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RESEARCH PAPERS

On-Line Seam Detection in Rolling Processes Using Snake Projection and Discrete Wavelet Transform

[+] Author and Article Information
Jing Li, Jianjun Shi

Department of Industrial and Operations Engineering, The University of Michigan, 1205 Beal Avenue, Ann Arbor, MI 48109-2117

Tzyy-Shuh Chang

 OG Technologies, Inc., 4300 Varsity Drive, Suite C, Ann Arbor, MI 48108

Here, the “time” is not clock time, but is defined in a broad sense, as the order of the pixels in the sequence.

J. Manuf. Sci. Eng 129(5), 926-933 (May 03, 2007) (8 pages) doi:10.1115/1.2752519 History: Received December 01, 2004; Revised May 03, 2007

This paper describes the development of an on-line quality inspection algorithm for detecting the surface defect “seam” generated in rolling processes. A feature-preserving “snake-projection” method is proposed for dimension reduction by converting the suspicious seam-containing images to one-dimensional sequences. Discrete wavelet transform is then performed on the sequences for feature extraction. Finally, a T2 control chart is established based on the extracted features to distinguish real seams from false positives. The snake-projection method has two parameters that impact the effectiveness of the algorithm. Thus, selection of the parameters is discussed. Implementation of the proposed algorithm shows that it satisfies the speed and accuracy requirements for on-line seam detection.

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Copyright © 2007 by American Society of Mechanical Engineers
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Figures

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Figure 3

(a) Two ridge-based false positive sub-images (left: containing two dark strips; right: containing one dark strip), (b) a mark-based false positive sub-image

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Figure 9

T2 control chart at k=8, s=5 for the testing dataset

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Figure 1

A portion of a sensing image from a bar-rolling process

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Figure 2

A seam sub-image

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Figure 4

Procedure of the snake-projection-wavelet algorithm

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Figure 5

Sequence of the seam sub-image in Fig. 2 by snake projection (k=8,s=5)

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Figure 6

(a) Sequences of the ridge-based false positive sub-images in Fig. 3 by snake projection (k=8,s=5) (top: corresponding to the sub-image with two dark strips; bottom: corresponding to the sub-image with one dark strip), (b) sequence of the mark-based false positive sub-image in Fig. 3 by snake projection (k=8,s=5)

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Figure 7

Flow chart of the procedure for selecting snake-projection parameters

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Figure 8

Chi-square quantile-quantile plot of features (x-axis: chi-square quantiles; y-axis: squared Mahalanobis distances (14) of features; the labels for x- and y-axes are omitted)

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