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

• Ashhab M. S. (2008) Optimization and modeling of a photovoltaic solar integrated system by neural networks. Energy Conversion and Management 49(11): 3349–3355

  Article  Google Scholar 

• Chiang Y., Chang L., Chang F. (2004) Comparison of static-feedforward and dynamic- feedback neural networks for rainfall–runoff modeling. Journal of Hydrology 290(3–4): 297–311

  Article  Google Scholar 

• Haykin S. (1998) Neural networks: A comprehensive foundation. Prentice-Hall, Englewood Cliffs, NJ

  Google Scholar 

• Huang W., Kovacevic R. (2011) A neural network and multiple regression method for the characterization of the depth of weld penetration in laser welding based on acoustic signatures. Journal of Intelligent Manufacturing 22(2): 131–143

  Article  Google Scholar 

• Jeguirim S. E., Dhouib A. B., Sahnoun M., Cheikhrouhou M., Schacher L., Adolphe D. (2011) The use of fuzzy logic and neural networks models for sensory properties prediction from process and structure parameters of knitted fabrics. Journal of Intelligent Manufacturing 22(6): 873–884

  Article  Google Scholar 

• Johnston A. B., Maguireb L. P., McGinnity T. M. (2009) Downstream performance prediction for a manufacturing system using neural networks and six-sigma improvement techniques. Robotics and Computer-Integrated Manufacturing 25(3): 513–521

  Article  Google Scholar 

• Makarynskyy O. (2004) Improving wave predictions with artificial neural networks. Ocean Engineering 31(5–6): 709–724

  Article  Google Scholar 

• Merklein, M., Koch, J., Schneider, T., & Oppelt, S. (2010). Manufacturing of complex functional components with variants by using a new metal forming process—sheet-bulk metal forming. In Proceedings of conference on material forming, ESAFORM, Brescia.

• Mungiole M., Keith Wilson D. (2006) Prediction of outdoor sound transmission loss with an artificial neural network. Applied Acoustics 67(4): 324–345

  Article  Google Scholar 

• Patterson D. (1990) Introduction to artificial intelligence and expert systems. Prentice-Hall, Englewood Cliffs, NJ

  Google Scholar 

• Quintana G., Garcia-Romeu M. L., Ciurana J. (2011) Surface roughness monitoring application based on artificial neural networks for ball-end milling operations. Journal of Intelligent Manufacturing 22(4): 607–617

  Article  Google Scholar 

• Radha Ramanan T., Sridharan R., Shashikant K. S., Noorul Haq A. (2011) An artificial neural network based heuristic for flow shop scheduling problems. Journal of Intelligent Manufacturing 22(2): 279–288

  Article  Google Scholar 

• Reddy K., Ranjan M. (2003) Solar resource estimation using artificial neural networks and comparison with other correlation models. Energy Conversion and Management 44(15): 2519–2530

  Article  Google Scholar 

• Reklaitis G. (2002) Engineering optimization. Wiley, New York

  Google Scholar 

• Röhner, S., Breitsprecher, T., & Wartzack, S. (2011). Acquisition of design-relevant knowledge within the development of sheet-bulk metal forming. In: Proceedings of the 18th international conference on engineering design, Kopenhagen, Denmark.

• Tian Z. (2012) An artificial neural network method for remaining useful life prediction of equipment subject to condition monitoring. Journal of Intelligent Manufacturing 23(2): 227–237

  Article  Google Scholar 

• Vandegriff J., Wagstaff K., Ho G., Plauger J. (2005) Forecasting space weather: Predicting interplanetary shocks using neural networks. Advances in Space Research 36(12): 2323–2327

  Article  Google Scholar 

• Wang Q. (2007) Artificial neural networks as cost engineering methods in a collaborative manufacturing environment. International Journal of Production Economics 109(1–2): 53–64

  Article  Google Scholar 

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