Quantitative evaluation of die casting surface defect severity by analyzing surface height
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Quantitative evaluation of die casting surface defect severity by analyzing surface height
Document code: A
0 Yan Xu, Naoya Hirata and Koichi Anzai Department of Metallurgy, Graduate School of Engineering, Tohoku University , Sendai 980-8579 , Japan
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.
die casting; surface defect; surface roughness
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Vproducts may occur due to the complex situation
arious types of surface defects of die casting
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
*Yan Xu
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.
processing to automatically distinguish the quench
crack, mechanical crack and hole on the surface of
castings. Defect area, perimeter, length, etc. are basic
parameters in fuzzy logic algorithm, in which new
parameters like radius ratio, axis ratio and approximate
area were calculated to characterize the shape feature
of defect area. Świłło [2-3] et al. developed a surface
defect inspecting machine for die castings with image
processing algorithms based on modified Laplacian of
Gaussian edge detection method to recognize defects
with different shapes and sizes. Most of the relevant
works were carried out by analyzing the optical images
taken from the object surfaces and tried to classify
surface defects by calculating shape features like
area, perimeter, length, etc. However, defect depth
(defect height for blisters) also plays an essential role
in evaluating surface defect severity. Zhang [4] et al.
also tried to analyze the surface defect severity using
shape features achieved from image analysis. The
combining usage of these features highly improves
classifying precision. Most of the relevant works were
carried out by analyzing the optical images taken from
the object surfaces and tried to classify surface defects
by calculating shape features like area, perimeter, length, etc.
However, defect depth (defect height for blisters) from the
bottom (top for blisters) to the expected surface level of sound
castings also plays an essential role in evaluating surface defect
severity. An evaluation method generally considering defect
length, area and depth is desirable. Surface roughness [7] is
such a conventional concept. It reflects the surface asperity by
calculating surface height variations.
In this study, the surface roughness of standard die casting
pieces for surface defect (...truncated)