• Journal of Welding and Joining
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• v.11 no.4
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• pp.44-56
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• 1993

The arc welding processes are substantially nonlinear, in addition to being highly coupled multivariable systems, Frequently, not all the variables affecting the welding quality are known, nor may they be easily quantified. From this point of view, decoupling between the welding parameters from the welding quality is very difficult, which makes it also difficult to control the welding parameters for obtaining the desired welding quality. In this study, a neural network based on the backpropagation algorithm was implemented and adopted for the selection of gas metal arc welding parameters of the fillet joint, that is, welding current, arc voltage and welding speed. The performance of the neural network for modeling the relationship between the welding quality and welding parameters was presented and evaluated by using the actual welding data. To obtain the optimal neural network structure, various types of the neural network structures were tested with the experimental data. It was revealed that the neural network can be effectively adopted to select the appropriate gas metal arc welding parameter of fillet joints for a given weld quality.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.28B no.10
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• pp.771-782
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• 1991

In industrial multivariable plants, it is ofte the case that the plant outputs are measured in similar components not simultaneously but serially. In this paper, the problem of estimating the state vector of the plant based on the data obtained from such a measuring scheme is considered, and a special type of observer(referred to as a $'$multiple serial-sampling$'$ type observer) which renews its internal states whenever a new group of data is obtained is proposed. It is proved that such an observer can be constructed for almost every sampling period if the palnt is observable as a continuous-time multivariable system, and that the poles of the closed-loop system using the multiple serial-sampling type observer consist of the poles of the observer and those of the state feedback system. The behaviors of the observer and the closed-loop system are studied by simulation. The results of simulation indicate that a multiple serial-ampling type observer can estimate the state of the plant more accurately than the ordinary type observers and improve the closed-loop performance, especially, in the existence of measuring noise.ng noise.