서지주요정보
다층 신경회로망을 이용한 GMA 용접 공정에서의 용융지의 크기 예측 및 제어에 관한 연구 = A study on the estimation and control of weld pool sizes in GMA welding processes using multilayer perceptrons
서명 / 저자 다층 신경회로망을 이용한 GMA 용접 공정에서의 용융지의 크기 예측 및 제어에 관한 연구 = A study on the estimation and control of weld pool sizes in GMA welding processes using multilayer perceptrons / 임태균.
저자명 임태균 ; Lim, Tae-Gyoon
발행사항 [대전 : 한국과학기술원, 1993].
Online Access 원문보기 원문인쇄

소장정보

등록번호

8003329

소장위치/청구기호

학술문화관(문화관) 보존서고

DPE 93001

SMS전송

도서상태

이용가능

대출가능

반납예정일

초록정보

This thesis presents an on-line quality monitoring and control method to obtain uniform weld quality in gas metal are welding (GMAW) processes. The geometrical parameters of the weld pool such as the top bead width and the penetration depth plus half back width are utilized to assess the integrity of the weld quality. Monitoring of these geometrical parameters is very important for on-line process control as well as for on-line quality evaluation. It is, however, an extremely difficult problem because of the inherent characteristics of the welding process. The monitoring variables used are the surface temperatures measured at various points on the top surface of the weldment which are strongly related to the formation of the weld pool. The surface temperatures are measured using infra-red temperature sensing system. The relationship between the measured temperatures and the weld pool size is implemented on the multilayer perceptrons which are powerful for realization of complex mapping characteristics. The main task of the neural network is to realize the mapping characteristics from the point temperatures to the weld pool sizes through training. After training, the neural estimator can estimate the weld pool sizes from the leamed mapping characteristics. The design parameters of the neural network estimator such as the number of hidden layers and the number of nodes in a layer, are chosen based upon an estimation error analysis. A series of bead-on-plate welding experiments was performed to assess the performance of the neural network estimator. The experimental results show that the proposed neural network estimator can estimate the weld pool sizes with satisfactory accuracy. To estimate the weld pool sizes in the region of transient states, the time history of the surface temperatures is used as the input to the neural estimator. Various types of the input to the neural estimator such as the type of input information and the number of terms in the time sequence of the surface temperatures are tested. Estimation of the weld pool sizes in the region of transient states is very important for on-line quality monitoring and control. The control purpose is to obtain uniform weld quality in GMA welding process. In this research, the weld pool size is directly regulated to a desired one. The proposed controller is composed of the neural pool size estimator, the neural feedforward controller and the feedback controller. The pool size estimator predicts the weld pool size under growing. The feedforward controller compensates for the nonlinear characteristics of the welding process. The simulation study and experimental results show that the proposed control method improves the overall system response in the presence of changes in torch travel speed during GMA welding and guarantees the uniform weld quality.

서지기타정보

서지기타정보
청구기호 {DPE 93001
형태사항 151 p. : 삽도, 사진 ; 26 cm
언어 한국어
일반주기 저자명의 영문표기 : Tae-Gyoon Lim
지도교수의 한글표기 : 조형석
지도교수의 영문표기 : Hyung-Suck Cho
학위논문 학위논문(박사) - 한국과학기술원 : 정밀공학과,
서지주기 참고문헌 : p. 141-146
주제 Gas metal arc welding.
Welding --Research.
Neural networks (Computer science)
Temperature measurements.
Feedforward control systems.
아크 용접. --과학기술용어시소러스
용융지. --과학기술용어시소러스
신경 회로망. --과학기술용어시소러스
온도 측정. --과학기술용어시소러스
피드포워드 제어. --과학기술용어시소러스
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