Unternehmen & Trends 1/2024

31 supplying the perfect number of parts just in time. This all happens in real time so that shop floor and production managers can respond directly to any changes or bottlenecks. Artificial intelligence (AI) additionally enables predictions, allowing manufacturing and assembly to act rather than react. Digital twins: key for data exchange A digital twin is the digital copy of a physical asset in the real factory and enables its simulation, control and optimization. Using digital tools, products as well as machines and their components are modeled into digital twins, including all geometric, kinematic and logic data. Germany’s Plattform Industrie 4.0 (Industry 4.0 platform) working groups are discussing digital twins in connection with the so-called Asset Administration Shell (AAS). Digital twins will be further developed in the coming years. One thing is clear: this is no monolithic data model but rather various aspects of digital representations, functionalities, models and interfaces, so-called sub-models. Many of these digital twins are currently being set up in the industry to record and map the behavior of machines and components, for example. From the viewpoint of industrial production and its engineering, digital twins comprise a number of aspects, including:  M odel-based self-descriptions with the aim of auto-identification and auto-configuration so that machines and their components can use supplied driver information to log on to the MES system or the industrial IoT system with their skills and services (“PLUGandWORK”).  D escription of the “skills” of production systems, such as their ability to conduct turning, drilling, milling or MAG welding tasks or material flow functions, e.g., lifting or continuous conveying. Skills also comprise attributes and their permissible value ranges and, if applicable, parts of the logic. These descriptions and corresponding sequence descriptions can be used to quickly combine production equipment into systems for new manufacturing tasks, including configuration and commissioning.  D ata-based models of the normal behavior of a machine, a production line or entire production setup, based on runtime data, which is gained during real operation, e.g., collecting machine data with the help of machine learning. Digital twins can be used to predict machine or component failures and — in the future — generate automated, data-based suggestions for improvement. Consequently, MES functions are essential for building digital twins. They record and store product, process and resource data supplied by machines and systems or measuring systems in the form of quality data.  O ffline and online simulations, including special simulators, e.g., for finite elements, virtual commissioning or simulation of physical processes. In an ideal case, different simulation models interact with each other. In the past, the term “digital twin” was synonymous with simulation, but this definition would be too narrow nowadays.  T he digital factory, which describes a “comprehensive network of digital models, methods, and tools (...) integrated into a continuous data management system,” e.g., for production and material flow systems, buildings and technical building services (according to standard VDI 4499, Blatt 1, of the Association of German Engineers (VDI)). The term “digital factory” is well known and has been described in relevant standards, such as the VDI 4499 standards.  C omplete digital twins also include IT security, access rights, certificate handling, version management and compatibility tests of different digital twin versions.  L astly, the exchange of digital twins between companies that are part of data ecosystems requires interoperability based on open standards. Projects such as Catena-X and Factory-X have impressively demonstrated this in practice. Digital twins are essential for Industry 4.0 and the further digital transformation of manufacturing. Their content is created at different stages of the product or factory life cycle, using various tools on different platforms. Practical examples have already shown that the definition of digital twins must be application-specific and tailored to each company. Recent examples of the use and benefits of digital twins are provided by manufacturers and suppliers in the automotive industry, who have always been at the forefront of deploying digital factory tools and systems or identifying and tracking parts and vehicles. Specific projects include digital twins of products, also in the form of completely digitalized product life cycle records, or production systems. In the case of systems, the aim is to correlate quality data with process parameters so that process parameters can be readjusted in the event of non-conforming parts. This also involves suppliers, as OEMs demand more data on products and processes that will allow them to optimize their own processes. One example: data on coils that are processed into blanks and deep-drawn parts in the press shop. The more detailed the measurements provided by the steel manufacturer, the easier it is to optimize the deep-drawing parameters in order to prevent or even predict cracks or other non-conforming characteristics. Embrace cooperation! In addition to traditional hardware-related expertise, factory operators and their suppliers should therefore waste no time start learning and getting to grips with the broad range of skills needed to implement and make effective use of new methods and tools like Gaia-X, as well as new platforms, data ecosystems, and data security and data sovereignty systems. And this is not something they will succeed in doing on their own; partners will need to work together to make up for the current gaps in knowledge. The facility to share data securely stimulates cooperation and innovation within the ecosystem, making it possible to implement new business models in a commercially effective way that would not have been viable previously. Data ecosystems are based on enabling secure and transparent access to data for all authenticated ecosystem participants. Data space initiatives such as Catena-X or Factory-X pursue a clearly defined goal of developing “fitness programs” for the digital transformation of Europe medium-sized manufacturers and thus strengthening the continent’s overall status as a business hub. Industrie 4.0-Blog Dr. Olaf Sauer