Kenji Ohshima

Digital control of torch position and weld pool in MIG welding using image processing device

A digital computer control system for improving the stability of the torch position and weld pool shape in metal inert gas (MIG) welding is proposed. It is shown how the characteristic of the digital controller may be selected for yielding a desired transient behavior. >

Neural network and fuzzy control of weld pool with welding robot

The sensing and control of weld pool depth in robotic welding are discussed. A neural network-based method for measuring the depth is proposed, since the depth cannot be directly measured in real time. The weld pool depth is estimated by using the information obtained from the welding side. The surface shape of the weld pool and the width of the groove gap can be measured during the welding. The weld pool depth can also be measured after the welding. Training data were constructed from these numerical data. When the width of the groove gap changes, the weld pool depth changes too. The feedforward control system for the variation of the groove gap width just under the electrode can be constructed by observing the groove gap width before the electrode. The feedback control system was constructed in order to keep the output of the neural network constant. The fuzzy control system was constructed from the feedback control part and the feedforward control part. The validity of a neuro-fuzzy controller was verified by welding experiments.<<ETX>>

Controlling of torch attitude and seam tracking using neuro arc sensor

It is important to realize intelligent welding robots to obtain a good quality of weld. For this purpose, it is required to detect the deviation from the center of the groove, the torch height, and the torch attitude. In order to simultaneously detect these, the authors propose a neuro arc sensor as the sensor fusion by using a neural network. First, the authors deal with the welding phenomena as the melting phenomena in the electrode wire of the MIG welding. Next, the training data of the neural networks are made from the numerical simulations. A neuro arc sensor is trained so as to get the desired performance by the backpropagation method. The welding experiments are carried out to examine the performance of the neuro arc sensor. A good performance of the neuro arc sensor is obtained. By using it, the seam tracking can be performed in the T-joint welding.

Adaptive control of pulsed MIG welding using image processing systems

A digital computer control system is proposed for improving the stability of the weld pool shape in MIG (metal inert gas) welding. The authors deal with an application of adaptive control and image processing to a welding robot. In a welding system, the parameters vary because the base metal thickness changes during the operation. To obtain the desirable response, adaptive control theory was applied to the control of the weld pool width.<<ETX>>

Sensor fusion using neural network in the robotic welding

It is important to realize intelligent welding robots to obtain a good quality of the welding results. For this purpose, it is required to detect the torch height, the torch attitude, the deviation from the center of the gap. In order to simultaneously detect those, the authors propose sensor fusion by using the neural network, i.e., the information concerning the welding torch is detected by using both the welding current and the welding voltage. First, the authors deal with the welding phenomena as the melting phenomena in the electrode wire of the MIG welding and the CO/sub 2/ short circuiting welding. Next, the training data of the neural networks are made from the numerical simulations. The neuro arc sensor is trained so as to get the desired performance of the sensor. By using it, the seam tracking is carried out in the T-joint.

Control of back weld pool shape in MIG welding by using switch back method

This paper deals with the problem concerning the sensing and controlling of weld pool shape in the MIG welding of plates. In the robotic one side MIG welding process without a backing plate, in order to obtain a good quality of weld, it is important to control the weld pool shape so as to prevent the melting metal from burning through the plate. The method of controlling the weld pool shape is discussed. The moving torch is repeatedly switch changed, known as the switch back method. Primary welding experimental results have proved that the switch back method is effective and satisfactory for controlling the back weld pool shape in one side MIG welding processes without a backing plate.

Fuzzy control in seam tracking of the welding robots using sensor fusion

In order to obtain a goad quality of the welding result, it is important to determine the attitude of the welding torch and to trace the orbit in the welding robots. A sensor fusion system with a CCD camera and a touch sensor is proposed for determining the attitude of the torch and tracking the orbit. First, the shape of the work is measured with the touch sensor. The orbit is estimated from the measurement result. The optimum attitude of the torch is determined against the orbit by using the genetic algorithm. Its attitude is stored into the database. Next, the orbit is taken with a CCD camera. A method of processing the image is discussed for detecting the orbit. The robot traces the orbit with the fuzzy controller. Then the attitude of the torch is controlled according to the database. The validity of the sensor fusion system and the fuzzy controller is verified by performing tracking experiments.<<ETX>>