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A Scalable Synthetic Data Creation Pipeline for AI-Based Automated Optical Quality Control

Christian Schorr; Sebastian Hocke; Tobias Masiak; Patrick Trampert
In: Proceedings of the 14th International Conference on Simulation and Modeling Methodologies, Technologies and Applications. International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH-2024), July 10-12, Dijon, France, Pages 37-46, ISBN 978-989-758-708-5, SCITEPRESS, Portugal, 2024.


In recent years, the industry’s interest in improving its production efficiency with AI algorithms has grown rapidly. Especially advancement in computer vision seem promising for visual quality inspection. However. the proper classification or detection of defects in manufactured parts based on images or recordings requires large amounts of annotated data, ideally containing every possible occurring defect of the manufactured part. Since some defects only appear rarely in production, sufficient data collection takes a lot of time and may lead to a waste of resources. In this work we introduce a configurable, reusable, and scalable 3D rendering pipeline based on a digital reality concept for generating highly realistic annotated image data. We combine various modelling concepts and rendering techniques and evaluate their use and practicability for industrial purposes by customizing our pipeline for a real-world industrial use case. The generated synthetic data is used in different combinations with real images to train a deep learning model for defect prediction. The results show that using synthetic data is a promising approach for AI-based automated quality control.