A prediction model for keyhole geometry and acoustic signatures during variable polarity plasma arc welding based on extreme learning machine

Abstract

Purpose

The purpose of this paper is to investigate the relationship between the keyhole geometry and acoustic signatures from the backside of a workpiece. It lays a solid foundation for monitoring the penetration state in variable polarity keyhole plasma arc welding.

Design/methodology/approach

The experiment system is conducted on 6-mm-thick aluminum alloy plates based on a dual-sensor system including a sound sensor and a charge coupled device (CCD) camera. The first step is to extract the keyhole boundary from the acquired keyhole images based on median filtering and edge extraction. The second step is to process the acquired acoustic signal to obtain some typical time domain features. Finally, a prediction model based on the extreme learning machine (ELM) technique is built to recognize different keyhole geometries through the acoustic signatures and then identify the welding penetration status according to the recognition results.

Findings

The keyhole geometry and acoustic features after processing can be closely related to dynamic change information of keyhole. These acoustic features can predict the keyhole geometry accurately based on the ELM model. Meanwhile, the predict results also can identify different welding penetration status.

Originality/value

This paper tries to make a foundation work to achieve the monitoring of keyhole condition and penetration status through image and acoustic signals. A useful model, ELM, is built based on these features for predicting the keyhole geometry. Compared with back-propagating neural network and support vector machine, this proposed model is faster and has better generalization performance in the case studied in this paper.

Citation

Wu, D., Chen, H., He, Y., Song, S., Lin, T. and Chen, S. (2016), "A prediction model for keyhole geometry and acoustic signatures during variable polarity plasma arc welding based on extreme learning machine", Sensor Review, Vol. 36 No. 3, pp. 257-266. https://doi.org/10.1108/SR-01-2016-0009