This book describes load modeling approaches for complex work pieces and batch forgings, and demonstrates analytical modeling and data-driven modeling approaches for known and unknown complex forging processes. It overcomes the current shortcomings of modeling, analysis and control approaches, presenting contributions in three major areas: In the first, several novel modeling approaches are proposed: a process/shape-decomposition modeling method to help estimate the deformation force; an online probabilistic learning machine for the modeling of batch forging processes; and several data-driven identification and modeling approaches for unknown forging processes under different work conditions. The second area develops model-based dynamic analysis methods to derive the conditions of stability and creep. Lastly, several novel intelligent control methods are proposed for complex forging processes.One of the most serious problems in forging forming involves the inaccurate forging conditions, velocity and position offered by the hydraulic actuator due to the complexity of both the deformation process of the metal work piece and the motion process of the hydraulic actuator. The book summarizes the current weaknesses of modeling, analysis and control approaches.  are summarized as follows: a) With the current modeling approaches it is difficult to model complex forging processes with unknown parameters, as they only model the dynamics in local working areas but do not effectively model unknown nonlinear systems across multiple working areas; further, they do not take the batch forging process into account, let alone its distribution modeling. b) All previous dynamic analysis studies simplify the forging system to having a single-frequency pressure fluctuation and neglect the influences of non-linear load force. Further, they fail to take the flow equation in both valves and cylinders into account. c) Conventional control approaches only consider the linear deformation force and pay no attention to sudden changes and the motion synchronization for the multi-cylinder system, making them less effective for complex, nonlinear time-varying forging processes subject to sudden changes.

Hersteller:
Springer Verlag GmbH
juergen.hartmann@springer.com
Tiergartenstr. 17
DE 69121 Heidelberg

XinJiang Lu received the B.E. and M.E. degrees in the School of Mechanical & Electrical Engineering, Central South University, China, and the Ph.D degree in the Department of Manufacturing Engineering and Engineering Management, City University of Hong Kong. Currently, he is currently a Professor of the School of Mechanical & Electrical Engineering, Central South University.  Dr. Lu has broad interest in academic as well as applied research. He has published 1 book with John Wiley & Sons in 2014, and published over 30 SCI journal papers and more than 20 conference papers. As PI, he has taken charge of 8 projects. His current research interests are in modeling and control of hydraulic actuator, data learning, process modeling and control, robust design, integration of design and control.  Dr. Lu serves as one of the Editorial Board Members in both International Journal of Industrial and Manufacturing Systems Engineering and International Journal of Computer & Software Engineering. He was awarded New Century Excellent Talents award by the Chinese Ministry of Education in 2013, Hiwin Doctoral Dissertation Award in 2011, Excellent Thesis Award for Master's Degree of both Hunan Province and Central South University respectively in 2007 and 2006. He is a member of IEEE.  The list of the selected publications: [1]       Lu X.J., Zou W., Huang M.H., A Novel Spatiotemporal LS-SVM Method for Complex Distributed Parameter Systems with Applications to Curing Thermal Process, IEEE Transactions on Industrial Informatics, 12(3), pp.1156 - 1165, 2016. [2]       Fan B., Lu X.J*., Huang M.H., A Novel LS-SVM Control for Unknown Nonlinear Systems with Application to Complex Forging Process, Journal of Central South University, 2016 (Accepted) [3]       Fan B., Lu X.J*., Li H.X., Probabilistic Inference based Least Squares Support Vector Machine for Modeling under Noisy Environment, IEEE Trans. Systems, Man, and Cybernetics: Sys