Abstract
Qualitative and quantitative analysis of surface defects on aluminum die-cast parts is important for both quality assurance and process monitoring. In addition to the functionality and durability of the parts, the appearance of a die-cast component can be of crucial importance during the inspection of incoming goods by customers in order to ensure their functionality. Nowadays, many of the detection operations of surface defects are performed by specialized operators, but this approach is far from being sustainable for high production rates. In this context, research has focused on machine vision systems for automatic defect detection based on artificial intelligence (AI) and artificial neural networks. However, several obstacles have so far hindered a full-scale application of these intelligent systems. Images of the aluminum surface can contain a large amount of noise due to surface reflectivity and, in addition, vibrations, improper/variable ambient lighting can make automatic analysis of surface defects, which usually have an irregular shape, very difficult. This led to combining the potential of 3D scanning and measuring systems with 2D machine vision as an acquisition technology to detect surface defects. Nevertheless, the two acquisition processes present strengths and weaknesses, which mostly depend on geometrical aspects of the parts to be detected. The present paper illustrates a first attempt to combine the mentioned 2D and 3D systems into an industrial production environment. The paper presents the developed system to allow multiple acquisitions and explains how these sources of information are fed to an AI system.