Rapidly advancing technology and groundbreaking innovations are changing the world of manufacturing. Trends such as Big Data, Cloud Technology, and the Internet of Things (IoT) are just some of the tools fueling a digital transformation that is impacting how products are developed, manufactured, and used across all sectors of the manufacturing industry. Harnessing the power of emerging technologies is key to successful, continuous innovation.
While transitioning can be a struggle, companies that embrace digitalization have the potential to not just survive, but also thrive in, and even disrupt, the market. Some examples of how new technologies are already transforming industries are:
Transportation vehicles that understand their environment and operate autonomously.
Medical implants designed and manufactured to the needs of a specific individual.
Aircraft that operate without pilots.
Energy systems that understand how to optimize themselves to minimize consumption.
In the context of developing and manufacturing complex smart products, digitalization begins with creating a digital model. This digital model, or digital twin, should describe, define, capture, and analyze how the product is expected to perform. The digital twin is often described as a digital replica of different assets, processes, and systems in a business that can be used in a number of ways.
While this generic definition is basically correct, a comprehensive digital twin consists of many mathematical models and virtual representations that encompass the asset’s entire lifecycle — from ideation, through realization and utilization — and all its constituent technologies.
Ideation: The Digital Product Twin
Companies today deal with greater competitive disruption on a global basis. To deal with these challenges, it is imperative that companies transform their engineering, design thinking, and processes practices. Integrated software tools such as CAD/CAM/CAE can enable companies to truly digitally represent the entire product in both mechanical and electrical/electronic disciplines.
Creating a 3D model with a CAD system is often the first thought to come to mind when talking about a digital twin; however, the digital product twin is actually a complex system of systems, including all the design elements of a product. This can be created using a Systems Driven Product Development (SDPD) methodology, which drives the creation of intelligent 3D models built with generative design practices and validated through predictive analytics.
SDPD brings together core elements of the design process including intellectual property, configuration, and change control with elements from systems engineering; mechanical, electronics, and software design; and multi-domain modeling and simulation. SDPD also supports interfaces and integrations with domain-specific tools.
Beginning with requirements and ending with integrated designs showing verification status, SDPD provides end-to-end traceability. It can also significantly increase reuse of proven models and simulations, which can improve quality. Additionally, it promotes rapid assessment of change impacts and early discovery of issues to improve schedule performance and product development times.
A product digital twin will typically include electronics and software simulations; finite element structural, flow, and heat transfer models; and motion simulations. This allows a company to predict the physical appearance of a product, as well as other factors such as performance characteristics. They rely heavily on predictive engineering analytics, which combines multidisciplinary engineering simulation and tests with intelligent reporting and data analytics. These capabilities lead to digital twins that can predict the product’s real-world behavior throughout its lifecycle.
This comprehensive computerized model of the product enables almost 100 percent of virtual validation and testing of the product under design, which minimizes the need for physical prototypes, reduces the amount of time needed for verification, improves quality of the final manufactured product, and enables faster reiteration in response to customer feedback. For example, the digital twin of an aircraft can be tested to see how it will respond through a number of environmental conditions, helping to predict potential failure modes under a wide set of conditions.
Another example is the automotive industry’s development of road-safe autonomous vehicles. It would be impossible to test vehicle reliability using physical testing and imprecise analytical models because there are an infinite number of combinations to test when considering environmental conditions, other vehicles, pedestrians, and traffic signs. It is estimated that physical testing would require 14 billion miles of testing — the equivalent of running 400 prototypes in parallel for 100 years at 40 mph for every hour of the year. A digital twin of the vehicle could enable testing to be completed through simulation, leading to safer vehicles, faster.