Image Processing for Fault Detection in Aluminum Castings

Mery, Domingo; Jaeger, Thomas de; Fil'bert, D. V.

doi:10.1201/9781420030365.ch16

Cited by 6 publications

(7 citation statements)

References 16 publications

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“…We observe that the selected crossing line is approximately perpendicular to the direction of the gradient of the X-ray image without defect. This coincides with one of the criteria used by approaches with a-priori knowledge: the selected pixels of the defect-free area are located perpendicular to the direction of the gradient of the piece's contour [1]. Before the features are extracted, a pre-processing of the selected crossing line profile is performed as follows: 1) The selected profile is resized to size n = 32 using a nearest neighbour interpolation.…”

Section: Crossing Line Profilesmentioning

confidence: 99%

“…In our approach, the pre-processing techniques are used to remove noise, enhance contrast, correct the shading effect and restore blur deformation [1].…”

Section: Segmentation Of Potential Flawsmentioning

confidence: 99%

“…However, they require a very precise positioning of the test object and a complicated selection process of the defect-free areas. A review of the existing approaches of automated flaw detections in aluminium castings can be found in [1].…”

Section: Introductionmentioning

confidence: 99%

“…To ensure the safety of construction, it is necessary to check every part thoroughly. Radioscopy rapidly became the accepted way for controlling the quality of die cast pieces through computer-aided analysis of X-ray images [1]. The purpose of this non-destructive testing (NDT) method is to identify casting defects, which may be located within the piece and thus are undetectable to the naked eye.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, the defect-free areas are defined in sections where the grey level values have a small variance. These methods have become the most widely established in industrial applications owing to their high detection performance 1 . However, they require a very precise positioning of the test object and a complicated selection process of the defect-free areas.…”

Section: Introductionmentioning

confidence: 99%

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Crossing Line Profile: A New Approach to Detecting Defects in Aluminium Die Casting

Mery

2003

Lecture Notes in Computer Science

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Abstract. Radioscopy is the accepted way for controlling the quality of aluminium die cast pieces through computer-aided analysis of X-ray images. Two classes of regions are possible in a digital X-ray image of a casting: regions belonging to regular structures of the specimen, and those relating to defects. Since the contrast between a flaw and a defectfree neighbourhood is distinctive, the detection is usually performed by thresholding this feature. Nevertheless, this measurement suffers from accuracy error when the neighbourhood is not homogeneous, for example when the flaw is at an edge of a regular structure of the test object. For this reason, many approaches use a-priori information about the location of regular structures of the test piece. In this paper, a new approach to detecting defects without a-priori knowledge is proposed. The approach is based on features extracted from crossing line profiles, i.e., the grey level profiles along straight lines crossing each segmented potential flaw in the middle. The profile that contains the most similar grey levels in the extremes is selected. Hence, the homogeneity of the neighbourhood is ensured. Features from the selected profile are extracted. The detection performance of our features and a vast number of other known features are assessed by computing the area A z under the Receiver Operation Characteristic (ROC) curve. The best performance is achieved using one of the proposed features yielding an area A z = 0.9944 in 50 X-ray images of aluminium wheels with 23.000 potential flaws.

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Section: Crossing Line Profilesmentioning

confidence: 99%

“…In our approach, the pre-processing techniques are used to remove noise, enhance contrast, correct the shading effect and restore blur deformation [1].…”

Section: Segmentation Of Potential Flawsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Crossing Line Profile: A New Approach to Detecting Defects in Aluminium Die Casting

Mery

2003

Lecture Notes in Computer Science

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Machine Vision Solutions in Automotive Industry

Prasad¹,

Radhakrishnan²,

Bharathi³

2014

Soft Computing Techniques in Engineering Applications

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Applications in X-ray Testing

Mery

2015

Computer Vision for X-Ray Testing

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In this chapter, relevant applications on X-ray testing are described. We cover X-ray testing in (i) castings, (ii) welds, (iii) baggage, (iv) natural products, and (v) others (like cargos and electronic circuits). For each application, the state of the art is presented. Approaches in each application are summarized showing how they use computer vision techniques. A detailed approach is shown in each application and some examples using Matlab are given in order to illustrate the performance of the methods.Cover image: 3D representation of the X-ray image of a wheel (X-ray image C0023_0001 colored with 'sinmap' colormap).

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Image Processing for Fault Detection in Aluminum Castings

Cited by 6 publications

References 16 publications

Crossing Line Profile: A New Approach to Detecting Defects in Aluminium Die Casting

Crossing Line Profile: A New Approach to Detecting Defects in Aluminium Die Casting

Machine Vision Solutions in Automotive Industry

Applications in X-ray Testing

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