## 1 Introduction  
Resistance spot welding (RSW) still stands out due to its high efficiency, low cost, robustness, flexibility, and widespread use in metal joining in automatic manufacturing. However, expulsion is a negative phenomenon that can seriously affect the welds quality in some way. In order to ensure the welding quality, numbers of test methods are adopted, such as casual inspection, destructive testing, and nondestructive testing [1; 2; 3]. Nowadays, benefit from the high production efficiency and without a post-weld test procedure, online quality monitoring of resistance spot welding is developing rapidly. Welding parameters are important indicators in online quality monitoring. Scholars have undertaken many studies in recognition of expulsion defects from different characteristic parameters,including the welding voltage, welding current, dynamic resistance, electrode displacement, electrode force, and so on [4; 5; 6; 7]. [PERSON] [8] has proposed an evaluation method based on the online monitoring of the weld quality of spot welded titanium alloys. In this paper, a great oscillation on the electrode force curve/displacement curve was pointed out when expulsion occurred, and the D-value between the original data and the data after linear fitting was used as the expulsion characteristic value. [PERSON] et al. [9] proposed a linear vector quantization (LVQ) neural network system to achieve the expulsion detection. The results show that the LVQ neural network is able to detect the expulsion in different materials. This work also pointed out that the welding force signal was the most important indicator of the expulsion occurrence. [PERSON] et al. [10] proposed an expulsion model for resistance spot welding, and pointed out that expulsion could be described by the interaction between forces from the liquid nugget and its surrounding solid containment. According to the above reviewed works, it shows that the electrode force during the welding process is a suitable and effective signal for expulsion detection online or offline. Besides, signal processing, predicting models, and modern pattern recognition were also introduced in the defect features extraction and identification of resistance spot welding. [PERSON] et al. [11] proposed a method by using wavelets analysis to extract the aluminum alloy shock wave from the electrode force curve of resistance spot welding. The maximum value of the signal was used as the unique feature parameter, and the threshold setting mainly concentrated on the aluminum alloy. The wavelet packet transform obtained a good result in decomposing this kind of signal. [PERSON] et al. [12] introduced the wavelet analysis with three characteristic indexes, including the peak-to-peak value, the kurtosis index, and the pulse index, in RSW to identify expulsion. These characteristic indexes could all identify expulsion from other conditions, while the analyzed signals are intercepted from the electrode force signal. The actual experiment data showed that the expulsion features (impact signal) may exist before the welding time, likely resulting from the squeeze of the electrode or the influence of the oxide film. Therefore, a single index to predict the weld quality may lead to erroneous judgement. A hybrid combination of the artificial neural networks and the multi objective genetic algorithm were adopted to achieve best nugget size, also the number of spots which should be welded before the electrode tip dressing operation was calculated [13]. A predictive model based on elastic nets with multi input welding parameters, including welding current (WC), welding time (WT), and electrode force (EF), and output as tensile shear load bearing capacity (TSLBC) was proposed. The result showed that it was an amenable tool in the design of the RSW [14]. [PERSON]. [PERSON] set up a multi-sensor fusion system that combines the IR measurement, and the ultrasonic measurement together with the traditional process parameters collection.