Industry 4.0, often known as the Internet of Things (IoT), is supposed to help the manufacturing landscape undergo a massive overhaul to improve revenues, optimize worker productivity and increase operational agility to meet demanding market conditions. The “digital twin” concept is becoming a part of this productivity improvement and decision making process by connecting the silos between digital data. But part of the challenge with this concept is that industry leaders define the concept differently.
The digital twin was first introduced and clearly defined by Dr. Michael Grieves in 2003 at University of Michigan. CAD technology companies like PTC and Dassault Systemes, for example, perceive it differently to address the key concerns of their current and future customers.
To clear the air, the basic concept of the digital twin model is to build rich digital information for virtual products; digital information that is indistinguishable from the physical counterpart. This digital information will serve as a “twin” of the information embedded within the physical product or system itself and will be linked to it throughout the lifecycle of the system.
The digital twin concept model as defined by Dr. Grieves consists of three main parts: physical products in real space, virtual products in virtual space and the connected data that tie the physical and virtual products together.
Digital twin benefits
The concept of digital twin remains beneficial in many ways. It eliminates the use of symbols or numbers extracted from the visual information for conceptualization. Instead of looking at the factory report, the digital twin simulations let users directly see the progress as the product moves along the manufacturing stages. Comparing the digital and physical product becomes easier as the twin model tracks the progress of the physical product development directly, and clearly indicates deviations from the idealized processes.
The most powerful benefit of digital twin however is in collaboration. Tracking the state of the physical product under the development through a replicated digital model lets individuals monitor the performance from anywhere.
Connecting the digital silos
The first step to implement the digital twin concept requires 3D models, not 2D drawings. The 2016 Worldwide CAD Trends Survey by Business Advantage shows that two thirds of users surveyed out of 610 still rate 2D drafting as highly important. 39% of design work produces 2D drawings, 27% 3D models and 34% both 2D drawings and 3D models. 2D drawings automatically generated from 3D CAD models are important as is more software development on 2D drawing capability.
While large corporations can demonstrate proof of concept, this proof will not be enough to realize economic gains. A digital twin will be required across entire supply chains. The challenges here involve globalization, new manufacturing techniques and liberalization policies. Managing all these design data for digital twin amongst partners and suppliers as the physical product evolves will be a challenge.
For a successful implementation of a digital twin concept, the key is to first assist and support small suppliers in adopting a digital approach. Looking at the survey data mentioned earlier, there is a need for organizations to transform their design operations to completely 3D and get rid of 2D drawings. The Digital Manufacturing and Design Innovation Institute (DMDII) is making efforts in this direction through a U.S. federally funded R&D organization that recently issued a project call to demonstrate technologies for digital twins from supply chain participants.
To unlock the real value from the digital twin concept requires a holistic approach to store, manage and manipulate the digital data of the product. There is also a need to have a robust engineering change management process in place to ensure that the digital twin accurately maintains the virtual and physical configurations.
Small-scale manufacturers and suppliers need to become aware of this concept and initiate processes towards digitization, and convert design information to digital models to successfully compete in the market.
Filed Under: 3D printing • additive manufacturing • stereolithography, IoT • IIoT • internet of things • Industry 4.0