Quantitative evaluation of die casting surface defect severity by analyzing surface height

Abstract

Male, Ph.D. candidate in Tohoku university. His research interests mainly focus on the quantitative analysis for microstructure of die castings and compounds using mathematical morphology. V arious types of surface defects of die casting products may occur due to the complex situation inside the die. The evaluation and classification of surface defects on die castings have long been an important and challenging issue. Traditionally, visual inspection depending on technicians' individual experience has been used in factories to examine whether the surface defects are acceptable or not. However, this calls for high requirement on the technicians' skill. Even professional technicians will find it difficult to balance the conflict between efficiency and accuracy, since the work is so subjective, tedious and monotonous.Image processing technology provides a viable alternative to automatically detect the defects and evaluate the defect severity. Wong [1] et al. adopted fuzzy logic algorithm to surface images after image Abstract: It is necessary in factories to assess the severity of the surface defects of castings, as a slight surface defect will be taken as qualified when it brings no bad effect or it can be removed by the subsequent processing. In practical production, professional technicians visually inspect the surface defect severity according to their individual experience. Therefore, it is difficult for them to maintain the same standard and accuracy in the subjective, tedious and labor-intensive work. Recently, image processing techniques based on optical images have been applied to achieve better accuracy and high efficiency. Unfortunately, optical images cannot directly quantify surface depth, which works as a crucial factor in the practical assessment of surface defect severity. The surface roughness evaluation algorithm, which takes into account of both area and depth information of the assessed surface, was applied to directly characterize surface defect severity based on surface asperity rather than optical image. The results using standard casting pieces show that surface defect severity has no apparent dependence on surface roughness. However, the subsequent results show that the root-mean-squared-deviation (RMSD) of surface gradient of flow line defects positively correlates with the increase of defect severity. The other types of defect do not present such tendency. Thus, practical workpieces with flow line defects on the surface were used to verify the universality of this tendency. The results show that surface roughness of an unqualified workpiece is larger than that of a qualified workpiece after surface slope adjustment, but presents no obvious coincidence before the adjustment. In contrast, the RMSD of an unqualified workpiece, no matter before or after the adjustment, is larger than that of a qualified one.