Abstract—In the forging industry, like in many other economic sectors, it is common to simulate forming processes before executing experimental trials. An iterative simulation process is more economic than trials only but still takes a lot of time. A simulation with realistic parameters takes many hours. For an economical production the idea of predicting some main results of the simulation by Data mining was developed. Within this paper, the use of four different Data mining methods for the prediction of certain characteristics of a simulated flange forging process are presented. The methods artificial neural network, support vector machine, linear regression and polynomial regression are used to predict forming forces and the lack of volume. Both are important parameters for a successful simulation of a forging process. Regarding both, forging forming forces and lack of volume after the simulation, it is revealed that an artificial neural network is the most suitable.
Index Terms—Data mining, artificial neural network, linear regression, polynomial regression, support vector machine.
Neelam Frederike Rasche, Jan Langner, and Malte Stonis are with Institut für Integrierte Produktion gemeinnützige GmbH Hannover (IPH), Hannover, Germany (e-mail: rasche@iph-hannover.de, langner@iph-hannover.de, stonis@iph-hannover.de).
Bernd-Arno Behrens is with Institute of Metal Forming and Metal Forming Machines (IFUM), Garbsen, Germany (e-mail: behrens@iph-hannover.de).
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Cite: Neelam Frederike Rasche, Jan Langner, Malte Stonis, and Bernd-Arno Behrens, "Predicting Forming Forces and Lack of Volume with Data Mining Methods for a Flange Forging Process," International Journal of Modeling and Optimization vol. 7, no. 6, pp. 363-369, 2017.
