Welding penetration monitoring for pulsed GTAW using visual sensor based on AAM and random forests

On-line welding penetration state monitoring based on passive visual sensing technology in pulsed gas tungsten arc welding (GTAW) has been a hot research topic in industry and academia. Passive vision sensing technology has some disadvantages. It is easy to be affected by some common disturbance during pulsed GTAW, like spattering, the change of arc intensity, the slightly change of focal length and viewing angle of visual sensor, etc. This paper proposed an intelligent penetration monitoring methodology for GTAW by combining pattern-based visual feature extraction method and supervised machine learning method. The proposed methodology mainly includes two parts: (a) visual features extraction for weld pool images based on active appearance model (AAM); (b) the implementation of penetration monitoring based on supervised machine learning method, random forests (RF). The extracted weld pool visual features include width, length and area size of weld pool. Then the extracted features are utilized to train RF model. Further, the trained RF model can implement the evaluation of penetration states and backside weld seam width. The experiments verify that the proposed method is effective with high accuracy and robustness, which also lays the foundation for online welding penetration control.