Subscribe and listen to AW’s podcast!
Subscribe and listen to the Automation World Gets Your Questions Answered podcast!
Listen Here

Digital Twins for Discrete Manufacturing Applications

Discover how the implementation of the digital twin is contributing to an immediate and significant impact on how companies implement technologies that enable smart manufacturing.

D Slansky Web 5e4b044f96bb4

Multiple technologies have emerged in recent years that are instrumental in driving the advance of smart manufacturing and the Industrial IoT (Internet of Things). These include advanced analytics, artificial intelligence (AI) and machine learning (ML), operational intelligence, advanced robotics, cyber-physical systems, and generative design for additive manufacturing. While each of these technologies is changing the face of manufacturing today, ARC Advisory Group research suggests that the Industrial IoT, connected smart assets, and the digital twin are having the most immediate and significant impact on how companies implement technologies that enable smart manufacturing.

How the digital twin is being implemented today
An integral component of a digital twin of a production system is the virtual model of the real-world products, assets, and processes. Virtual modeling provides manufacturing engineers with the ability to simulate and model the virtual and the physical, simultaneously or separately. This digital twin system modeling approach enables them to understand the holistic nature of their assets and production systems within the overall manufacturing ecosystem. Further, we are seeing the emergence of powerful digital twin development tools offered by suppliers that will enable manufacturers to understand exactly how their factory systems and equipment function, and enable them to make decisions to enhance performance and product quality through human and artificial intelligence.

Digital twins can be applied to discrete manufacturing ecosystems in three distinct areas: product, production, and performance.

The product digital twin is used to enable more efficient design and improve the product. In some cases, the product is the actual equipment and assets used in the production system. Virtual simulation modeling can validate product performance, while simulating how the product is currently behaving in a physical environment. This provides the product developer with a physical-virtual connection that allows them to analyze how a product performs under various conditions and make changes in the virtual design model to ensure that the physical product will perform as designed in the field. This eliminates the need for physical prototypes and reduces development time.

Production digital twins are used in manufacturing and production planning. They can help to validate how well a manufacturing process will perform on the shop floor before the physical production equipment and work cells go into actual production. Today, the virtual commissioning of production automation —an established technology and process—is merging with the more expansive scope of the digital twin. Virtual commissioning is typically a one-time validation of an automated production system. In contrast, the digital twin represents an ongoing analytical and optimizing process that takes place in real time. By simulating the production process using a digital twin and analyzing the physical events across the digital thread, manufacturers can create a production environment that remains efficient under variable conditions.

Performance digital twins are used to capture, analyze, and act on operational data. An important initial step when developing and implementing a digital twin is to identify the exact operational configuration of the product, asset, or production equipment that represent the physical components.

Context and configurational data required
When implementing, user companies need to include context within the digital twin configuration. For predictive analytics or industrial IoT to be effective, the context (physical configuration) of the asset and system are required to know exactly what is needed to collect the relevant operational and performance data. Companies implementing any digital twin project should begin by capturing and managing the actual physical configuration of the asset. Additionally, due to the many use cases for a digital twin across the product lifecycle, implementers would be well served to employ digital twin technology that can integrate a flexible/ dynamic data model.

The operational element of the digital twin aligns closely with concepts and technologies associated with industrial IoT. While virtual CAD models and product performance simulations define the fit, form, and function of the product, the real-time and operational data is the digital output of the physical assets in operation. The information is captured through sensors, industrial IoT endpoints, and intelligent edge devices in real time. This connects the digital twin to physical reality. Combined with the various forms of contextual data, this knowledge provides a foundation for insightful and timely decision-making, leading to process improvement and optimization.

Recommendations
To realize meaningful benefits when implementing a digital twin for smart manufacturing, organizations must think holistically. Successful implementation is much more than engineering design models or mechanical and electric components of a production work cell. The entire digital twin system must be modeled based on the virtual and physical elements and the desired output and results. For a digital twin, analytics, and operational performance to be effective, the context of the product or asset within the system is required. The ultimate benefits of a digital twin can be quantified by understanding up front that physical configuration is just as important as virtual design.

Discover New Content
Access Automation World's free educational content library!
Unlock Learning Here
Discover New Content
Test Your Machine Learning Smarts
Take Automation World's machine learning quiz to prove your knowledge!
Take Quiz
Test Your Machine Learning Smarts