Unternehmen&Trends - Ausgabe zur HANNOVER MESSE 2019

One significant feature of Industrie 4.0 is the consistent networking and penetration of all factory components and complete value-added chains with sensor technolo- gy, embedded systems and communication technology – which are called cyber-physi- cal systems. This results in large amounts of data, usually generated by machines, ranging from planning the products to be manufac- tured and the production resources to actual production and the use of the products. This data forms the basis of modern and powerful analysis and evaluation methods, which are called “artificial intelligence” (AI). AI proce- dures are capable of “coping with new situ- ations successfully, processing new data or new information, drawing conclusions from the available knowledge, thus generating new knowledge (…) or solving new tasks.” 1 Today, it is widely accepted that AI is a key technol- ogy that allows all users to make use of large potentials for improvement in all stages of the value chain. Even though current trials certify that Germa- ny holds a good position in AI research, they also state that AI applications IN THE USA are far more competitive. China is making tremen- dous investment in Artifical Intelligence – and Chinese companies will enter the German market for AI applications in production in a few years. Therefore, the Federal Government is absolutely right to state, in the context of its AI strategy, that they intend to make Germany and Europe a leading AI location. 2 Industrial production is one of the most important fields of application in this context. Using Smart Factories and specific and challenging use cases from our customers in industrial manu- facturing, we have already started to develop innovative AI methods and tools, which will be briefly presented in the following sections. Collection and use of data In production, data always has to be inter- preted in the context of the product or the processes. If this is the case, data forms pre- cious resources to improve the value-added process or to develop new business models. 3 This also means that each use case requires its specific data. For this very reason, it is so important to analyze the right high-quality data. It is only under these preconditions that we can make efficient use of AI. Our experience has shown that one of the major obstacles in the use of AI today is to extract the very data from the processes. We support you in gathering AI-relevant data from your machinery and equipment as well as their components. This data is either collected from the machine controls, the existing sensor systems of the machine and/or smart sensors that have been added later 4 , which we will choose and install in co- operation with you. Together with you, we will specify the level of granularity required for a specific task, and define how data from multiple sources can be integrated smoothly and in what format the data is transferred and stored. In this context, we make sure to select the right data for practical use, to sort existing data sets and to edit them for subsequent modelling. Artifical Intelligence for the Production of the Future By Dr.-Ing. Olaf Sauer, Business Unit Automation/Deputy Head of the Fraunhofer Institute for Optronics, Systems Engineering and Image Evaluation (IOSB) For decades now, “Made in Germany” has been synonymous with the high quality of German engineering skills. It is true, however, that manufacturers and their engineers are facing increasing international competition. As commonly known, this results in pressure with regard to costs, price, time and quality. Since 2012, ‚Industrie 4.0‘ has been the strategic program in industry, politics and science to ensure future-proof industrial production, including suppliers from mechanical and plant engineering, automation technology and shopfloor-related IT. 52  Unternehmen & Trends Image/Graphic: © IOSB Dr. Olaf Sauer