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

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.

[1]  Yu Qian Zhao,et al.  Effects of Feed Angle on Mannesmann Piercing in Drill Steel Production , 2014 .

[2]  Feng Liu,et al.  Numerical Simulation of the Piercing Process of Large-Sized Seamless Steel Tube , 2013 .

[3]  Jing Li,et al.  On-Line Seam Detection in Rolling Processes Using Snake Projection and Discrete Wavelet Transform , 2007 .

[4]  Erik Oberg,et al.  Machinery's encyclopedia with 1925 supplement : a work of reference covering practical mathematics and mechanics, machine design, machine construction and operation, electrical, gas, hydraulic, and steam power machinery, metallurgy, and kindred subjects in the engineering field , .

[5]  Marc Choquet,et al.  Laser Ultrasonic System for On‐Line Steel Tube Gauging , 2003 .

[6]  Jing Li,et al.  Optimal sensor allocation by integrating causal models and set-covering algorithms , 2010 .

[7]  E. Degarmo Materials and Processes in Manufacturing , 1974 .

[8]  H. Ogi,et al.  EMATs for Science and Industry: Noncontacting Ultrasonic Measurements , 2010 .

[9]  R. B. Thompson Physical Principles of Measurements with EMAT Transducers , 1990 .

[10]  Gábor J. Székely,et al.  Hierarchical Clustering via Joint Between-Within Distances: Extending Ward's Minimum Variance Method , 2005, J. Classif..

[11]  Jianjun Shi,et al.  On-Line Bleeds Detection in Continuous Casting Processes Using Engineering-Driven Rule-Based Algorithm , 2009 .

[12]  Qiang Li,et al.  Online classification of surface defects in hot rolling processes , 2009 .

[13]  Yu Qian Zhao,et al.  Effects of Plug Position on Mannesmann Piercing in Drill Steel Production , 2013 .

[14]  Jionghua Jin,et al.  AUTOMATIC FEATURE EXTRACTION AND CLASSIFICATION OF SURFACE DEFECTS IN CONTINUOUS CASTING , 2010 .

[15]  Qiang Li Process Monitoring and Quality Control in Hot Rolling Processes Using Image Sensing Data , 2012 .

[16]  H. Hotelling The Generalization of Student’s Ratio , 1931 .

[17]  Qiang Li,et al.  Detection and diagnosis of repetitive surface defects for hot rolling processes , 2010 .

[18]  Alan R. Jones,et al.  Fast Fourier Transform , 1970, SIGP.

[19]  Xin-She Yang,et al.  Introduction to Algorithms , 2021, Nature-Inspired Optimization Algorithms.

[20]  Howard Hsun-hau,et al.  Imaging-based In-Line Surface Defect Inspection for Bar Rolling , 2004 .

[21]  Karl-Heinz Brensing,et al.  Steel Tube and Pipe Manufacturing Processes , 2004 .