A novel LS-SVM modeling method for a hydraulic press forging process with multiple localized solutions

IEEE Transactions on Industrial Informatics

Volumn 11, Issue 3, 2015, Pages 663-670

  Lu, Xinjiang ^a   Fan, Bi ^b   Huang, Minghui ^a  

^a CENTRAL SOUTH UNIVERSITY   (China)

^b CITY UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Forging process; Least squares support vector machine (LS SVM); Multiple working regions; Regularization

Indexed keywords

FORGING MACHINES; HYDRAULIC MACHINERY; LEAST SQUARES APPROXIMATIONS; PRESSES (MACHINE TOOLS); SUPPORT VECTOR MACHINES;

FORGING PROCESS; HYDRAULIC PRESS FORGING; HYDRAULIC PRESS MACHINES; LEAST SQUARES SUPPORT VECTOR MACHINES; LOCALIZED SOLUTIONS; MULTIPLE WORKING REGIONS; NON-LINEAR SOLUTIONS; REGULARIZATION;

FORGING;

EID: 84937401230     PISSN: 15513203     EISSN: None     Source Type: Journal    
DOI: 10.1109/TII.2015.2422614     Document Type: Article

References (33)

1. X. J. Lu and M. H. Huang, "A simple online modeling approach for a time-varying forging process," Int. J. Adv. Manuf. Technol., vol. 75, pp. 1197-1205, 2014.
2. Z. P. Lin, Engineering Computation of Deformation Force Under Forging. Beijing: Mechanical Industry Press, 1986.
3. X. J. Lu and M. H. Huang, "System decomposition based multi-level control for hydraulic press machine," IEEE Trans. Ind. Electron., vol. 59, no. 4, pp. 1980-1987, Apr. 2012.
4. G. Shen and D. Furrer, "Manufacturing of aerospace forgings," J. Mater. Process. Technol., vol. 98, pp. 189-195, 2000.
5. J.M. Zheng, S. D. Zhao, and S. G.Wei, "Application of self-tuning fuzzy PID controller for a SRM direct drive volume control hydraulic press," Control Eng. Pract., vol. 17, no. 12, pp. 1398-1404, 2009.
6. P. H. Zhu et al., "A novel sensitivity analysis method in structural performance of hydraulic press," Math. Prob. Eng., vol. 2012, pp. 1-21, 2012.
7. Q. Liu and X. Bian, "Multi-objective optimization of the hydraulic press crossbeam based on neural network and pareto GA," in Proc. 2nd Int. Conf. Adv. Comput. Control (ICACC), vol. 1, 2010, pp. 52-55.
8. B. Armstrong-HeLouvry, P. Dupont, and C. C. D.Wit, "A survey of models, analysis tools and compensation methods for the control of machines with friction," Automatica, vol. 30, no. 7, pp. 1083-1138, 1994.
9. W. S. Owen and E. A. Croft, "The reduction of stick-slip friction in hydraulic actuators," IEEE/ASME Trans. Mechatron., vol. 8, no. 3, pp. 362-371, Sep. 2003.
10. T. H. Lee, K. K. Tan, and S. Huang, "Adaptive friction compensation with a dynamical friction model," IEEE/ASME Trans. Mechatron., vol. 16, no. 1, pp. 133-140, Feb. 2011.
11. X. J. Lu, Y. B. Li, and M. H. Huang, "Operation-region-decompositionbased singular value decomposition/neural network modeling method for complex hydraulic press machines," Ind. Eng. Chem. Res., vol. 52, no. 48, pp. 17221-17228, 2013.
12. J. A. K. Suykens, T. V. Gestel, J. D. Brabanter, B. D. Moor, and J. Vandewalle, Least Squares Support VectorMachines. Singapore:World Scientific, 2002.
13. G. L.Wang, Y. F. Li, and D. X. Bi, "Support vector machine networks for friction modeling," IEEE/ASME Trans.Mechatron., vol. 9, no. 3, pp. 601- 606, Sep. 2004.
14. C. Qi et al., "Time/space-separation-based SVM modeling for nonlinear distributed parameter processes," Ind. Eng. Chem. Res., vol. 50, no. 1, pp. 332-341, 2010.
15. J. Zhao, Q. Liu,W. Pedrycz, and D. Li, "Effective noise estimation-based online prediction for byproduct gas system in steel industry," IEEE Trans. Ind. Informat., vol. 8, no. 4, pp. 953-963, Nov. 2012.
16. N. Shrivastava, A. Khosravi, and B. Panigrahi, "Prediction interval estimation of electricity prices using PSO tuned support vector machines," IEEE Trans. Ind. Informat., vol. 11, no. 2, pp. 322-331, Apr. 2015.
17. G. Liu, L. Chen, W. Zhao, Y. Jiang, and L. Qu, "Internal model control of permanent magnet synchronous motor using support vector machine generalized inverse," IEEE Trans. Ind. Informat., vol. 9, no. 2, pp. 890- 898, May 2013.
18. C.-F. Juang and G.-C. Chen, "A TS fuzzy system learned through a support vector machine in principal component space for real-time object detection," IEEE Trans. Ind. Electron., vol. 59, no. 8, pp. 3309-3320, Aug. 2012.
19. J. C. Wang, C. H. Lin, E. Siahaan, B. W. Chen, and H. L. Chuang, "Mixed sound event verification on wireless sensor network for home automation," IEEE Trans. Ind. Informat., vol. 10, no. 1, pp. 803-812, Feb. 2014.
20. P. Nader, P. Honeine, and P. Beauseroy, "lp-norms in one-class classification for intrusion detection in SCADA systems," IEEE Trans. Ind. Informat., vol. 10, no. 4, pp. 2308-2317, Nov. 2014.
21. W. Kim, J. Park, J. Yoo, H. J. Kim, and C. G. Park, "Target localization using ensemble support vector regression in wireless sensor networks," IEEE Trans. Cybern., vol. 43, no. 4, pp. 1189-1198, Aug. 2013.
22. L. Jian, Z. Xia, X. Liang, and C. Gao, "Design of a multiple kernel learning algorithm for LS-SVM by convex programming," Neural Netw., vol. 24, no. 5, pp. 476-483, 2011.
23. G. J. Qi, Q. Tian, and T. Huang, "Locality-sensitive support vector machine by exploring local correlation and global regularization," in Proc. IEEE Conf. Comput. Vis. Pattern Recog. (CVPR), 2011, pp. 841- 848.
24. S. Qiu and T. Lane, "A framework for multiple kernel support vector regression and its applications to siRNA efficacy prediction," IEEE/ACM Trans. Comput. Biol. Bioinf., vol. 6, no. 2, pp. 190-199, Apr./Jun. 2009.
25. Q. Gu and J. Han, "Clustered support vector machines," in Proc. 16th Int. Conf. Artif. Intell. Stat. (AISTATS), 2013, pp. 307-315.
26. J. Beddoes and M. J. Bibbly, Principles of Metal Manufacturing Process, London, U.K.: Arnold, 1999.
27. K. De Brabanter et al. (2011). LS-SVMlab v1.8 [Online]. Available: http://www.esat.kuleuven.be/sista/lssvmlab/
28. S. Srivastava, K. Srivastava, R. S. Sharma, and K. H. Raj, "Modelling of hot closed die forging of an automotive piston with ANN for intelligent manufacturing," J. Sci. Ind. Res., vol. 63, pp. 997-1005, 2004.
29. Y. Li, J. Li, and J. Liu, "Application of BP neural network in the control of hydraulic die forging hammer," in Proc. 2nd Int. Conf. Intell. Comput. Technol. Autom., 2009, pp. 39-41.
30. D. Peng, W. Luo, and Y. Zhang, "Intelligent control based on SVM prediction forging hydraulic press," Comput. Meas. Control, vol. 20, no. 1, pp. 88-90, 2012.
31. A. S. S. Vasan, B. Long, and M. Pecht, "Diagnostics and prognostics method for analog electronic circuits," IEEE Trans. Ind. Electron., vol. 60, no. 11, pp. 5277-5291, Nov. 2013.
32. D. You, X. Gao, and S. Katayama, "Multisensor fusion system for monitoring high-power disk laser welding using support vector machine," IEEE Trans. Ind. Informat., vol. 10, no. 2, pp. 1285-1295, May 2014.
33. X. J. Lu, W. Zou, M. H. Huang, and K. Deng, "A process/shapedecomposition modeling method for deformation force estimation in complex forging processes," Int. J. Mech. Sci., vol. 90, pp. 190-199, 2015.