Abstract
As forging processes require to working across a large operation region, input/output samples do not easily satisfy the requirement of data-driven modeling because of many practical constraints involved. This renders forging processes difficult to model accurately. In this chapter, an operation-region-decomposition-based SVD/NN modeling method is presented for modeling of this type of processes. Because the complexity of the system at the local region is much lower than the original system throughout the operation region, the required input signal for modeling at a local region is easier to obtain than the one suitable for the whole region. An SVD/NN modeling method is then proposed to produce a low-order global model from these experiments at all local operation regions. The practical forging experiment finally demonstrates the effectiveness of the proposed method.
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References
S.J. Cho, J.C. Lee, Y.H. Jeon, J.W. Jeon, The Development of a Position Conversion Controller for Hydraulic Press Systems. International Conference on Robotics and Biomimetics (2009), pp. 2019–2022
M. Chen, M.H. Huang, Y.C. Zhou, L.H. Zhan, Synchronism control system of heavy hydraulic press. IEEE Int. Conf. Measur. Technol. Mechatron. Automat. 2, 17–19 (2009)
X.J. Lu, M.H. Huang, System decomposition based multi-level control for hydraulic press machine. IEEE Trans. Industr. Electron. 59(4), 1980–1987 (2012)
P.H. Zhu, L. Zhang, R. Zhou, L. Chen, B. Yu, Q. Xie, A novel sensitivity analysis method in structural performance of hydraulic press. Math. Prob. Eng. 2012(2–4), 1101–1114 (2012)
Q. Liu, X. Bian, Multi-objective optimization of the hydraulic press crossbeam based on neural network and pareto GA. Int. Conf. Adv. Comp. Control (ICACC) 1(8), 52–55 (2010)
T. Soderstrom, P. Stoica, System Identification (Prentice Hall International, 1989)
P.V. Overschee, B.D. Moon, Subspace Identification for Linear Systems: Theory, Implementation, Applications (Kluwer Academic Publishers, Boston, 1996), pp. 57–93
S.L. Dai, C. Wang, F. Luo, Identification and learning control of ocean surface ship using neural networks. IEEE Trans. Industr. Inf. 8(4), 801–810 (2012)
S.H. Jeon, K.K. Oh, J.Y. Choi, Flux observer with online tuning of stator and rotor resistances for induction motors. IEEE Trans. Industr. Electron. 49(3), 653–664 (2002)
R. Lozano, X.H. Zhao, Adaptive pole placement without excitation probing signals. IEEE Trans. Autom. Control 39(1), 47–58 (1994)
G. Marafioti, R. Bitmead, M. Hovd, Persistently exciting model predictive control using fir models. Int. J. Adapt. Control Sig. Process. 45(6), 536–552 (2010)
X.J. Lu, H.X. Li, Sub-domain intelligent modeling based on neural networks. IEEE International Joint Conference on Neural Networks (2008), pp. 445–449
C.K. Qi, H.X. Li, S.Y. Li, X.C. Zhao, F. Gao, Kernel-based spatiotemporal multimodeling for nonlinear distributed parameter industrial processes. Ind. Eng. Chem. Res. 51(40), 13205–13218 (2012)
C.K. Qi, H.X. Li, S.Y. Li, X.C. Zhao, F. Gao, Probabilistic PCA-based spatiotemporal multimodeling for nonlinear distributed parameter processes. Ind. Eng. Chem. Res. 51(19), 6811–6822 (2012)
D. Zheng, K.A. Hoo, M.J., Piovoso, Low-order model identification of distributed parameter systems by a combination of singular value decomposition and the Karhunen-Loève expansion. Ind. Eng. Chem. Res. 41(6), 1545–1556 (2002)
H.X. Li, C.K. Qi, Modeling of distributed parameter systems for applications-a synthesized review from time-space separation. J. Process Control 20(8), 891–901 (2010)
T.S. Seecharan, Probabilistic Robust Design for Dynamic Systems Using Metamodeling, Master thesis, University of Waterloo, 2007
D. Wehrwein, Z.P. Mourelatos, Reliability-based design optimization of vehicle drivetrain dynamic performance. Int. J. Prod. Dev. 5(1-2), 54–75 (2008)
D. Wehrwein, Z.P. Mourelatos, Optimization of engine torque management under uncertainty for vehicle driveline clunk using time-dependent metamodels. J. Mech. Des. 131(5), 861–872 (2009)
D.P. Berrar, W. Dubitzky, M. Granzow, A practical approach to microarray data analysis. Kluwer Academic Publishers 2(1), 82–84 (2003)
J.H. Zhou, C.K. Pang, F.L. Lewis, Z.W. Zhong, Intelligent DIAGNOSIS AND PROGNOSIS OF TOOL WEAR USING DOMINANT FEATURE IDENTIfiCATION. IEEE Trans. Industr. Inf. 5(4), 454–464 (2009)
D. Mathieu, Power law expressions for predicting lower and upper flammability limit temperatures. Ind. Eng. Chem. Res. 52(26), 9317–9322 (2013)
Z.Q. Ge, Z.H. Song, F.R. Gao, Review of recent research on data-based process monitoring. Ind. Eng. Chem. Res. 52(10), 3543–3562 (2013)
Z.J. Jiang, Y. Yang, S.Y. Mo, K. Yao, F.R. Gao, Polymer extrusion: from control system design to product quality. Ind. Eng. Chem. Res. 51(45), 14759–14770 (2012)
M.H. Huang, Y.B. Li, M. Zhang, J.W. Yang, Dynamic performance analysis for die-forging press machine under extremely low speed. J. Central South Univ. 43(11), 1672–7207 (2012)
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Lu, X., Huang, M. (2018). Forging Process Modeling via Multi-experiment Data. In: Modeling, Analysis and Control of Hydraulic Actuator for Forging. Springer, Singapore. https://doi.org/10.1007/978-981-10-5583-6_6
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DOI: https://doi.org/10.1007/978-981-10-5583-6_6
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