There is great competitive pressure for small and medium-sized enterprises to apply new IT concepts such as machine learning and artificial intelligence (AI). Practical and monetary hurdles as well as a lack of skilled workers have an additional limiting effect. With this project, the CiS Research Institute aims to develop a practicable AI-supported service that combines automatic image processing with other manufacturing-relevant quality data. Industrial image processing software is being further developed on the basis of two different sensor productions and coupled to various electrical parameters for quality assurance in a production process. Empirical values flow into firmly defined error catalogs and contribute to the early detection of deviations in order to optimize the production process and save resources. The focus is on the flexible adaptability of the methods to be developed to changed objects and environmental influences and promises enormous advantages, especially for changing product types and small to medium quantities of individual assemblies.
Companies benefit directly from this methodological knowledge by adapting or adopting the developed procedures and applying them to their own development and production processes. The CiS Research Institute is available as a development partner and consultant after project completion.
The project “PinSpek – Service for AI-supported Quality Control” belongs to the specialization field “Industrial Production and Systems” of RIS3 Thuringia and concerns the key objectives “LIPS 1.1: Thuringia is 202 competence region for intelligent networked production with adequately adapted human-machine interaction”, “LIPS 1. 2:Thuringia is a leading region in Europe for the development and application of flexible and efficient processes, systems as well as technologies for individualized products” and “LIPS 1.3: Intelligent production monitoring and control: Thuringia is a globally established region in the field of sensor and measurement technology for industrial production” in 2020.
The project underlying these results was funded by the Free State of Thuringia under the number 2022 WFN 0035 and co-financed by funds from the European Union under the European Fund for Regional Development (EFRE).