Artificial intelligence identification of process parameters and adaptive control system for deep-drawing process

K. Manabe, M. Yang, Shouichirou Yoshihara

Research output: Contribution to journalArticle

29 Citations (Scopus)

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 languageEnglish
Pages (from-to)421-426
Number of pages6
JournalJournal of Materials Processing Technology
Volume80-81
DOIs
Publication statusPublished - 1998 Jan 1
Externally publishedYes

Fingerprint

Adaptive control systems
Deep drawing
Artificial intelligence
Identification (control systems)
Friction
Neural networks
Control systems
Force control
Sheet metal
Lubrication
Process control
Plastic deformation
Materials properties
Plastics
Temperature

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

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title = "Artificial intelligence identification of process parameters and adaptive control system for deep-drawing process",
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.",
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author = "K. Manabe and M. Yang and Shouichirou Yoshihara",
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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.

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