Article from Kunal Suri, Researcher at CEA
Digital twins are currently making the headlines in various industrial domains such as manufacturing, infrastructure, natural resources, healthcare, energy, aerospace and defence, just to name a few.
According to the latest domain independent definition released via the Digital Twin consortium, a digital twin is defined as follows:
“A digital twin is a virtual representation of real-world entities and processes, synchronised at a specified frequency and fidelity.
- Digital twin systems transform business by accelerating holistic understanding, optimal decision-making, and effective action;
- Digital twins use real-time and historical data to represent the past and present and simulate predicted futures;
- Digital twins are motivated by outcomes, tailored to use cases, powered by integration, built on data, guided by domain knowledge, and implemented in IT/OT systems.”
The Industrial Internet Consortium (IIC) goes a step further into domain specific area related to the digital twins for industrial applications. They point out that the information held within a digital twin is a combination of the following categories (but not limited to them only):
- physics-based model and data;
- analytical models and data;
- time-series data and historians;
- transactional data;
- master data;
- visual models and computations.
In layman’s terms, in the manufacturing domain, a digital twin is the digital replica of a real-world factory entity such as production equipment (machine tool, robot, etc.).
Via a set of correlated digital models and supporting data, it provides cohesive information about their components, product, process or entire production life cycle. Hence, a digital twin is a very powerful tool that will allow you to predict problems and prevent downtime, which leads to increased productivity and reduced costs.
1. How Digital Twins can make production lines more flexible
The end goal for the DIMOFAC project is to provide an industrial solution to make factory production lines more modular (i.e. easier to reconfigure): a must if manufacturers want to adapt quickly and in a cost-effective way to an increasingly personalised demand as well as to changing market dynamics (which is particularly relevant in pandemic times). You can read more information about the purpose of the project here.
The 6 pilot lines (see Figure 1) involved in DIMOFAC, that will demonstrate the effectiveness of the solution, share some commonalities but also show some differences in terms of both production and decision-making needs. For instance, all the 6 pilot lines need to exchange information between the machines on the shop floor and their internal Manufacturing Execution System (MES), wherein each of such machines will have their own Digital Twin which will interact with the MES that orchestrates these machines. Thus, if a new machine is needed to be added to the shop floor, the onsite engineers will just need to make the Digital Twin of the new machine to talk to the APIs provided by the existing MES. Additionally, this new machine will be automatically registered to the Digital Twin of the production line and will be taken into account during any simulations in the future.
Figure 1: Different Pilot Lines in DIMOFAC
The variety of these needs can be addressed with the use of digital twin-related technologies such as modelling and simulation along with gathering inference from real-time data and/ or historical data from the modules (or assets) in the production line.
In other words, the use of digital twins will provide a common environment for managing the different pilot lines.
2. How Digital Twins are going to be used in the framework of DIMOFAC
As stated before, a digital twin comprises a set of correlated digital models and supporting data that provides cohesive information about the assets of a factory (i.e., components, product, process or entire production life cycle).
In DIMOFAC, various representational models and/or computational simulation models are used. Some of them are 3D Computer-Aided Design (CAD) models of the parts produced and mathematical equations and physics-based models to perform simulation such as plastic flow in a mould and its thermodynamics–related properties.
In parallel, a set of functional process models will be used to optimise the resource allocation and usage of the entire production line.
These set of correlated models, simulation results and other relevant data about the modules will enable the stakeholders to make informed decisions about the production process in real-time. Moreover, the DIMOFAC project will make use of upcoming standards for Digital Twin technologies such as Asset Administrative Shell (AAS ) from the Plattform Industrie 4.0, to allow the possibility of using any standardised tool that may come in the future.
3. What industrial benefits will Digital Twins yield for manufacturers
The digital twin will provide a virtual representation of the physical modules, objects and/or systems. Overall, the Digital Twin will help as follows:
- Enable early verification for both products and processes
- Fast reconfiguration and setting time, which are a direct result of the simulation tools that allow users to get a better prediction of how the product will perform under everyday use
- Automatic path planning for robots / Automated Guided Vehicles (AGVs) and automatic process parameters based on digital twin simulations
- Traceability assurance for requirements across the entire production process
- Offering a software environment that provides digital and visual support for work preparation and quality control, which in turn helps to improve turnaround time*.
In other words, thanks to the use of digital twin technology, the pilot lines in the DIMOFAC project will see a reduction in time and cost of production line reconfigurations and adaptations of modules and processes. It will act as a very important piece to solve the puzzle that assist the achievement of the overall KPIs of the DIMOFAC project, which are:
- More than 20% decrease in reconfiguration time
- At least 12.5% increase in resource efficiency
- At least 15% reduction in overall cost of production
- More than 10% increased yield improvement.
*The turnaround time refers to the amount of time taken to complete a process or fulfil a request.