Abstract
Gas metal arc welding (GMAW) is a welding process in which an electric arc is formed between a wire electrode and a metal workpiece alongside a shielding gas to protect the arc from contaminants. There are several ways in which the molten electrode droplet can be transferred to the weld pool known as metal transfer modes. Identifying the metal transfer mode automatically is essential to monitor and control the welding process, especially in automated processes employed in modern Industry 4.0 manufacturing lines. However, limited research on this topic has been found in literature. This paper explores the automatic classification of metal transfer modes in GMAW based on machine learning techniques with various signals from the welding process, including acoustics, current, voltage and gas flow rate signals. Time and frequency domain features are first extracted from these signals and are used in a support vector machine classifier to detect the metal transfer modes. A feature selection algorithm is proposed to improve the prediction rate from 80 to 99% when all four signals are utilised. When only the non-intrusive acoustic signal is used, the prediction rates with and without the proposed feature selection algorithm are approximately 96% and 81%, respectively. The high prediction rate demonstrates the feasibility and promising accuracy of the acoustic signal–based classification method for future smart welding technology with real-time adaptive feedback control of the welding process.
Highlights
Gas Metal Arc Welding (GMAW) is the welding process in which an electric arc is formed between a wire electrode and a metal work piece that generates enough heat to melt the metals together
The results demonstrate the feasibility of automatic metal transfer mode identification based on various signals, among which the acoustic sensing based approach has a distinct advantage over the others due to its non-intrusiveness and ease of installation, making it a promising plug-and-play solution for GMAW processes
A new GMAW transfer mode classification method has been introduced based on acoustic signal analysis
Summary
Gas Metal Arc Welding (GMAW) is the welding process in which an electric arc is formed between a wire electrode and a metal work piece that generates enough heat to melt the metals together. Research undertaken by Grad et al [13] employed the sound signal for online GMAW process monitoring and found that the major source of acoustic emission is generated by the arc re-ignition in short circuit transfer mode. They discovered that shielding gas composition had a large impact on acoustic signal parameters. A multi-sensor measurement system is developed to simultaneously measure the welding current, arc voltage, gas flow rate and acoustic signals, from which various time and frequency domain features are extracted and selected to train a Support Vector Machine (SVM) classifier for metal transfer mode identification. In combination with the camera, two 660 nm bandpass filters and a 1.5 neutral density filter were used to attenuate the light emitted by the ignition of the welding arc
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