What are Digital Twins Video Interview
Digital Twin Interview with Action Engineering
The digital twin interview is with Jennifer Herron, Founder & CEO, and Rhiannon Gallager, Chief Social Scientist, at Action Engineering.
Action Engineering helps organizations large and small achieve their Model-Based Enterprise goals by motivating stakeholders, delivering training, and providing business planning and implementation consulting services.
What is a digital twin?
A digital twin in a cross-industry sense is a digital representation of a thing. Digital twins can be anything from a digital representation of a building to a factory machine to a human heart. The term is beginning to take on a life of its own outside of the traditional manufacturing definition.
In engineering and manufacturing, a digital twin is a digital representation of a physical part, product, or machine with relevant data to replicate real-world patterns or behaviors.
What is the purpose of a digital twin?
We cover real-world digital twin examples in our Industry 4.0 article.
Reasons for digital twin implementations:
- Predictive Maintenance
- Catastrophic Failure Analysis
- Diversifying the Supply Chain
- Onboarding in Manufacturing
- Capturing Engineering Tribal Knowledge
In the design phase, engineers use frameworks like MBD (Model-Based Definitions) to add as much relevant data to the engineering models as possible. This includes data like PMI (Product Manufacturing Information) using techniques like GD&T (Geometric Dimensioning and Tolerancing).
Here’s what Jennifer Herron, Founder of Action Engineering, has to say, “we want to be able to capture all the engineering requirements for each of the components in an assembly.”
It’s also important for engineers to use component digital twins from suppliers. These enable engineers to understand the design intent for the parts they spec from suppliers and capture important metadata to run processes downstream from engineering.
Digital twins can simulate prototype products in a digital environment. These prototype simulations enable design engineers to immediately simulate how a product will operate in the real world.
For example, a shampoo bottle manufacturer can simulate how shampoo flows through the bottle cap with differing pressure levels on the bottle. Tests can be performed by engineers virtually, and changes made to the bottle in a matter of hours. Without digital twin technology, a physical prototype has to be made for each design change and tested.
Digital testing can shave months off of the time to market for new products and product revisions.
The fabricator creates 3D instructions and in-process models that show how the part is built during the fabrication process. This information enables easier scaling and diversification of the fabrication process.
During the inspection process, inspection data gets added to the model along with engineering requirements. Inspection data is a window into the future performance of the part or product. Having inspection data included in the digital twin enables engineers to predict the future.
Combining all the data from each phase into the digital twin enables downstream processes like predictive maintenance.
Digital twins are used in manufacturing for predictive maintenance. For example, GE Aviation creates a digital version for each of the engines they manufacture. Sensors on the engines collect flight data and stress on many parts throughout the engine. This enables GE Aviation to predict when parts will wear out and schedule maintenance for each engine.
Digital twins for predictive maintenance are becoming more common in machine design and manufacturing equipment as well.
Catastrophic Failure Analysis
Combining data through design, simulation, fabrication, and inspection within a digital twin enables predictions of catastrophic failure and provides better safety measures and planning in case of a catastrophic event.
Diversifying the Supply Chain
OEMs (Original Equipment Manufacturers) achieve the best pricing and exceptional reliability by diversifying the supply chain. If an OEM purchases parts from a single supplier, they risk a single point of failure.
By leveraging digital twins and model-based definitions (MBD), OEMs can take the same part to multiple manufacturers, limiting points of failure and eliminating the chance that two suppliers will interpret drawing differently.
Capturing Engineering Tribal Knowledge
Another reason to use digital twins is to capture engineering tribal knowledge. Many senior engineers are nearing retirement age. They have much knowledge tucked away in their brain that will be gone as soon as they leave or retire. By implementing this digital technology, tribal knowledge can be applied within 3D CAD models and associated documentation for training and use by less experienced engineers.
What are the challenges of implementing a digital twin strategy?
Change is inevitable. The energy around Industry 4.0 technologies is palpable. So why is it so hard for companies to embrace and implement digital twins?
Rhiannon Galagher, the Senior Social Scientist at Action Engineering, said, “I think (companies) drastically underestimate the complexity of the change.”
Many companies focus on the technology around digital twins, but the real implementation challenges are people problems. Companies often overlook the ominous void of personnel skills that must be bridged before the desired future state is possible. Digital twin implementation will remain a pipe dream for companies that fail to account for challenges the people responsible for using technology face.
Simple questions like, “have you used a computer before” can reveal critical skill gaps that must be addressed within the digital twin roadmap.
There is a J curve when implementing new technologies. Productivity almost always sees a dip before the improvements are realized. This is a scary prospect, but the rewards vastly outway the short-term loss in productivity.
Companies must plan for the J curve dip and be ready to respond to criticism when the plan isn’t showing results immediately. Part of limiting the negative effects of the J curve of implementation is to properly document the current state before moving to the future state. Everyone wants the future, but it requires excellent documentation of present processes to achieve desired results.
What are some practical steps companies can take to implement digital twin technology successfully?
Here are some practical steps for digital twin implementation:
- Get clear on your goals and objectives
- Define a realistic timeline
- Prepare to change current processes
- Plan a non-production pilot or multiple pilots
Use the story from a successful pilot to get enterprise-wide buy-in. This isn’t only the best practice for digital twin implementation but has value with nearly all digital transformation initiatives.
If you are looking to implement a digital twin project or are interested in getting help with a model-based enterprise initiative, you can find out how to get Action Engineering involved early in the process on their website www.action-engineering.com.
Learn how you can offer CAD that is ready for the future. Engineers need data for IIoT, programming, and factory automation. They require more data from suppliers. Set your components apart by giving engineers the CAD data they need.
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