For mechanical and plant engineering, a simulation platform is a very high-performance tool for configuring entire plants, including the control system (PLC), at low cost using digital twins, and for testing and commissioning them virtually. For the companies, however, additional sources of revenue also arise by offering their customers the virtual components, plants and simulation models in addition to the real ones, or by using them themselves for new services.
From software-in-the-loop simulation ...
In recent years, the increasing complexity of machines and plants has led more and more to the fact that control software was only finally programmed on the real plant - i.e. during commissioning. With the development of digital twins and software-in-the-loop simulation (SILS), it has been possible to move this process back to the engineering phase: In this process, the virtual components, assemblies and controllers of a mechatronic system map their real-life models one-to-one, from the parameters to the interfaces to the behavior. Especially at the beginning of engineering, high-resolution simulation models for individual processes are advantageous in order to run through different designs or control processes in a cost-effective and uncomplicated manner. Hardware-in-the-loop simulation (HILS), on the other hand, is used to test real control hardware: it is connected to the digital twin via the industrial real-time fieldbus, which simulates the behavior of the fieldbus components in such a way that there is no difference to the behavior of real fieldbus components. Practical experience confirms that these digital test simulations significantly accelerate development processes and commissioning, contribute to quality improvement and reduce costs.
... to the simulation platform
However, when it comes to virtually commissioning an entire factory, it is not enough to simulate each individual process; their complex interaction must also be taken into account. It is necessary to simulate all interactions between the mechatronic processes, the control system and the operating behavior. To do this, you have to combine the various simulation models from the different disciplines - mechanical, electrical and software engineering - into one overall model. This is also referred to as coupled or multidomain simulation. The challenge lies not only in the fact that this simulation must take place in real time, but also in the fact that a greater model depth of the digital twins is required than before to represent physical effects, i.e., the model becomes much more complex. Therefore, it is imperative to increase the computing power that can be used in HILS. The solution: platform to real-time co-simulation, whose characteristic features are partitioning, parallelization, synchronization, data exchange and integration interfaces for blog-based modeling.
Real-time multicore simulation
Real-time computation and real-time synchronization is made possible by model partitioning: the building block-based system model is decomposed into different sub-models - across component boundaries. This can be done automatically by the system or manually by the user. Several computational kernels work in parallel and make it possible to couple a wide variety of simulation disciplines. These include the description of the respective process, the simulation of machine behavior, for example with regard to logic, kinematics and dynamics, as well as the behavior simulation of industrial control components including their sensors and actuators. The modeling environment shows the user how the overall model is distributed across the individual computational cores and allows him to make adjustments and reconfigurations. The platform is thus the basis for functional solutions that allow different models to be combined, regardless of whether they are SILS or HILS models.
Platform library combines knowledge of many users
A platform offers the highest added value if it also allows the inclusion of third-party simulations via standardized integration interfaces. As a kind of library for simulation solutions - tools and models - it should contain the virtual twins of common control systems, such as those from Siemens or Kuka, and also allow users to integrate their own simulations and those of partners. Then it will be possible for machine builders to simulate the hardware and software components of a plant continuously without a system break, right up to the virtual commissioning of entire factories. The highlight: A modular platform library grows continuously and maps more and more application scenarios. It also offers plant and machine manufacturers new business models with which they can contribute directly to value creation within the company. Four business opportunities are outlined below.
1. Dummy for training purposes
It is already common practice for machine builders to give the virtual twin to plant operators so that they can use it for training their personnel. The advantages are obvious: critical situations can be simulated on the computer that cannot be caused in reality, even for training purposes, for safety reasons or because production has to run. With training in the virtual world, however, operators can prepare themselves well for an emergency so that they can then react correctly and restart the plant as quickly as possible. The training via the online platform also increases the productivity of the plant - the plant operators can start immediately after the real commissioning of the machine, without having to first complete an "operator crash course" and blocking the plant in the process.
2. Predictive maintenance service
Systems must be serviced regularly. In most cases, this is done by the machine manufacturer or a specialized service provider. With a digital twin of the plant and the additional use of a predictive tool, maintenance companies can offer predictive maintenance to the customer as an additional service. This involves detecting when a problem is just beginning to develop. The virtual plant serves as a "golden reference" - if the parameters of the real plant deviate from this, then intervention may be necessary. The underlying predictive analytics model takes into account current operating data as well as process engineering correlations and relevant influencing factors of the plant periphery. By comparing this with historical data, e.g. through an integrated AI solution, it is possible to determine how the data is most likely to develop further, thus allowing countermeasures to be taken before damage occurs. Another business model based on predictive maintenance is that the service provider is not remunerated for the maintenance measures, but for guaranteed system availability. It is then up to the service provider to maintain the machine accordingly.
3. Compass to the best technical solution
Today, there are hardly any off-the-shelf machines. The rule is rather one-off production adapted to the customer's wishes. This is where the difficulty often lies for the customer - what is the optimum solution for him? The plant manufacturer can carry out a pre-project with the customer before the quotation phase, in which they run through various configurations on the simulation platform. The more digital components are already available in the platform library, the less effort is required. It is also to be expected that the buyer will then also commission this machine builder to implement the system.
4. Retrofitting digitally
Instead of building from scratch, it is worthwhile in many cases to modernize existing plants by replacing obsolete components or retrofitting them for a new product. Analogous to the engineering of a new development, the effort involved can be significantly reduced if the operator first carries out the retrofitting virtually on the simulation platform. The prerequisite for this is that his plant manufacturer provides him with the digital twins and simulation models for a corresponding fee.
An open and multicore-capable simulation platform enables co-simulations in real time. Designers can thus, on the one hand, carry out extensive test runs with different virtual control systems in order to find the optimal control system for the respective plant with relatively little effort and in a short time. On the other hand, an entire plant can now be continuously simulated and thus virtually commissioned. The platform should contain expandable simulation libraries that also allow highly specialized, technology-specific simulation solutions from third parties to be integrated via appropriate interfaces. It has also been shown that a common online platform that grows with the user is very crisis-proof. Keywords here are location-independent access, redundancy and knowledge transfer. New sources of revenue are also available to plant manufacturers - through the provision of digital twins, simulation models and services such as predictive maintenance.
Authors: Dr. Christian Daniel, Business Manager Simulation Technology; Dr. Christian Scheifele, Head of R&D Simulation Technology; ISG Industrielle Steuerungstechnik GmbH
Dr. Christian Daniel has been working as Business Manager Simulation Technology at ISG Industrielle Steuerungstechnik GmbH (www.isg-stuttgart.de) since 2013. His focus is on customer consulting and solution finding for sustainable optimization of engineering and production processes, for significant cost reduction with the help of simulation technology, and configuration management. He passes on his many years of management experience in plant and mechanical engineering as well as his success in the strategic development of companies to decision-makers in various industries. Previously, he held the position of Technical Director at Bystronic Lenhardt GmbH for thirteen years. After graduating with a doctorate in engineering, specializing in automation technology, from the University of Stuttgart in 1995, Daniel started his career as Chairman of the Board of FISW GmbH and subsequently served as Head of the Technology Department and as a senior executive at C. Haushahn GmbH.
Dr. Christian Scheifele has been Head of Research and Development Simulation Technology at ISG Industrielle Steuerungstechnik GmbH (www.isg-stuttgart.de) since 2019. Prior to that, he was group leader "Virtual Methods in Production Engineering" at the Institute of Control Engineering of Machine Tools and Manufacturing Units at the University of Stuttgart since 2016. Already since 2015 he worked in the field of digital factory with its modeling, methods and tools to develop, engineer, test and commission production processes and plants based on simulation and software. He is a scientific leader in simulation technology, as evidenced by numerous publications. Scheifele holds Dr.-Ing. and Master of Science degrees from the University of Stuttgart and a Master's Thesis from the University of Auckland.