Automated assembly inspection using a multiscale algorithm trained on synthetic images

Abstract

An important part of a robust automated assembly process is an accurate and efficient method for the inspection of finished assemblies. This paper presents a novel multiscale assembly inspection algorithm that is used to detect errors in an assembled product. The algorithm is trained on synthetic images generated using the CAD model of the different components of the assembly. The CAD model guides the inspection algorithm through its training stage by addressing the different types of variations that occur during manufacturing and assembly. Those variations are classified into those that can affect the functionality of the assembled product and those that are unrelated to its functionality. Using synthetic images in the training process adds to the versatility of the technique by removing the need to manufacture multiple prototypes and control the lighting conditions. Once trained on synthetic images, the algorithm can detect assembly errors by examining real images of the assembled product. The effectiveness of the system is illustrated on a typical mechanical assembly.