In many aspects such as manufacturing, construction, and urban management, digital twins are no longer an out-of-reach concept but have become a core tool for achieving refined operations and innovation. The essence of digital twins is to use data and models to build dynamic images of physical entities in virtual space for simulation, analysis, and optimization. However, successful implementation is not simply the deployment of a piece of software, but a set of system projects that require careful planning.
How to choose the right technology platform for digital twins
In a digital twin project, the choice of technology platform is the cornerstone. The first thing to evaluate is the data integration and processing capabilities of the platform. This capability needs to be able to seamlessly connect multi-source heterogeneous data, such as various data from IoT sensors, enterprise ERP, and CAD drawings. Many projects fail because the platform cannot break through data silos, resulting in one-sided and lagging twin data.
Among the platforms, model construction and simulation engines are extremely critical. In this case, it should have the ability to support the import and integration of lightweight 3D models to high-fidelity physical models, and it can also perform real-time or quasi-real-time dynamic simulations. In industrial scenarios, what may need to be paid attention to is the depth of the platform's support for specific industry protocols and standards, such as OPC UA. When making a choice, don’t blindly pursue the most comprehensive functions, but focus on the capabilities that can best solve your core business pain points.
What key data are needed for digital twin implementation?
What directly determines the upper limit of the value of a digital twin is the quality and dimension of the data. Key data is divided into two categories: static data and dynamic data. Static data such as equipment 3D models, bills of materials (BOM), engineering drawings, etc. create the skeleton of the twin. In order to facilitate call and association, these data must be standardized and structured.
Dynamic data mainly comes from IoT sensors and business systems, covering temperature, pressure, vibration, energy consumption, production order status, etc. This data, both real-time and historical, brings the twins to life. One point that is often overlooked is the need to build a unified data dictionary and identification system to ensure that data from different sources can be semantically aligned, otherwise the analysis results will be meaningless.
How to build an accurate digital twin model
Three-dimensional visualization is not simply equivalent to the construction of a digital twin model. Determining the fidelity level of the model is the first step. For a factory, you may need multi-level models at the factory level, production line level, equipment level or even component level. The model accuracy and required data at different levels are completely different, and resources should be invested appropriately based on the analysis goals.
The construction process often starts from the lightweighting of existing CAD and BIM models, and adds business logic and rules on this basis. For example, a machine tool model is associated with its maintenance manual, fault code library, and real-time performance parameter thresholds. The model must be updateable to adapt to the transformation and changes of the physical entity during its life cycle.
How digital twins integrate with existing systems
The most complex and time-consuming link in implementation is integration. Integration strategies often use middleware or API gateways to build data channels between the digital twin platform and existing MES, SCADA, CMMS and other systems. The focus is on finalizing clear data interface specifications and update frequency, and weighing real-time requirements and system load.
Permission and security inheritance are another key point. As a new level of data aggregation and display, digital twins must inherit the user permission system and network security policies of the original system. The purpose of integration is not to replace the original system, but to become the "upper brain" that connects various systems, enhances the value of data collaboration, and prevents the formation of new information islands.
What business problems can digital twins solve?
The value of digital twins must be specifically implemented into business-related issues that can be quantified. In terms of predictive maintenance, with the help of continuous analysis based on data generated during equipment operation, models can be used to predict the service life of parts and arrange maintenance work in advance, thereby reducing downtime that does not occur as planned. For example, wind power companies have used digital twin analysis of wind turbine blade stress to optimize maintenance routes and spare parts inventory.
From the perspective of process optimization, the digital twin of the production line can simulate different production scheduling plans, and can also simulate the material flow path to find production bottlenecks and optimization opportunities. In the planning stage of a new factory, digital twins can carry out layout simulation and human flow and logistics simulation to avoid design defects and save a lot of later transformation costs. Provide global procurement services for weak current intelligent products!
What are the common challenges in digital twin project implementation?
The biggest challenges are often not technical, but organizational and managerial. The lack of clear business leadership is the primary problem. If the project is promoted by the IT department alone, it can easily become a technology demonstration. Business departments (such as production and operation and maintenance) must give clear performance improvement indicators (KPIs) and be deeply involved throughout the process.
Another major obstacle is the lack of data governance. The data is inaccurate, incomplete, and untimely. As a result, the insights output by the twin are worthless. Therefore, data cleaning and governance work should be started simultaneously at the early stage of the project. In addition, it is also a common misunderstanding to have too high initial expectations and try to build a "whole-factory twin" at once. A more feasible path is to select a key asset or process with high value and good data foundation as a pilot to quickly verify the value, and then gradually promote it.
In your industry, do you think the most significant obstacle to the implementation of digital twins is the complexity of technology integration, or the resistance to the reconstruction and collaboration of internal business processes? Welcome to share your opinions and insights in the comment area. If you think this article has reference value, please like it and share it with more colleagues who may need it.
Leave a Reply