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A Review of Business Intelligence Techniques for Mild Steel Defect Diagnosis

by Veena Jokhakar, S.v.patel
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 113 - Number 10
Year of Publication: 2015
Authors: Veena Jokhakar, S.v.patel
10.5120/19863-1823

Veena Jokhakar, S.v.patel . A Review of Business Intelligence Techniques for Mild Steel Defect Diagnosis. International Journal of Computer Applications. 113, 10 ( March 2015), 32-38. DOI=10.5120/19863-1823

@article{ 10.5120/19863-1823,
author = { Veena Jokhakar, S.v.patel },
title = { A Review of Business Intelligence Techniques for Mild Steel Defect Diagnosis },
journal = { International Journal of Computer Applications },
issue_date = { March 2015 },
volume = { 113 },
number = { 10 },
month = { March },
year = { 2015 },
issn = { 0975-8887 },
pages = { 32-38 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume113/number10/19863-1823/ },
doi = { 10.5120/19863-1823 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:50:36.192073+05:30
%A Veena Jokhakar
%A S.v.patel
%T A Review of Business Intelligence Techniques for Mild Steel Defect Diagnosis
%J International Journal of Computer Applications
%@ 0975-8887
%V 113
%N 10
%P 32-38
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this competitive era, manufacturing companies have to focus on the quality of the produced products. The quality of the product produced is affected by many influential parameters during the process. The product once produced with a lower quality then usually ends up with incurring loss in certain terms to the company. Hence, it is extremely important to know the defect causing parameters and perform defect diagnosis. Various techniques like SPC-SQC, Six-Sigma and Kaizen have been used for quality analysis. But since last few years machine learning and data mining is being used for analysis due to advancement in the field and its advantages. This paper conducts an analytical survey of various business intelligence techniques used in for defect diagnosis. The paper concludes with the analytical results as random forest performs the best in terms of performance compared to other techniques and shows the future research scope in this area. Moreover, we find that random forest has not been introduced yet in steel defect diagnosis.

References
  1. Dharminder Kumar, Suman, "Performance Analysis Of Various Data Mining Algorithms: A Review", International Journal Of Computer Applications (0975 – 8887), Volume 32– No. 6, October 2011
  2. Daniel Grossman and Pedro Domingos, "Learning Bayesian Network Classifiers by Maximizing Conditional Likelihood", 21st International Conference on Machine Learning, Banff, Canada, 2004
  3. A Review of Clustering and Classification Techniques in Data Mining, Samir Kumar Sarangi, Dr. Vivek Jaglan, Yajnaseni Dash, In ternational Journal of Engineering, Business and Enterprise Applications (IJEBEA), ISSN (Print): 2279-0020 ISSN (Online): 2279-0039
  4. Bratina Božidar And Boris Tovornik, Multivariate Statistical Methods For Industrial Process Prognostics, Cybernetics And Informatics, International Conference February 10 - 13, 2010
  5. Kesheng Wang,Applying Data Mining To Manufacturing: The Nature And Implications, Journal Of Intelligent Manufacturing, 2007
  6. B. Bak?r, Bak?r, I. Batmaz, F. A. Güntürkün, I. A. Ipekçi, G. Köksal, And N. E. Özdemirel , Defect Cause Modeling With Decision Tree And Regression Analysis, World Academy Of Science, Engineering And Technology International Journal Of Mechanical, Aerospace, Industrial And Mechatronics Engineering Vol:2 No:12, 2008
  7. Feng Zhang ; Fairchild Semicond. , South Portland, ME ; Timwah Luk, A Data Mining Algorithm for Monitoring PCB Assembly Quality , Electronics Packaging Manufacturing, IEEE Transactions on (Volume:30 , Issue: 4 ), ISSN : 1521-334X, Oct. 2007
  8. Berna Bakir, Defect Cause Modeling With Decision Tree And Regression Analysis: A Case Study In Casting Industry, A Thesis Submitted To The Graduate School Of Informatics Of Middle East Technical University,May 2007
  9. Shu-Gauge He,Zhen He,G. Alan Wang And Lili, Quality Improvement Using Data Mining In Manufacturing Processes, Data Mining And Knowledge Discovery In Real Life Applications, Isbn 978-3-902613-53-0,Pp. 438,Feb 2009
  10. Martin Vlado,1 Róbert Bidulský,2;_ LuciaGulová,1 Kristína Machová,1 Jana Bidulská,1 JánValí?Cek3 And Ján Sas1, The Production Of Cracks Evolution In Continuously Cast Steel Slab, High Temp. Mater. Proc. , Vol. 1–2 (2011), Pp. 105–111, Copyright © 2011 De Gruyter. Doi 10. 1515/Htmp. 2011. 014
  11. Fahmi Arif,Nanna Suryana,Burairah Hussin, A Data Mining Approach For Developing Quality Prediction Model In Multi-Stage Manufacturing,© 2013 By Ijca Journal, Volume 69 - Number 22
  12. Mu'taz M. Qubbaj, Supervised By Dr. Amay Gupta, Using Decision Trees To Create A Control Mechanism For Regulation Of The Hot Metal Temparature Of The "G" Blast Furnace At Steelcorp, 2000.
  13. Jarno J. Haapamaki,Satu M. Tammimen And Juha J. Roning,Data Mining Methods In Hot Steel Rolling For Scale Defect Prediction, , Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, 2005
  14. Shankar Kumar Choudhari, Sunil Kumar, Vinit K Mathur, Application Of Datamining Technology At Tata Steel,2004
  15. M. Li. S. . Sethi, J. Luciow,K. Wangner, Mining Production Data With Neural Network & Cart, In Conf. Rec. IEEE Int. Conf. Data Mining, 2003.
  16. Bulsari, Abhay And Saxen, Henrik. "Classification Of Blast Furnace Probe Temperatures Using Neural Networks. " Steel Research. Vol. 66. 1995
  17. Sujit Kumar Bag, ANN BASED PREDICTION OF BLAST FURNACE PARAMETERS , Journal - The Institution of Engineers, Malaysia (Vol. 68, No. 1, March 2007)
  18. D. Flynn,J. Ritchie And M. Cregan, Data Mining Techniques Applied To power Plant Performance Monitoring, Processdings of the 16th IFAC Worl Congress,2005 , Volume 16 , Part 1, pp. 1636-1636
  19. Golriz Amooee,Behrouz Minaei-Bidgoli,Malihe Bagheri-Devnavi, A Comparison Between Data Mining Prodiction Algorithms For Fault Detection,Ijcsi ,Vol. 8,Issue 6,No 3, Nov 2011, Issn :1694-0814
  20. Kai-Ying Chen, Long-Sheng Chen, Mu-Chen Chen, Chia-Lung Lee , Using Svm Based Method For Equipment Fault Detection In A Thermal Power Plant, Computers In Industry 62, 42–50, 2011 Journal Homepage: Www. Elsevier. Com/Locate/Compind
  21. Sankar Mahadevan And Sirish L. Shah, Fault Detection And Diagonosis In Process Data Usinh Support Vector Machines,Jounal Of Process Control 19, 1627-1639, 2009
  22. Yu-Chiang Li, Jieh-Shan Yeh, Chin-Chen Chang, "Efficient Algorithms for Mining Shared-Frequent Itemsets", In Proceedings of the 11th World Congress Of Intl. Fuzzy Systems Association, 2005.
  23. V. Umarani And Dr. M. Punithavalli, A Study On Effective Mining Of Association Rules From Huge Databases, Ijcsr, Vol. 1 Issue 1, 2010
  24. Sayed Mehran Sharafi,Hamid Reza Esmaeily,Applying Data Mining Methods To Predict Defects On Steel Surface,Jounal Of Theoretical And Applied Information Technology,©2005-2010
  25. Wei-Chou Chen, Shian-Shyong Tseng, Ching-Yao Wang*, A novel manufacturing defect detection method using association rule mining techniques, ELSEVIER,Expert Systems with Applications 29,807–815, 2005
  26. Jarno Haapamaki And Juha Roning,Genetic Algorithms In Hot Steel Rolling For Scale Defect Prediction,World Academy Of Science ,Engineering And Technology 5, 2007
  27. By Danny Lai, Supervised By Dr. Amar Gupta, Using Genetic Algorithms As A Controller For Hot Metal Temperature In Blast Furnace Processes, 2000
  28. Tzu-Liang (Bill) Tseng, M. C. Jothishankar, Tong (Teresa)Wu, Quality Control Problem In Printed Circuit Board Manufacturing ,An Extended Rough Set Theory Approach, Journal O['Manufacturing Systems, Vol. 23/No. 1,2004
  29. Tarun chopra and Jayashi Vajpi, Fault Diagnosis Benchmark Process Control System Using Stochastic Gradient Boosted Decision Trees, Ijsce,Issn:2231-2307,Vol-1,Issue-3,Jul 2011
  30. Richard Derrig, Ph. D. And Louise Francis, Distinguishing The Forest From The Trees: A Comparison Of Tree Based Data Mining Methods, Casualty Actuarial Society Forum, Winter 2006
  31. Krogh A, Vedelsby J, Neural Network Ensembles, Cross Validation, and Active Learning, Advances in Neural Information Processing Systems Vol 7, MIT Press , 231-238, 1995
  32. Opitz D, Maclin R, Popular Ensemble Methods: An Empirical Study, Journal of Artificial Intelligence 11, 169-198, 1999
  33. Vrushali Y Kulkarni,Pradeep K Sinha, Effective Learning and Classification using Random Forest Algorithm , International Journal of Engineering and Innovative Technology (IJEIT) Volume 3, Issue 11, May 2014, ISSN: 2277-3754, ISO 9001:2008 Certified
  34. Byron P. Roea, Hai-Jun Yanga,_, Ji Zhub, Yong Liuc, Ion Stancuc, Gordon Mcgregord, Boosted Decision Trees As An Alternative To Artificial Neural Networks For Particle Identification, Nuclear Instruments And Methods In Physics Research A 543 (2005) 577–584,Science Direct, Available Online 25 January 2005
  35. Lidia Auret , Process Monitoring and Fault Diagnosis using Random Forests , Dissertation presented for the Degree of Doctor Of Philosophy (Extractive Metallurgical Engineering) , December 2010
  36. Vrishali Y Kulkarni,Dr. Pradeep K. Sinha, Random Forest Classifiers:A Survey and Future, International Journal of Advanced Computing, ISSN:2051-0845, Vol. 36, Issue. 1,April 2013
  37. Cuomg Nguyen,Yong Wand,Ha Nam Nguyen,Random forest classifier combined with feature seletion for breast cancer disgnosisi and prognostic,Journal Biomedical Science and engineering ,6,551-560,May 2013
  38. Jin-Ding Cai, Ren-Wu Yan, Fault Diagnosis of Power Electronic Circuit Based on Random Forests Algorithm, 2009 Fifth International Conference on Natural Computation,978-0-7695-3736-8/09 © 2009 IEEE
  39. Manolis Maragoudakis, Euripides Loukis and Panagiotis-Prodromos Pantelides, Random Forests Identification of Gas Turbine Faults, 19th International Conference on Systems Engineering, 978-0-7695-3331-5/08 © 2008 IEEE
  40. Zhiyuan Yang, Qinming Tan ,The Application of Random Forest and Morphology Analysis to Fault Diagnosis on the Chain box of ships, Third International Symposium on Intelligent Information Technology and Security Informatics, 978-0-7695-4020-7/10 © 2010 IEEE
Index Terms

Computer Science
Information Sciences

Keywords

Ensemble approach random forest steel defect diagnosis

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