Real-Time Testing of Steel Strip Welds based on Bayesian Decision Theory
One of the main trouble in a steel strip manufacturing
line is the breakage of whatever weld carried out between steel coils,
that are used to produce the continuous strip to be processed. A weld
breakage results in a several hours stop of the manufacturing line. In
this process the damages caused by the breakage must be repaired.
After the reparation and in order to go on with the production it will
be necessary a restarting process of the line. For minimizing this
problem, a human operator must inspect visually and manually each
weld in order to avoid its breakage during the manufacturing process.
The work presented in this paper is based on the Bayesian decision
theory and it presents an approach to detect, on real-time, steel strip
defective welds. This approach is based on quantifying the tradeoffs
between various classification decisions using probability and the
costs that accompany such decisions.
[1] G. E. Cook, R. J. Barnett, K. Andersen, J. F. Springfield, A. M. Strauss,
"Automated Visual Inspection and Interpretation System for Weld
Quality Evaluation". Industry Applications Conference, IEEE 1995.
[2] Lars Hildebrand, Madjid Fathi, "Vision Systems for the Inspection of
Resistance Welding Joints". Human Vision and Electronic Imaging,
Proc. SPIE Vol. 3959, Jun 2000.
[3] Xiao-Guang Zhang, Jian-Jian Xu, Guang-Ying Ge, "Defects
Recognition on X-Ray Images for Weld Inspection using SVM".
Machine Learning and Cybernetics, IEEE 2004.
[4] Yi Sun, En-Liang Wang, Peng Zhou, Mohan Li, "Real-Time Weld
Defect Inspection System in X-Ray Images". Optical Information
Processing Technology, Proc. SPIE Vol. 4929, Sep 2002.
[5] Wolfram A. Karl Deutsch, "Automated Ultrasonic Inspection". October,
2000.
[6] Stephen W. Kercel, Roger A. Kisner, Marvin B. Klein, Gerald D.
Bacher, Bruno F. Pouet, "In-Process Detection of Welds Defects Using
Laser-Based Ultrasound". Environment Sensors II, Proc. SPIE Vol.
3852, Dec 1999.
[7] S. Adolfson, A. Bahrami, I. Claesson, "Quality Monitoring in Robotised
Welding using Sequential Probability Ratio Test". Digital Signal
Processing Applications, IEEE 1996.
[8] A. Erçil, "Classification Tress Prove Useful in Nondestructive Testing
of Spot Weld Quality". Welding Journal. September 1993, pp. 59−67.
[9] A. Erçil, "Regression Trees for Weld Quality Inspection". Bogaziçi
University Research Report FBE - EM 92/14, 1992.
[10] J. M. Quero, R. L. Millan, L. G. Franquelo, "Neural Network Approach
to Weld Quality Monitoring". Industrial Electronics, Control and
Instrumentation, IEEE 1994.
[11] N. Ivezic, J. D. Alien, T. Zacharia, "Neural Network-Based Resistance
Spot Welding Control and Quality Prediction". Intelligent Processing
and Manufacturing of Materials, IEEE 1999.
[12] J. Molleda, D.F. García, D. González, I. Peteira, J. A. González,
"Fuzzy−Based Approach to Real−Time Detection of Steel Strips
Detective Welds". Computational Intelligence for Measurement Systems
and Applications, IEEE 2005.
[13] SangRyong Lee, YoonJun Choo, TaeYoung Lee, ChangWoo Han,
MyunHee Kim, "Neuro−Fuzzy Algorithm for Quality Assurance of
Resistance Spot Welding". Industry Applications Conference, IEEE
2000.
[14] Chun-Hua Zhang, Li Di, Zeng An, "Welding Quality Monitoring and
Management System Based on Data Mining Technology". Machine
Learning and Cybernetics, IEEE 2003.
[15] Richard O. Duda, Peter E. Hart, David G. Stork, "Pattern
Classification", 2nd Edition, 2001. Wiley. ISBN: 0−471−05669−3.
[16] John W. Sheppard, Mark A. Kaufman, "A Bayesian Approach to
Diagnosis and Prognosis Using Built-In Test". Transactions on
Instrumentation and Measurement, IEEE 2005.
[1] G. E. Cook, R. J. Barnett, K. Andersen, J. F. Springfield, A. M. Strauss,
"Automated Visual Inspection and Interpretation System for Weld
Quality Evaluation". Industry Applications Conference, IEEE 1995.
[2] Lars Hildebrand, Madjid Fathi, "Vision Systems for the Inspection of
Resistance Welding Joints". Human Vision and Electronic Imaging,
Proc. SPIE Vol. 3959, Jun 2000.
[3] Xiao-Guang Zhang, Jian-Jian Xu, Guang-Ying Ge, "Defects
Recognition on X-Ray Images for Weld Inspection using SVM".
Machine Learning and Cybernetics, IEEE 2004.
[4] Yi Sun, En-Liang Wang, Peng Zhou, Mohan Li, "Real-Time Weld
Defect Inspection System in X-Ray Images". Optical Information
Processing Technology, Proc. SPIE Vol. 4929, Sep 2002.
[5] Wolfram A. Karl Deutsch, "Automated Ultrasonic Inspection". October,
2000.
[6] Stephen W. Kercel, Roger A. Kisner, Marvin B. Klein, Gerald D.
Bacher, Bruno F. Pouet, "In-Process Detection of Welds Defects Using
Laser-Based Ultrasound". Environment Sensors II, Proc. SPIE Vol.
3852, Dec 1999.
[7] S. Adolfson, A. Bahrami, I. Claesson, "Quality Monitoring in Robotised
Welding using Sequential Probability Ratio Test". Digital Signal
Processing Applications, IEEE 1996.
[8] A. Erçil, "Classification Tress Prove Useful in Nondestructive Testing
of Spot Weld Quality". Welding Journal. September 1993, pp. 59−67.
[9] A. Erçil, "Regression Trees for Weld Quality Inspection". Bogaziçi
University Research Report FBE - EM 92/14, 1992.
[10] J. M. Quero, R. L. Millan, L. G. Franquelo, "Neural Network Approach
to Weld Quality Monitoring". Industrial Electronics, Control and
Instrumentation, IEEE 1994.
[11] N. Ivezic, J. D. Alien, T. Zacharia, "Neural Network-Based Resistance
Spot Welding Control and Quality Prediction". Intelligent Processing
and Manufacturing of Materials, IEEE 1999.
[12] J. Molleda, D.F. García, D. González, I. Peteira, J. A. González,
"Fuzzy−Based Approach to Real−Time Detection of Steel Strips
Detective Welds". Computational Intelligence for Measurement Systems
and Applications, IEEE 2005.
[13] SangRyong Lee, YoonJun Choo, TaeYoung Lee, ChangWoo Han,
MyunHee Kim, "Neuro−Fuzzy Algorithm for Quality Assurance of
Resistance Spot Welding". Industry Applications Conference, IEEE
2000.
[14] Chun-Hua Zhang, Li Di, Zeng An, "Welding Quality Monitoring and
Management System Based on Data Mining Technology". Machine
Learning and Cybernetics, IEEE 2003.
[15] Richard O. Duda, Peter E. Hart, David G. Stork, "Pattern
Classification", 2nd Edition, 2001. Wiley. ISBN: 0−471−05669−3.
[16] John W. Sheppard, Mark A. Kaufman, "A Bayesian Approach to
Diagnosis and Prognosis Using Built-In Test". Transactions on
Instrumentation and Measurement, IEEE 2005.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:62557", author = "Julio Molleda and Daniel F. García and Juan C. Granda and Francisco J. Suárez", title = "Real-Time Testing of Steel Strip Welds based on Bayesian Decision Theory", abstract = "One of the main trouble in a steel strip manufacturing
line is the breakage of whatever weld carried out between steel coils,
that are used to produce the continuous strip to be processed. A weld
breakage results in a several hours stop of the manufacturing line. In
this process the damages caused by the breakage must be repaired.
After the reparation and in order to go on with the production it will
be necessary a restarting process of the line. For minimizing this
problem, a human operator must inspect visually and manually each
weld in order to avoid its breakage during the manufacturing process.
The work presented in this paper is based on the Bayesian decision
theory and it presents an approach to detect, on real-time, steel strip
defective welds. This approach is based on quantifying the tradeoffs
between various classification decisions using probability and the
costs that accompany such decisions.", keywords = "Classification, Pattern Recognition, ProbabilisticReasoning, Statistical Data Analysis.", volume = "1", number = "12", pages = "770-6", }
