The Digital Twin
Making complexity possible through engineering simulation
The Digital Twin: Making complexity possible through engineering simulation
We’re living in an increasingly complex world. Every day, there are innovations and new systems and designs that require companies to adapt and evolve their products and processes. But, as their designs and processes get more complex and interconnected, how do they keep up and compete?
The solution comes down to one thing: Innovation.
In order to compete, companies need to find a way to reduce time to market and bring production costs down while still catering to the growing consumer need for more personalized products.
This has led to companies increasingly needing to create smarter, more intelligent products. But these products require a complex design and manufacturing process that can no longer be done in isolation. Their production has become part of a larger system which requires a far more complex development process. And this process needs to evolve and optimize continually to maintain its relevance in the current market. Trying to reduce that complexity will only limit a company’s ability to innovate. So, they need to find a way to manage and deal with the complexity.
This is where the digital twin comes in. According to a Deloitte study, the global market for digital twins is expected to grow by 38% CAGR to reach $16 billion by 2023. More and more companies are using a simulation-driven approach to create products and processes to manage the growing complexity of new trends.
What is a digital twin?
A digital twin is a virtual representation of a physical process or product. It’s used to gain a deeper understanding of the physical counterpart to predict performance characteristics.
Across industries, digital twins are used to simulate, predict and optimize products and production systems before companies invest in physical prototypes and assets. Digital twins combine multi-physics simulation, data analytics and machine learning capabilities to demonstrate the impact of various scenarios, environmental conditions and other variables.
For example, a digital twin could create a model representing how a product behaves when used. Including how much energy it will use or how much noise it will make.
Testing is a critical component of designing the right product that meets all performance targets and industry compliances. By creating a digital twin, you remove the need for developing physical prototypes as you can do all testing in simulation. This results in quicker development time and improved quality of the final product or process design.
How do they work?
Sensors are installed on physical objects that the digital twin then uses to determine the object’s real-time performance, operating conditions and changes. It then uses this data to evolve and constantly update to reflect any changes happening with the physical counterpart throughout the product life cycle.
This continual exchange of data creates a closed-loop of feedback in a virtual environment that companies can use to optimize their products, production line and performance—with minimal cost. Early in the design process, designers can evaluate possible design alternatives and quickly innovate to find the best design. It allows designers to do more “what if” modeling and analysis—opening the door to more innovation.
The three types of digital twins
The potential applications for a digital twin depend on what stage of the product life cycle it models. Generally speaking, there are three types of digital twins:
- Product: Used to virtually validate product performance by representing how the product is currently working in the physical world. Product digital twins help you navigate complex systems and materials to design more efficient products.
- Production: Used to help validate the efficiency of a manufacturing process before anything goes into production. Production digital twins help you optimize production and even predict when to do preventative maintenance.
- Performance: Used to capture, analyze and act on operational data to make informed decisions. Performance digital twins help you improve system efficiency and models while creating new business opportunities.
Over time, the three digital twins combine and integrate as they evolve. This process is known as the digital thread because they weave data from all stages of the product and production life cycle together.
The benefits of using digital twins
As digital twins become more capable, they’re being applied to many uses: from designing and testing products and processes to monitoring day-to-day operations and maintenance.
Taking an integrated approach to engineering through the use of digital twins is beneficial across industries.
A few key benefits include:
- Fast-tracks innovation
- Reduces production timelines
- Cuts down overall lifecycle costs
- Improves quality of the final manufactured products
- Aids in designing more efficient processes
- Optimizes designs to meet industry-specific standards
- Enables faster iterations in response to customer feedback
- Less material waste from multiple prototype developments
- Optimizes day-to-day performances
- Enables predictive maintenance
- Helps with planning for large-scale infrastructure changes
However, it’s important to note that adopting digital twins requires considerable investment, collaboration, and sustained commitment from all business functions affected. From revising criteria, workflows and feedback loops to tackling data-sharing, security and governance, leaders need to actively ensure that they’re making the best use of the technology. However, if done well, the long-term benefits are invaluable.
Breaking location barriers
Another significant benefit of using a digital twin is breaking down any previous location barriers. Businesses have gone from having one location to many across the world. Going digital means that key players within your company no longer need to be in the exact physical location. You can tap into the best resources worldwide without the added logistics that come with bringing them all physically together.
Designers across a team can access the same information, and any changes made to a product or process are automatically communicated and updated for all team members. This keeps everyone aligned throughout the design process.
Use cases of using a digital twin
As the technologies that enable digital twins become more cost-effective, more companies are starting to find ways to benefit from them. Digital twins are now deployed and used across multiple industries, including automotive, construction, architecture, manufacturing, healthcare and retail.
There’s no better example of a digital twin application than how the manufacturing industry uses the technology. Digital twins are revolutionizing the industry by empowering product development, predictive maintenance, unique design and large scale personalization.
During product development, engineers can quickly test the feasibility of a product before it’s launched or even gone for consumer testing. Getting these results early on allows them to fine-tune the design and produce more successful products.
Testing cars have become incredibly complex in the automotive industry due to the high regulations and compliance protocols required. Digital twins optimize that process and help inform future decisions through the support of real-world data. Engineers can test new vehicle concepts long before they make it to a physical testing track. For example, automaker Maserati has used virtual modeling and simulation to reduce the number of expensive, real-world prototypes and test drives, cutting vehicle development time by 30%.
Digital twins are also used in the healthcare industry, where researchers can’t always take new products or procedures and apply them without proper testing. It can also be used to help develop new strategies, such as optimizing hospital staff and processes to meet fluctuating demands. For example, during the initial stages of the COVID-19 pandemic, creating a digital twin of hospital operations helped operators examine performance and problems which they could then apply to the real world to better prepare and support hospitals.
In construction, digital twins are used to assist construction crews better plan out projects and understand how they’re progressing in real-time. The models created are used to inform engineers about what is and what isn’t going to work, allowing them to make those changes without delaying construction times.
On one project for CRB, a construction provider for the biotech industry, the use of digital twins reduced the number of onsite engineers from 10 to 1, reduced travel costs by 33%, and expedited design by three weeks.
In the energy and process industries, physics-based digital twins are used to determine optimal equipment designs before production. Engineers can capture the complexity of the industry and use it to explore design and operating spaces to find optimum conditions.
Making complexity possible through simulation
The virtual and physical worlds of design and manufacturing are moving closer and closer together. To remain competitive, companies have no option but to innovate and embrace the complexity that comes with innovation. Digital twins manage this complexity by validating the most complex products and processes.
Going digital can help make a company more agile and quicker to respond to complex market needs and customer demands. Having a real-world virtual model to extract actionable data has become invaluable. Digital twins are shaping our future and make complexity possible.