Performances of an in-line deep learning-based inspection system for surface defects of die-cast components for hybrid vehicles

Abstract

The detection of surface defects is a critical activity for quality assurance of automotive products. Deep learning (DL) methods range among the most suitable tools to support this task. They allow the inspection system to learn how to detect surface defects on the basis of a series of sample images. However, most of the past studies regard laboratory experiments. This study demonstrates the actual integration of an inspection system into an industrial production plant to perform surface defect inspection operations on machined and unmachined areas of aluminium components for hybrid cars. The proposed DL method is practical for industry, where the number of available defect samples is low. Experimental results showed a True Positive Rate of 98%.