# Weld Defect Detection of a CMT Arc-Welded Aluminum Alloy Sheet Based on Arc Sound Signal Processing
## 1 Introduction  
Aluminum alloys are widely used in rail vehicle, aerospace, and automobile manufacturing because of their high specific modulus, high specific strength, corrosion resistance, and good weldability [1; 2]. For aluminum alloy sheets, due to the high thermal conductivity and low stiffness of the material, the traditional method of welding is prone to defects such as weld collapse and burn-through. CMT (cold metal transfer) is an improved short-circuit transition welding process based on MIG welding, which has a low heat input and no welding slag splash and is widely used in thin plate welding, additive manufacturing, dissimilar metal welding, weld cladding, etc. [3; 4; 5; 6; 7; 8; 9]. The CMT welding process can effectively reduce the probability of weld-through occurrence for thin aluminum alloy plate welding. However, in actual production, assembly errors lead to problems such as large or misaligned weld gaps, resulting in serious welding defects such as burn-through and a lack of fusion, especially for thin aluminum alloy plates. In the CMT automated welding process, the real-time monitoring of weld seam status and the automatic identification of defects can effectively improve welding productivity and are also key to controlling weld quality. In the automated welding process, a wealth of information related to weld quality is acquired through sensing technology, including molten pool image signals [10; 11; 12], arc sound signals [13; 14; 15; 16], and electrical signals [17; 18]. Because the molten pool image can directly reflect the weld quality, its processing method is widely used in predicting penetration. However, the visual sensor is susceptible to arc light interference, which makes molten pool image acquisition and processing difficult, especially for aluminum alloy welding, because of its high reflectivity, which makes molten pool image acquisitionand processing even more difficult. In addition, the size of vision sensors also limits their usage. The arc sound generated by welding contains rich information related to the welding process, and experienced welders can identify some abnormal weld problems using this sound [15]. Therefore, research on the relationship between the arc sound and melt-through is gradually receiving attention from a large number of scholars, and a significant amount of research has been conducted on the relationship between the arc sound and penetration states of the weld seam in the GTAW process [13; 16]. However, the droplet transition form, protective gas type, and arc state of different processes are different, resulting in great differences in the arc sound generated during welding [19; 20]. [PERSON] et al. [21] researched the arc sound of CMT lap welding for low-carbon steel. Nevertheless, welding defect identification of aluminum alloy using the CMT arc sound is rarely reported, especially for thin aluminum alloy plates. Machine learning models such as decision trees and neural networks are widely used for welding defect identification [10; 22; 23]. Speech recognition models such as STFT and MFCC are commonly used in the study of welding arc sound [16; 21]. STFT is used in combination with neural networks for GTAW weld penetration state recognition and achieves good results [16; 24]. However, STFT combined with a neural network model for CMT welding defect identification of thin aluminum alloy plates has rarely been reported. In this work, the multi-channel time-frequency spectrum of the arc sound is extracted and input into a custom convolutional neural network to identify CMT welding defects in aluminum alloy thin plates. The remainder of the paper is organized as follows. Section 2 describes a method for defect identification using a custom convolutional network with a multi-channel time-frequency spectrum as the input.