Equipment/Infrastructure

Digital Twin

Creates a perfect virtual replica of physical manufacturing sites and equipment for real-time simulation and data visualization.

ISO 23247 (Manufacturing Digital Twin)

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Are you facing these challenges?

As you pursue smart factory initiatives, digitalization of equipment and processes has emerged as a critical priority. But in reality, the following difficulties keep recurring.

Commissioning new equipment or lines takes too long

When introducing equipment or modifying a line, on-site commissioning takes weeks to months before actual operation begins. Various operating conditions must be tested one by one on physical equipment, and when unexpected issues arise, corrections and retesting are repeated. During the commissioning period, production stops and costs rise.

By the time anomalies are detected, it is already too late

When anomalies are detected in equipment, the equipment is usually on the verge of failure or quality issues have already occurred. Sensor data is being collected, but early-stage deviations from normal ranges are not captured. Sometimes too many alarms sound, causing critical alerts to be missed.

You cannot know equipment status without visiting the shop floor

To check current equipment status, you must physically visit the site. For companies operating multiple plants, there is no way to get a consolidated view of key equipment status across all locations. When problems occur during nights or holidays, response time is delayed and the situation is only assessed upon arrival.

There is plenty of sensor data but it is not being utilized

Data streams from vibration, temperature, pressure, current, and other sensors, but there is no systematic framework for analyzing this data and converting it into meaningful insights. Data accumulates, but predictions and optimizations based on that data are not happening.

The cost and complexity of digital twin adoption is a barrier to entry

Global digital twin platforms cost hundreds of thousands of dollars or more to implement and take months to deploy. Specialized development personnel are required, and integration with existing systems must be conducted as a separate project. It is difficult to be confident in the return on investment, making the adoption decision itself challenging.

Here is how we solve it

Reduce on-site commissioning time with virtual commissioning

When equipment and processes are implemented in a digital twin, various operating conditions can be tested in a virtual environment before operating the physical equipment.

6 types of simulation scenarios (physics-based, data-driven, hybrid, finite element analysis, computational fluid dynamics, discrete event simulation) are supported, with integration to external engines (ANSYS, COMSOL, SIMIO, FlexSim, AnyLogic, etc.) for precision simulation.

What-If analysis is possible by changing parameter sets for repeated execution and comparing results against baseline. Simulation results are linked to KPIs, enabling quantitative comparison of productivity, energy consumption, and quality impact under each condition.

Detect anomalies at the earliest stage

5 types of anomaly detection methods (statistical, machine learning-autoencoder, machine learning-Isolation Forest, rule-based, ensemble) are combined to capture subtle anomaly patterns that a single method might miss.

Alarm management is designed per the ISA-18.2 standard. Shelving (temporary suppression) and Suppression (conditional suppression) reduce unnecessary alarms, and an alarm rationalization workflow periodically reviews the appropriateness of each alarm. Chattering (frequently repeating) alarms and Nuisance (meaningless) alarms are statistically identified, creating an environment where operators can focus on truly important alerts.

Hysteresis (deadband) settings prevent repeated alarms near threshold values, and escalation rules automatically notify senior personnel if no action is taken within a specified time.

Monitor equipment status in real time from anywhere

Real-time operational data from equipment is reflected in the digital twin, enabling intuitive understanding of current equipment status on 3D visualization screens. 4 types of 3D engines are supported (Unity, Unreal Engine, WebGL, custom renderer), with 8 types of data-to-visualization mapping (color, position, size, rotation, transparency, text, gauge, animation) to visually represent sensor data.

LOD (Level of Detail) settings enable zooming from plant-wide overview down to individual component level. DTDL (Digital Twins Definition Language) v3 compatible schemas are supported, keeping the path open for Azure Digital Twins integration.

Leverage sensor data for prediction and optimization

Based on collected sensor data, 7 types of ML algorithms (linear regression, random forest, XGBoost, LSTM, GRU, Transformer, ensemble) are applied to perform 6 types of predictive analytics: remaining useful life prediction, energy consumption prediction, quality prediction, process optimization, failure prediction, and demand forecasting.

Feature Store manages feature data, and the model registry handles model version management and A/B testing. XAI (Explainable AI) functionality supports 6 explanation methods including SHAP and LIME, enabling understanding of why a model made a particular prediction.

The entire MLOps pipeline is built into the solution, enabling prediction model construction and operation without a separate data science platform.

Build digital twins with No-Code

Design tables, screens, and workflows on the canvas, and DDL, API, and UI are automatically generated. There is no need to conduct a separate development project for digital twin construction.

While global digital twin platforms take months to deploy, VEXPLOR Digital Twin can launch a basic digital twin within hours to days with just data source connection and model configuration. Simulation, predictive analytics, and 3D visualization can be progressively added as needed.

Global standards this solution follows

ISO 23247 -- Manufacturing Digital Twin Framework

Why does this standard matter?

While the term "digital twin" is widely used, ISO 23247 is the international standard that defines "what specific components a digital twin should have." This standard defines manufacturing digital twins as 4 sub-entities, clarifying the roles and relationships of the physical entity, virtual entity, data collection layer, and user interface. Following this framework ensures that a digital twin goes beyond simple visualization to achieve a complete loop of data collection-modeling-analysis-decision making.