Study on Training Data of DNN (Deep Neural Network) for Visual Inspection of Large Die-Cast Parts with Three-Dimensional and Complex Shapes

Abstract

This paper describes training data to be applied to a Deep Neural Network (DNN) to automate the visual inspection of large die-cast parts with complex 3D shapes. In visual inspection systems, an alignment of a target workpiece is an important issue. If the workpiece is planar, it can be aligned by geometric transformation using image processing. However, when the workpiece is three-dimensional, alignment by image processing is essentially impossible. When the workpiece is large and has a complex three-dimensional shape, the difference in appearance due to misalignment is significant. In this study, we propose a method to reproduce differences in appearance images caused by misalignment by controlling the inspection system. By using the images reproduced by the proposed method as training data, the inspection accuracy of the appearance inspection DNN is improved. Experiments using actual inspection images confirmed the effectiveness of the proposed method.