Transfer Learning for Automatic Quality Inspection
Companies which offer a high variety of products by using a wide range of production processes are facing the challenge to follow the trend by setting up a flexible production in order to handle small lot sizes with frequent changing and extended product types. In such an environment, the generally extremely high quality requirements must also meet economical aspects at any time. This challenge is additionally exacerbated by the global distribution of production sites with different quality standards and conditions. State of - the - art quality inspection systems reach their limits, as these systems only us e specific data for the respective inspection task. This project proposal addresses the above mentioned problem by using deep transfer learning methods in order to transfer quality- related knowledge from a part A to a part B.The objective here is to optimize quality inspection models by utilizing so far unused data with different modalities from different inspection systems. To ensure the generality of the approach,the framework will be developed by using data with different modalities (optical, acoustical etc.) from different use cases. Finally, the methods will be evaluated, especially with small lot sizes, on production - variants of sinter metal parts, friction scs and engine bearings for the automotive industry.
The central result of the project will be a software framework designed as a “fog computing cloud” for data - driven methodologies and workflows for a later use in the industrial environment. The project demonstrates on the basis of automated, non destructive quality inspection systems , how effective data - driven modeling techniques of artificial intelligence can be adapted using transfer learning to meet the requirements of the manufacturing industry and to exploit synergies . In this way, this project will provide important conceptual and technical foundations for the upcoming transformation process of digitization and flexibilization.
Project start 02. 2018 (duration 36 month)
The project will be funded within the framework of the programme "Produktion der Zukunft".