Journal of Intelligent Manufacturing

, Volume 25, Issue 1, pp 77–84

Neural network based modeling and optimization of deep drawing – extrusion combined process

Authors

    • Lehrstuhl für KonstruktionstechnikFriedrich-Alexander-Universität Erlangen-Nürnberg
    • Department of Mechanical EngineeringThe Hashemite University
  • Thilo Breitsprecher
    • Lehrstuhl für KonstruktionstechnikFriedrich-Alexander-Universität Erlangen-Nürnberg
  • Sandro Wartzack
    • Lehrstuhl für KonstruktionstechnikFriedrich-Alexander-Universität Erlangen-Nürnberg
Article

DOI: 10.1007/s10845-012-0676-z

Cite this article as:
Ashhab, M.S., Breitsprecher, T. & Wartzack, S. J Intell Manuf (2014) 25: 77. doi:10.1007/s10845-012-0676-z

Abstract

A combined deep drawing–extrusion process is modeled with artificial neural networks (ANN’s). The process is used for manufacturing synchronizer rings and it combines sheet and bulk metal forming processes. Input–output data relevant to the process was collected. The inputs represent geometrical parameters of the synchronizer ring and the outputs are the total equivalent plastic strain (TEPS), contact ratio and forming force. This data is used to train the ANN which approximates the input-output relation well and therefore can be relied on in predicting the process input parameters that will result in desired outputs provided by the designer. The complex method constrained optimization is applied to the ANN model to find the inputs or geometrical parameters that will produce the desired or optimum values of TEPS, contact ratio and forming force. This information will be very hard to obtain by just looking at the available historical input–output data. Therefore, the presented technique is very useful for selection of process design parameters to obtain desired product properties.

Keywords

Sheet-bulk metal forming Deep drawing–extrusion Synchronizer ring Neural networks Constrained optimization

Copyright information

© Springer Science+Business Media, LLC 2012