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.
Realization: The Digital Production Twin
A smart factory is a fully digitalized factory model representing a production system — a digital twin for production — that is completely connected to a product lifecycle management (PLM) data repository via sensors, supervisory control and data acquisition (SCADA) systems, programmable logic controllers (PLCs), and other automation devices. In manufacturing, a digital twin enables flexibility to reduce time needed for the manufacturing process and system planning, as well as for production facility design. Breakthrough strengths and key enablers of smart factories are additive manufacturing, advanced robotics, flexible automation, and virtual and augmented reality.
Conventionally, designers and manufacturers work independently in different systems and throw information over the wall. This can create problems as information gets out of sync, making it difficult for everyone to see the same picture. As a result, teams are able to assess performance and make necessary adjustments only at the late stage of a physical prototype. Issues discovered this late in the process can cause delays in production and significantly increase the cost to fix.
Additionally, these errors can be transferred into assembly and installation instructions, which end up on the shop floor or in the field. This not only makes the product more difficult to produce the way in which it was designed, but also can negatively impact the quality of the product itself.
Using a digital twin in manufacturing offers a unique opportunity to virtually simulate, validate, and optimize the entire production system to test how the product will be built in its entirety using the manufacturing processes, production lines, and automation in place. The process logistics can also be incorporated into the digital production twin to aid teams in designing an effective sideline logistics solution to feed the production lines.
Utilization: The Digital Performance Twin
The digital performance twin of factory assets in operation, and products in service, closes the loop between expected performance and actual performance. With IoT, companies can connect to real-world products, plants, machines, and systems to extract and analyze actual performance and utilization data.
Data analytics can then be used to derive information and insights from the raw data. These actionable insights can then be applied to close the loop with the digital product twin and digital production twin to optimize products, production systems, and processes in the next cycle of innovation. Collected and analyzed, this information could also uncover product issues before they occur, identify potentially problematic configurations, and help fine-tune operations.
Closing the loop on digital manufacturing enables companies to incorporate their customers’ voices and trends into the product innovation cycle, which not only can speed time to market, but also help companies predict shifts in the market.
The manufacturers who will succeed in the evolving digital world will be the ones to harness the intelligence being produced in real time to get innovation at higher quality to market faster than their competitors.
This article was written by Jim Rusk, Senior Vice President and Chief Technology Officer (CTO) for Siemens PLM Software, a business unit of the Siemens Digital Factory Division, Milford, OH. For more information, visit here .