As manufacturers strive to improve efficiency and reduce risk, many are turning to technology that eliminates costly guesswork: digital twins. Once reserved for advanced industries like aerospace and automotive, digital twins are now accessible to a wider range of manufacturers thanks to advances in cloud-based 3D modeling, physics engines, and AI. McKinsey predicts the market for digital twins will reach $73 billion by 2027, reflecting rapid adoption across industry verticals.
This article provides a clear look at what they are, how they help, their limitations, and how Vention makes them accessible to manufacturers of all sizes.
What is a Digital Twin in the Context of Manufacturing Automation?
A digital twin is essentially an identical virtual replica of the physical automation system. Its core purpose is to behave in the digital world precisely as its real-life counterpart would. The more accurately this virtual model mirrors reality, the more effective and reliable it becomes as a tool to validate manufacturing automation.
A true digital twin goes beyond offering a static 3D, made-to-scale model. It should have the ability to put the model in context of laws of physics. For instance, a digital twin of a conveyor system should accurately simulate the movement of objects while replicating real world factors such as object dimensions, insertion rate, and weight. Ideally a digital twin must allow users to virtually explore and observe the system as they would in the real world.
For complex systems like robots and actuators, the digital twin needs to use the same backend motion commands and planners as the real-life system. This ensures that the virtual motion accurately reflects physical movement.
Levels of Digital Twins in Manufacturing
Digital twins can be applied at different scales, from single components to entire factories. These three levels highlight the different ways digital twins can deliver value, depending on scope.
- Component twins: Model a single asset, such as a robotic gripper, actuator, or spindle, to test performance under varying loads.
- System twins: Replicate an integrated system (for example, a CNC tending cell or a robotic welding line) to validate workflows, throughput, and safety logic.
- Factory twins: Represent an entire facility, combining production lines, conveyors, and utilities to optimize scheduling, energy use, and layout planning.
How Digital Twins Help De-risk Automation Projects
The biggest advantage of digital twins is their ability to allow system validation with zero upfront investment. Beyond validating feasibility for new deployments, a digital twin can also be an effective tool in improving performance of existing automation equipment and even making end-of-life or preventative decisions. Here are the top benefits of digital twins for manufacturing.
Benefits of Digital Twins
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Pre-validation to De-risk Automation: For companies new to automation, simulation is crucial for validating the design before committing to hardware. Digital twins allow manufacturers to thoroughly test and ensure the system is working before spending any money on hardware.
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Reduced Downtime: For machines already in operation, digital twins significantly reduce downtime. Any proposed changes, whether to a robot’s recipe, machine relocation, or introduction of new environmental elements, can be experimented with virtually. Mistakes can be undone with a simple click, preventing costly disruptions to live production. Once validated in the digital twin, the proven code can be transferred to the real system with confidence, eliminating surprises.
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Accelerated Commissioning: Digital twins expedite the commissioning process by allowing tasks like repositioning equipment, testing robot paths, and validating reach to be performed virtually with a mouse, rather than physically moving heavy objects. This predictive power allows for quicker setup and testing. For instance, if a new tray is out of a robot’s range, the digital twin can help determine optimal placement virtually, saving significant time and effort.
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Enhanced Collaboration and Remote Programming: Cloud-based digital twins facilitate remote collaboration, allowing experts or colleagues to access, review, and adjust program logic from any location. This is particularly powerful when the digital twin uses the exact same programming environment and code as the real system, preventing information loss or inaccuracies during code translation.
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OEE and Quality Improvement: Once a machine is running, the digital twin helps cut downtime, a key factor in OEE. Changes like new recipes or environmental adjustments can be tested virtually before going live. Errors can be undone instantly in the twin, so the real system runs smoothly when code is transferred, avoiding costly surprises. For quality control, the digital twins with modern physics engines have a crucial role to play. Their ability to perform quality checks virtually helps manufacturers determine factors such as adequate suction on boxes to avoid product damages, or the pressure required to handle delicate parts. Ultimately, this helps manufacturers maintain a high level of quality across the board.
The pre-validation and safe testing environment offered by the digital twins is invaluable for manufacturers who want to avoid costly mistakes, reduce downtime, and accelerate the deployment of new or modified automated systems. And with Vention’s platform this can be done at no cost.
Real World Examples of Digital Twins
Digital twins are already helping businesses accurately validate diverse automation applications. Here are two great examples of how digital twins can be used effectively.
The Feed Debugged Their Modular Conveyors with a Digital Twin
The Feed, the world’s largest sports nutrition marketplace for endurance athletes successfully validated their conveyor design using Vention’s MachineLogic. The Physics Engine helped accurately simulate their conveyors in a highly realistic virtual environment. Being able to test product specifications helped them test and debug program behavior, improving confidence in the final system.
Lúnasa Space is Simulated Complex Satellite Interactions with a Digital Twin
Lúnasa Space, which builds mission-enabling technologies for in-orbit satellite services, needed an Earth-based robotic testbed to accurately emulate satellite dynamics. Simulating complex interactions and ensuring precise, sub-millimeter control was critical for validating their missions safely. Creating a digital twin with Vention helped Lúnasa de-risk their project by enabling simulations to validate robot arm reach and movement before physical deployment.
Limitations and Challenges of Digital Twins
Digital twins have an immense potential to enable rapid iteration and cut down time-to-deployment. However, they aren’t without their limitations or challenges. Here are some of the important considerations to take into account when developing a digital twin.
1. Partial Digital Twins of Interconnected Systems: When the digital twin interacts with another physical system without any digital counterpart of its own, it can lead to inaccuracies and unpredictable behavior. For instance, if a palletizer relies on a conveyor that brings boxes at random, and the operator lacks data or control over its physics and operation, it’s difficult to accurately replicate its behavior within the digital twin.
2. Integration with External Software: On the software side, integrating external systems that perform specific calculations or logic (e.g., a quality check sent to an external PC for the next step decision) can be challenging if that software cannot be integrated into the digital twin’s environment. As a result, any simulation of the system’s behavior may need several assumptions, reducing the accuracy significantly.
3. Mismatched Controllers in Real and Simulated Systems: If a system designed and simulated with a specific control system (like Vention’s MachineMotion AI) is then deployed with a different real-world controller, the direct connection and behavioral similarity between the digital and physical twin can be lost. While the mechanical behavior might still be understood, the programming logic will differ, making the digital twin less representative of the real system’s operation.
The Future Trends and Evolution of Digital Twins
Recent advances in AI models and cloud-based manufacturing platforms are improving the accuracy of digital twins. The following three trends highlight how these developments will help manufacturers validate and deploy automation more efficiently.
AI-Powered Predictions and Optimizations: AI will essentially become a ‘prediction engine’ for digital twins, significantly enhancing their predictive capabilities. This will allow manufacturers to gain insights into whether the system will deliver the results expected out of it. AI will also enable digital twins to test all possible paths for specific tasks to find the optimal solution in the virtual world and then seamlessly transfer that knowledge to the real world.
Continuous Feedback Loop: Once a system becomes operational, the digital twin can use real-time operational data to recreate and debug issues that occur in the physical world. This will enable continuous learning and improvements without stopping the physical system, ultimately reducing the gap between simulation and reality.
Fleet Management and Cross-System Learning: Digital twins will reach their full potential when they can be applied across multiple machines and facilities. Insights from one system can improve others throughout the organization, fostering cross-functional communication and efficiency gains. A detected pattern or improvement in one palletizer can be replicated across dozens of others, multiplying the benefits exponentially.
Digital twins are more than simulations. They form the foundation of a future where design, validation, and operation become fully integrated, intelligent, and continuously optimized. Manufacturers that use digital twins in their daily operations deploy systems faster, cut downtime, and achieve higher equipment performance, turning efficiency gains into competitive financial growth.
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