抄録
A new in-process identification method of material properties and lubrication condition in the deep-drawing process of anisotropic sheet metals is proposed and applied to the adaptive process control of the blank holding force (BHF). The method is based on a combination model of artificial neural network (ANN) and elastoplastic theory. Three delegated plastic deformation properties, i.e. n value, F value and plastic anisotropic coefficient r, were identified using the measured process information at the beginning of the process by means of ANN. The friction coefficient μ and the optimal BHF control path were then calculated from the theoretical model. Furthermore, the friction coefficient was monitored during the entire process, and a closed-loop control was applied to modify the BHF path corresponding to the frictional variation. Experimental results show that the artificial intelligence (AI) control system can cover a wide range of both materials and influential parameters, such as friction and ambient temperature automatically. It is confirmed that the newly developed system is a valid alternative for the quick responsible control system with high flexibility.
本文言語 | English |
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ページ(範囲) | 421-426 |
ページ数 | 6 |
ジャーナル | Journal of Materials Processing Technology |
巻 | 80-81 |
DOI | |
出版ステータス | Published - 1998 |
外部発表 | はい |
ASJC Scopus subject areas
- セラミックおよび複合材料
- コンピュータ サイエンスの応用
- 金属および合金
- 産業および生産工学