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Research Papers

Online Eccentricity Monitoring of Seamless Tubes in Cross-Roll Piercing Mill

[+] Author and Article Information
Weihong Guo

Department of Industrial and
Operations Engineering,
The University of Michigan,
Ann Arbor, MI 48109
e-mail: graceguo@umich.edu

Rui Chen

Manufacturing Engineering Program of
Integrative Systems and Design Division,
College of Engineering,
The University of Michigan,
Ann Arbor, MI 48109
e-mail: ruichen@umich.edu

Jionghua (Judy) Jin

Fellow ASME
Department of Industrial and
Operations Engineering,
The University of Michigan,
Ann Arbor, MI 48109
e-mail: jhjin@umich.edu

1Corresponding author.

Contributed by the Manufacturing Engineering Division of ASME for publication in the JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING. Manuscript received April 21, 2014; final manuscript received August 22, 2014; published online December 12, 2014. Assoc. Editor: Robert Gao.

J. Manuf. Sci. Eng 137(2), 021007 (Apr 01, 2015) (10 pages) Paper No: MANU-14-1238; doi: 10.1115/1.4028440 History: Received April 21, 2014; Revised August 22, 2014; Online December 12, 2014

Wall-thickness eccentricity is a major dimensional deviation problem in seamless steel tube production. Although eccentricity is mainly caused by abnormal process conditions in the cross-roll piercing mill, most seamless tube plants lack the monitoring at the hot piercing stage but only inspect the quality of finished tubes using ultrasonic testing (UT) at the end of all manufacturing processes. This paper develops an online monitoring technique to detect abnormal conditions in the cross-roll piercing mill. Based on an image-sensing technique, process operation condition can be extracted from the vibration signals. Optimal frequency features that are sensitive to tube wall-thickness variation are then selected through the formulation and solution of a set-covering optimization problem. Hotelling T2 control charts are constructed using the selected features for online monitoring. The developed monitoring technique enables early detection of eccentricity problems at the hot piercing stage, which can facilitate timely adjustment and defect prevention. The monitoring technique developed in this paper is generic and can be widely applied to the hot piercing process of various products. This paper also provides a general framework for effectively analyzing image-based sensing data and establishing the linkage between product quality information and process information.

Copyright © 2015 by ASME
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Figures

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Fig. 1

Seamless tube cross-roll piercing operation and image sensing

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Fig. 2

Examples of UT inspection results: (a) a conforming tube and (b) a nonconforming tube

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Fig. 3

Wall-thickness variation due to cross section eccentric errors: (a) segmental view of thickness variation of Fig. 3(b) and (b) cross section j

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Fig. 4

Vibration signal of a hot pierced tube

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Fig. 5

Three substages of hot piercing: (a) billet supported only by rollers, (b) billet supported by both rollers and clamps, and (c) billet supported only by clamps

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Fig. 6

Flowchart of the proposed methodology

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Fig. 7

Flowchart of tube quality classification

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Fig. 8

Patterns of wall-thickness variations: (a) a high-quality tube with small wall-thickness variations over the tube, (b) a low-quality tube with large wall-thickness variations over the tube, (c) a high-quality tube with large wall-thickness variations at only one cross section of the tube, and (d) a low-quality tube with large wall-thickness variations over 6% of the tube length

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Fig. 9

Hierarchical clustering of a batch of N = 15 tubes: (a) U5%,k values and (b) dendrogram

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Fig. 10

Vibration signal of substage B shown in frequency domain

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Fig. 11

Flowchart of monitoring feature selection

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Fig. 12

A greedy algorithm for monitoring feature selection

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Fig. 13

Procedures for Hotelling T2 control chart development

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Fig. 14

Quality classification results for batch 1: (a) U5%,k values and (b) dendrogram of hierarchical clustering

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Fig. 15

T2 control charts and monitoring results for batch 1: (a) monitoring features F1 = {f47~f52}, (b) monitoring features F2 = {f86~f91}, (c) monitoring features F3 = {f92~f97}, and (d) monitoring features F4 = {f115~f120}

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Fig. 16

Quality classification results for batch 4: (a) U5%,k values and (b) dendrogram of hierarchical clustering

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Fig. 17

T2 control charts and monitoring results for batch 4: (a) monitoring features F1 = {f47~f52}, (b) monitoring features F2 = {f86~f91}, (c) monitoring features F3 = {f92~f97}, and (d) monitoring features F4 = {f115~f120}

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