A development of an automatic learning method for various plastic characteristic using fuzzy clustering and its application
Ichiro Ishimaru, Sheiji Hata, Hideki Saito, T. Asano · 2003
We have proposed a new plastic deformation control method (Ishimaru et al. (1999)) based on work hardening. This method calculates the deformation load in accordance with average plastic characteristic such as n-value and work hardening, but drawback is that the adaptability for each work piece is not satisfactory. These mechanical characteristics of steel bar are a little different in each workpiece. In this case, the adaptability of the control algorithm for the change of the applied product's characteristics is a very important factor. In this paper, a new online learning algorithm utilizing fuzzy clustering is proposed. This new learning algorithm can realize adaptability for the difference of plastic characteristics of each workpiece by deriving and analyzing the abundant data obtained automatically in a mass production line.