheduling, and dynamic process analysis—demonstrate KDTMC’s feasibility and provide practical insight into the intelligent manufacturing paradigm.

Abstract

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.