| Co-Investigator(Kenkyū-buntansha) |
KANEKO Yasuyoshi Saitama University, Faculty of Engineer, Assistant Professor, 工学部, 講師 (10233892)
OSHIMA Kenji Saitama University, Faculty of Engineer, Professor, 工学部, 教授 (70026087)
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| Research Abstract |
In this study, where backing plate or backing material are not used, one side back bead welding system is proposed to obtain a good welding result without the variation of the root gap. In the system, the torch is not only oscillated in the groove, but also moved before and behind like the switch back. During the forward process, a heat is given and is melted the base material and its root edge. During backward process, the droplet is deposited at the root edge and the bridge is formed. After that, the torch is moved forward by high-speed before burn through is generated. This torch motion is repeated. Therefore, there is no incomplete fusion and the stable back bead is obtained.
For this purpose,
1) The system is constructed to do the cooperative control of the torch motion (weaving width, welding speed), the wire feed rate and the power source characteristic, i.e., the stable weld pool penetration is kept and the seam tracking is carried out. The pulsed current becomes peak where the t
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orch approaches the root edge. The droplet is deposited on the root edges. The interface circuit between computer and welding robot, the welding power source and the wire feeder was made, and the cooperative control of the wire feed rate and current waveform was carried out according to the torch position.
2) The technique is proposed for enabling the real-time sensing of the arc length and the extension length of the electrode wire in transient response.
3) The neural network models are constructed by using the extension length in the beginning of the welding, and the welding current at each sampling, the wire feed rate. Its model output is the arc length and the extension length.
The transient response is obtained by carrying out the fundamental welding experiment. The validity of this neural network is confirmed by comparing the output of the model with the experimental result. It is shown that the extension length and the arc length from voltage and current can be estimated and the neural network model is applicable as a sensor of the arc length. By using its model, it is possible to detect the groove shape during the forward process in the switch back motion. Less
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