Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 421-426 |
| Number of pages | 6 |
| Journal | Journal of Materials Processing Technology |
| Volume | 80-81 |
| DOIs | |
| Publication status | Published - 1998 Jan 1 |
| Externally published | Yes |
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Keywords
- Adaptive control
- Anisotropic sheet
- Artificial neural network
- Deep-drawing
- Friction coefficient
- Identification
- Material properties
- Variable BHF
ASJC Scopus subject areas
- Ceramics and Composites
- Computer Science Applications
- Metals and Alloys
- Industrial and Manufacturing Engineering
Cite this
Artificial intelligence identification of process parameters and adaptive control system for deep-drawing process. / Manabe, K.; Yang, M.; Yoshihara, Shouichirou.
In: Journal of Materials Processing Technology, Vol. 80-81, 01.01.1998, p. 421-426.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Artificial intelligence identification of process parameters and adaptive control system for deep-drawing process
AU - Manabe, K.
AU - Yang, M.
AU - Yoshihara, Shouichirou
PY - 1998/1/1
Y1 - 1998/1/1
N2 - 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.
AB - 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.
KW - Adaptive control
KW - Anisotropic sheet
KW - Artificial neural network
KW - Deep-drawing
KW - Friction coefficient
KW - Identification
KW - Material properties
KW - Variable BHF
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UR - http://www.scopus.com/inward/citedby.url?scp=0005369619&partnerID=8YFLogxK
U2 - 10.1016/S0924-0136(98)00121-6
DO - 10.1016/S0924-0136(98)00121-6
M3 - Article
AN - SCOPUS:0005369619
VL - 80-81
SP - 421
EP - 426
JO - Journal of Materials Processing Technology
JF - Journal of Materials Processing Technology
SN - 0924-0136
ER -