A new force distribution calculation model for high-quality production processes

International Journal of Advanced Manufacturing Technology

Volumn 27, Issue 7-8, 2006, Pages 726-732

  Zhang, Xiang ^a   Kneupner, Klaus ^a   Kuhlenkötter, Bernd ^a  

^a TU DORTMUND UNIVERSITY   (Germany)

Author keywords

Belt grinding; Finite element method; Neural network; Robot Control

Indexed keywords

COMPUTER SIMULATION; FINITE ELEMENT METHOD; GRINDING (COMMINUTION); INDUSTRIAL ROBOTS; MATHEMATICAL MODELS; NEURAL NETWORKS; PROBLEM SOLVING;

BELT GRINDING; ELASTIC GRINDING WHEEL; PRODUCTION PROCESSES; ROBOT CONTROL;

PRODUCTION ENGINEERING;

EID: 29344443026     PISSN: 02683768     EISSN: 14333015     Source Type: Journal    
DOI: 10.1007/s00170-004-2229-x     Document Type: Article

References (15)

1. Hammann G (1998) Modellierung des Abtragsverhaltens elastischer robotergeführter Schleifwerkzeuge. Dissertation, Stuttgart University
2. Schueppstuhl T (2003) Beitrag zum Bandschleifen komplexer Freiformgeometrien mit dem Industrieroboter. Dissertation, Dortmund University
3. Blum H, Suttmeier F-T (2000) An adaptive finite element discretisation for a simplified signorini problem. Calcolo 37(2):65-77
4. Blum H, Schroeder A, Suttmeier F-T (2003) A posteriori error bounds for finite element schemes for a model friction problem. In: Proceedings of Simulation Aided Offline Process Design and Optimization in Manufacturing Sculptured Surfaces, Witten-Bommerholz, Germany, pp 39-47
5. Suttmeier F-T. Error analysis for finite element solutions of variational inequalities. Dissertation, Dortmund University
6. Krause RH (2001) Monotone multigrid methods for signorini's problem with friction. Dissertation, Free University of Berlin
7. Cybenko G (1989) Approximation by superpositions of a sigmoidal function. Math Control Signals Syst 2:303-314
8. Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2(5):359-366
9. Haykin S (1995) Neural networks - a comprehensive foundation. Macmillan, Houndsmills
10. Riedmiller M, Braun H (1993) A direct adaptive method for faster backpropagation learning: the RPROP algorithm. In: Proceedings of the IEEE International Conference on Neural Networks, San Francisco, pp 586-591
11. Hagan MT, Menhaj M (1994) Training feedforward networks with the marquardt algorithm. IEEE Trans Neural Netw 5(6):989-993
12. Ernest R, Bellman E (1961) Adaptive control processes: a guided tour. Princeton University Press, Princeton
13. Donoho DL (2000) High-dimensional data analysis: the curses and blessings of dimensionality. In: Proceedings of the AMS Conference Mathematical Challenges of the 21st Century. http://www-stat.stanford.edu/~donoho/Lectures/ AMS2000/Curses.pdf
14. Demuth H, Beale M (2001) Neural network toolbox user guide. Mathworks, Natick, USA
15. Jolliffe IT (1986) Principal component analysis. Springer, Berlin Heidelberg New York