Digital Twin Success for Machine Builders: Part Two
Interacting with Models: Challenges and Opportunities
In our first article of this series, we discussed the benefits of digital twins for both machine builders and their customers.
In our next discussion, we will explore strategies for finding an ideal model for your specific use case and share innovative approaches to make the most of these models. We’ll discuss insights that will transform your machine-building strategies and elevate your organization’s digital transformation.
In the world of engineering and digital simulations, lifecycle management is key to ensuring that models can be leveraged across various applications over time. Any discussion of an ideal model would not be complete without a deep dive into the topic.
TwinOps represents the evolution of lifecycle management strategies for digital twins. Initially, DevOps integrated development and operations to enhance software delivery. This approach evolved into MLOps, which incorporated model-based engineering. TwinOps further extends these principles by combining DevOps practices with digital twin technology, enabling continuous integration, deployment, and validation of cyber-physical systems.
Organizations that have struggled with their digital twin strategies often limit their models to single-use cases without a dedicated life cycle management strategy, hindering the twin's ability to scale and access value for future revenue generation.
A common issue is that companies create models for specific purposes—such as material flow modeling, a specific machine model, or a multi-physics process model—and then struggle to maintain them. When they need these models one to two years later, they often find the effort to update these models difficult to overcome. This underscores the importance of managing models throughout their lifecycle to ensure their relevance and usability. Organizations should develop strategies to create adaptable models that fit a wide range of use cases.
Different industries and use cases require models with varying levels of detail and features. For machine builders, for instance, models used for product development are often highly detailed, whereas those designed for virtual commissioning focus more on functionality and integration.
It’s about finding the right model for the right problem.
Let’s dive into three different digital twins progressing from basic to more advanced and see how these can be leveraged by your organization.
Credit: Milwaukee Contactor Line, Modeled in Emulate3D, rendered in NVIDIA Omniverse
Sales & Marketing: Enhancing Representation
For OEM or machine builder sales and marketing teams, a compelling model can showcase potential improvements, such as increased output efficiency.
Key elements include:
- Visualizing the solution: Helping stakeholders understand the impact of proposed systems.
- Proving feasibility: Providing evidence that the solution works under realistic conditions.
- Demonstrating ROI: Quantifying benefits, such as increased production or efficiency gains, with clear evidence.
Credit: Cathryn Yong, Indicon Corp.
Virtual Commissioning: Reducing Time and Risk
For commissioning engineers or deployment teams, virtual commissioning models serve to test control code, software and system integration before physical implementation.
This approach helps:
- Reduce commissioning time and risk: Identifying and addressing issues before deployment.
- Facilitate integration: Supporting software control testing across multiple OEMs.
- Enhance collaboration: Enabling cooperation between different machine builders without exposing intellectual property (IP).
- Ensure security: CAD models, control systems, and software elements can be encrypted or hidden to protect proprietary data.
In our next blog, we’ll go deeper into how to share value while protecting IP.
Credit: Rockwell Automation Semiconductor Modeled and Rendered in Emulate3D
Product Development: High Accuracy and Simulation Maturity
Simulation has been a cornerstone of product development for years, with thousands of available tools, including hundreds from Ansys. Product development models are designed to be highly accurate, supporting dynamic testing of static designs.
The benefits include:
- Accelerating product development cycles: Reducing time to market by identifying issues early.
- Improving quality and performance: Enhancing reliability and efficiency through robust simulations.
- Enhancing 3D emulation: Supporting machine builders in testing and refining their designs.
Each of these use cases demands a different model scope, tailored to specific needs and objectives.
Approach to Developing Adaptable Models
To ensure adaptability, a structured approach is essential:
- Design: Document the model’s purpose and define the types of experiments to be conducted.
- Develop: Create the model with flexibility in mind, ensuring it meets a variety of use cases.
- Test: Validate performance and refine the model to improve accuracy and reliability.
Lifecycle management is also crucial for ensuring that digital twins remain accurate and useful throughout their existence. It involves overseeing the creation and maintenance of the digital twin, ensuring that it evolves in parallel with its physical counterpart.
As we discussed earlier, TwinOps plays a crucial role in this lifecycle management by providing the infrastructure to design, produce, maintain and monitor digital twins.
It ensures collaboration across multidisciplinary teams, meeting engineering, regulatory and cybersecurity requirements. By leveraging this, organizations can improve the quality of changes and maintain synchronization between the twin and the physical system, enhancing overall performance.
Remember, lifecycle management is not a one-time task but an ongoing process.
By using the digital twin to test changes prior to production deployment, organizations both improve the quality of their changes, and maintain synchronization between the digital and physical system.
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