Digital Twin – The Key Driving Force for the Digital & Intelligent Transformation of Manufacturing I

Digital Twin – The Core Driving Force for Intelligent Transformation in Manufacturing

With the advancement of information and manufacturing technologies, people’s material lives have grown increasingly abundant, alongside surging demand for intelligent and customized products. Global industries are now confronted with core challenges: lifting production efficiency, shortening product time-to-market, adopting more agile and flexible production models, boosting resource and energy utilization efficiency, and rapidly responding to intelligent, personalized and volatile market demands.

To address these pain points, major industrial powers worldwide have rolled out strategic roadmaps for intelligent manufacturing. Germany proposed Industry 4.0, the United States launched the Manufacturing Renaissance Initiative, and China unveiled the Made in China 2025 strategy. As one of the foundational technologies for constructing the metaverse, digital twin integrates with industrial IoT, 5G communications, big data, cloud computing, artificial intelligence, 3D visualization and other technologies to build virtual replicas of physical objects. It enables simulation, emulation, prediction and auxiliary decision-making covering geometric shapes, physical models and operational behaviors, effectively resolving the above challenges and fulfilling the vision of intelligent manufacturing.

1. Origins of Digital Twin

The concept of "twin" first emerged from NASA’s Apollo Program. The term "Digital Twin" was formally put forward by Dr. Michael Grieves at a joint seminar between the University of Michigan and NASA in 2002. He argued that modern product, production and enterprise systems are essentially complex systems amid rising system complexity. To optimize and predict the performance of complex systems, an observable digital model — a comprehensive multi-physics digital representation of products — is required. It supports the retention and reuse of digital data generated throughout product design, manufacturing and operation lifecycles. By analyzing and mining equipment status data, sensor readings and operational history, technicians can conduct condition diagnosis, behavior prediction and intelligent scheduling.

Furthermore, accumulated database records allow industrial big data analysts to evaluate specific equipment series and their components, feeding insights back to product and process engineers for continuous product and process improvement, ultimately forming a closed-loop digital twin system.

The phrase "Digital Twin" was officially documented in NASA’s technical reports in 2010. In 2012, NASA and the U.S. Air Force co-published a research paper on digital twin, focusing its application on next-generation aircraft development. The period from 2015 to 2020 marked the embryonic stage of digital twin adoption, when leading industrial software enterprises successively invested in digital twin businesses. Over the past two years, digital twin technology has entered a phase of rapid development. Integrated with emerging technologies including AI, AR and VR, it has been widely deployed across diverse industries.

2. Typical Characteristics of Digital Twin

At its core, digital twin is information modeling that creates digital counterparts of physical entities in a virtual digital space. Unlike conventional modeling built on basic underlying data transmission formats, digital twin delivers holistic abstract descriptions of physical objects, encompassing external form, internal mechanisms and operational correlations. Its technical difficulty and application value increase exponentially compared with traditional modeling. A key feature is its multi-form adaptability: distinct digital models can be constructed to suit different use cases and scenarios.

(1) Interoperability

Physical assets and digital spaces within a digital twin system support two-way mapping, dynamic interaction and real-time connection. Digital twin can map physical entities via diverse digital models, enabling conversion, fusion and equivalent expression across different digital models.

(2) Scalability

Digital twin technology supports the integration, addition and replacement of digital models, facilitating expansion of multi-scale, multi-physics and multi-layer model contents.

(3) Real-Time Performance

Digital twin relies on digitized data management readable and processable by computers to characterize time-variant physical entities, covering appearance, status, attributes and internal mechanisms, and generating real-time virtual digital mappings of physical assets.

(4) High Fidelity

Fidelity refers to the similarity between virtual digital models and corresponding physical entities. Virtual replicas must achieve high simulation accuracy not only in geometric structures, but also in state, phase and time sequence. Note that simulation precision requirements vary across scenarios: working condition analysis may only demand physical property modeling without detailed chemical structure simulation.

(5) Closed-Loop Capability

Virtual digital replicas visualize physical entities and reproduce their internal mechanisms, supporting real-time monitoring, analytical reasoning and optimization of process and operational parameters to realize data-driven decision-making. In effect, digital twin endows both virtual models and physical equipment with intelligent analytical capabilities, forming a closed-loop system.

3. Hierarchical Architecture of Digital Twin

The technical framework of digital twin consists of four core layers: Physical Layer, Data Layer, Model Layer and Function Layer, supporting upper-layer application development.

Physical Layer

This layer comprises all tangible physical objects described by the digital twin system. Target entities vary by use case: a smart factory digital twin covers factories, workshops, production lines, workstations, plus core production elements including manpower, machinery, materials, methods and environment. The scope of physical objects also differs across industries.

Data Layer

Digital twin is data-driven. Real-time mapping and interaction between physical assets and virtual twins depend on seamless data exchange, which the data layer enables. It undertakes data collection, transmission and pre-processing, acting as the communication bridge between physical and virtual spaces.

Model Layer

As the core of digital twin, this layer integrates geometric models, rule models, mechanism models and algorithm models:

Geometric models: Recreate the external shapes of physical equipment.

Rule models: Abstract business logic to align operational workflows between virtual and physical assets.

Mechanism models: Summarize physical operating laws to predict equipment behaviors and enable pre-emptive intervention.

Algorithm models: Extract hidden patterns and actionable insights from massive datasets to support management decision-making.

Function Layer

Built upon the data and model layers, software-enabled digital twin systems deliver core capabilities including physical asset description, fault diagnosis, performance prediction and intelligent decision-making.

Application Layer

Supported by the four underlying technical layers, customized digital twin applications can be developed for various industries and scenarios, such as intelligent manufacturing, smart transportation, smart cities, smart buildings and smart healthcare.

4. Digital Twin Applications in Manufacturing

Manufacturing applications of digital twin fall into three major categories: Product Digital Twin, Production Digital Twin and Equipment Digital Twin, covering the full value chain of Product Lifecycle Management (PLM).

4.1 Product Digital Twin

During the product design phase, digital twin generates 3D digital prototypes and precisely records all physical parameters with intuitive visualization. Simulation and emulation verify product performance and adaptability under diverse external environments at the design stage. Compared with traditional workflows requiring physical prototypes for validation, this solution drastically shortens product cycles and cuts design verification costs.

4.2 Production Digital Twin

Deployed in manufacturing phases, production digital twin optimizes production systems (manufacturing processes, equipment, workshops and management control systems) to achieve efficient, high-quality and low-cost production. It accelerates product launch cycles, improves design quality, reduces manufacturing costs and boosts delivery speed. Virtual production lines fully integrate product digital twins with digital replicas of equipment and workflows to elevate cross-department collaboration efficiency.

4.3 Process Definition

Structured management of product data, process specifications, factory line layouts and manufacturing resources enables refined production control and delivers accurate scheduling inputs for production systems.

4.4 Virtual Manufacturing

Virtual manufacturing environments verify and evaluate assembly workflows and methodologies. Equipped with 3D product and workshop models, the system supports CNC machining simulation, human-machine emulation at assembly stations and robot simulation to conduct pre-production virtual assessment.

4.5 Virtual Production Line Commissioning

Flexible automated digital factories feature long construction cycles, high capital investment and complex automated control logic, leading to heavy on-site debugging workloads. Virtual line simulation identifies layout defects, mechanical interferences and PLC logic faults in advance, and comprehensively evaluates production line feasibility by integrating processing machinery, logistics equipment, intelligent tooling and control systems. This technology detects design flaws early during factory construction and significantly reduces rework costs.

4.6 Production Process Simulation

The system supports large-screen visualization and configuration design via drag-and-drop editing, linking equipment and database data to visual components for real-time display. Industrial scene modeling and presentation can also be deployed on web platforms.

4.7 Production Monitoring & Management

Real-time operational data from all production line equipment enables full-process visual monitoring. Monitoring strategies for core equipment parameters and inspection indicators are built via industry experience or machine learning algorithms, supporting rapid response to abnormal conditions for stable and continuously optimized production.

4.8 Equipment Digital Twin

Key production equipment heavily impacts manufacturing workflows, and breakdowns may trigger massive production losses. Equipment digital twin establishes virtual equipment replicas to monitor real-time operational status. Combined with historical operating data, real-time readings and operational records, big data analytics optimize equipment performance and enable predictive maintenance, lowering unplanned downtime risks and extending the service life of core machinery.

4.9 Equipment Performance Optimization

By collecting real-time operational data of production equipment, full-process visual monitoring is realized. Monitoring strategies for key equipment parameters and inspection indicators are formulated through experience or machine learning, enabling timely handling of anomalies to sustain stable production and drive continuous optimization.

4.10 Predictive Maintenance

Continuous collection and intelligent analysis of equipment operational data deliver a revolutionary maintenance model. Data insights predict optimal maintenance windows for machinery and factory components. The system triggers rapid alerts when equipment metrics deviate from threshold values, minimizing production downtime and raising line throughput.

4.11 Iterative Optimization of Design, Process & Manufacturing

Digital twin models capture real-time equipment operational data. Data analytics and algorithm models deliver objective feedback on actual equipment performance and quality, supporting iterative upgrades of equipment design and manufacturing processes, and forming a closed-loop optimization cycle across the entire product lifecycle.