서지주요정보
하이드로포밍 공정에 있어서 신경회로망을 이용한 성형압력 추정에 관한 연구 = Estimation of forming pressure for hydroforming processes using a neural network
서명 / 저자 하이드로포밍 공정에 있어서 신경회로망을 이용한 성형압력 추정에 관한 연구 = Estimation of forming pressure for hydroforming processes using a neural network / 현봉섭.
저자명 현봉섭 ; Hyun, Bong-Sup
발행사항 [대전 : 한국과학기술원, 1993].
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소장정보

등록번호

8003488

소장위치/청구기호

학술문화관(문화관) 보존서고

DPE 93007

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초록정보

In industry, especially aircraft fields, the hydroforming process is widely used for sheet metal forming, because of its advantage, such as uniform thickness forming. Since the hydroforming operation is performed in the high-pressure chamber controlled by the pressure control valve, the determination of back-up fluid pressure in the chamber is one of the most essential tasks. Recently, to determine the back-up fluid pressure, many investigators have studied the relationships between the forming pressure and material characteristics of the product and geometrical parameters in the forming process by analytic approach of the plastic deformation. However, it's very difficult to obtain such relationships by an analytical method. In this thesis, we present an estimation system which estimates the back-up pressure of hydroforming process utilizing a multi-layered neural network. The neural network learns the nonlinear relationship between the back-up pressure and the geometric state variables of hydroforming process. The proposed method does not necessitate sophisticated analysis on hydroforming process but only needs qualitative understanding of physical phenomena associated with the process. In addition, for the estimation of the forming pressure, we can minimize the error by simplify the modelling of the hydroforming process. A series of experiments are performed for a number of different forming conditions by using various punch shapes, sizes, and drawing ratios. To minimize the number of the experimental data set needed for neural network training, we utilize Taguchi experiment method. From the experiments suggested by the Taguchi method we collected sets of data which yield uniform thickness product and utilized as a set of data for the multi-layered neural network. The experimental results show that the nueral network well approximates the nonlinear relationship between the back-up pressure and the geometric state variables of hydroforming process, thus giving the good estimation of back-up pressure vs punch stroke curve.

서지기타정보

서지기타정보
청구기호 {DPE 93007
형태사항 [vi], 159 p. : 삽도, 수표 ; 26 cm
언어 한국어
일반주기 저자명의 영문표기 : Bong-Sup Hyun
지도교수의 한글표기 : 조형석
지도교수의 영문표기 : Hyung-Suck Cho
학위논문 학위논문(박사) - 한국과학기술원 : 정밀공학과,
서지주기 참고문헌 : p. 56-59
주제 Neural networks (Computer science)
Plastic analysis.
Sheet metal work.
Modeling.
신경 회로망. --과학기술용어시소러스
박판. --과학기술용어시소러스
수압 성형. --과학기술용어시소러스
모델링. --과학기술용어시소러스
소성 역학. --과학기술용어시소러스
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