New Tools to Boost Automated Manufacturing
To succeed in the increased global competition, the manufacturing industry depends on high-level solutions to ensure excellent machine functionality. Enterprises have to invest in innovative technologies to improve their production, reduce waste and provide an intuitive human-machine interface for operators in the factory. The IMPROVE project now presents novel solutions for the next generation of enhanced manufacturing. “The tools and applications developed by the IMPROVE team over the past three years will help foster productivity of automated manufacturing and facilitate machine operation for enterprises all over the world,” states IMPROVE coordinator Prof. Dr Oliver Niggemann from the Ostwestfalen-Lippe University of Applied Sciences. “I am convinced that our solutions will pave the way for new standards in automated manufacturing. By applying our tools, producers, manufacturers, IT specialists, and engineers can not only boost productivity and strengthen competitiveness – but also guarantee the sustainability of the production.” The solutions in the fields of simulation and optimization, condition monitoring, alarm management, and quality prediction allow manufacturers and producers to improve their productivity and product quality, to increase cost efficiency and industrial competitiveness as well as to reduce energy consumption and waste. IMPROVE Solutions at a Glance With the simulation-optimization round-trip solution, IMPROVE provides an application to enhance the overall production design. The tool allows forecasting the production process and ensures an optimized machine setting. Furthermore, it provides quality forecast, roll change simulation, energy optimization, and the training education of operators by augmented reality applications. The self-learning condition monitoring solution allows forecasting maintenance needs based on a data-driven machine learning approach. Up to now, only experienced operators were able to perform condition monitoring efficiently. The new IMPROVE solution is revolutionary for a precise prediction. The tool protects producers from unexpected breakdowns or product degradation and can be translated into different software options. The alarm management solution supports the machine operator in case of an alarm flood. The innovative algorithm is based on data-driven similarity learning as well as case-based reasoning and integrates expert knowledge. It particularly assists the operator in finding the root cause for the alarm and taking the right action. With the decision support app for quality monitoring, IMPROVE helps the machine operator to predict the quality of the production by using data-driven models based on machine parameters. As the new app enables an exact quality prediction, it ensures the best possible quality for the whole production process. “Our IMPROVE tools are all supporting the operator to facilitate the handling of difficult situations in the manufacturing process,” explains Professor Niggemann. “These are for example alarm floods, unexpected breakdowns, quality measuring or detection of faults and anomalies – with our solutions we are providing an intuitive and best possible human machine interface to improve the overall production process and to support the operator in the best possible way.” About IMPROVE The European research project IMPROVE stands for “Innovative Modelling Approaches for Production Systems to Increase Validatable Efficiency”. Together, 13 partners from six countries have been working on new solutions for automated manufacturing since 2015 to meet the challenges of Industry 4.0. IMPROVE has received EU funding of €4.2 million within the Programme for Research and Innovation Horizon 2020. http://improve-vfof.eu/ Contact Project Coordinator: Prof. Dr Oliver Niggemann Ostwestfalen-Lippe University of Applied Sciences Phone: +49 526 19429042 email: oliver.niggemann@hs-owl.de
Keywords
Automation, Automated manufacturing, artificial intelligence, industry 4.0
Countries
Austria, Germany, Denmark, Italy, Poland, Türkiye
