1. Introduction
With the booming development of miniaturization and integration of electronic devices, micro enameled wires have been increasingly used in the manufacture of various electronic products, such as electroacoustic devices, micro coils, micro delay and chip inductor et al. [
1,
2]. The micro resistance spot welding (MRSW) of enameled wire to pad is a common production process, since the removal of insulation coating and the joining of wire to pad can be accomplished efficiently in a welding process. Because of advantages such as low cost, high efficiency and ease of automation, MRSW is an important micro joining technology for the manufacture of micro devices, which is also termed small-scale resistance spot welding [
3,
4].
In view of the demand of reliable signal transmission in electronic components, especially the high fidelity requirement of electroacoustic devices in smartphones and earphones, it is important to ensure the quality of each MRSW weld. However, quality control still relies heavily on manual inspection, which requires experienced people to do it with great attention and still overlooks some defects. It is essential to realize on-line quality monitoring.
Several studies have recently focused on the quality control of MRSW [
4,
5,
6,
7,
8]. Wan et al. [
4] developed a weld quality evaluation model based on electrode voltage recognition for the MRSW of 0.4-mm-thick titanium alloy sheets. Wan et al. [
5] compared the quality assessment performance between back propagation neural network (BPNN) and probabilistic neural network (PNN) in MRSW, and it was concluded that BPNN was more appropriate for failure load estimation and PNN was more effective at quality classification. Yue et al. [
6] established regression equations between welding parameters and weld quality with response surface methodology for foil metal joining. Chen et al. [
7] designed multi-performance optimization for MRSW with a hybrid approach. Rikka et al. [
8] optimized the welding parameters by Taguchi design to obtain low electrical contact resistance for joining a nickel tab to an inner aluminum casing in a cylindrical cell. However, despite the great demand in industry, there is a lack of investigation into quality monitoring in the MRSW for micro enameled wire joining.
Quality assessment for resistance spot welding (RSW) has been studied extensively [
9,
10]. Different process signals are analyzed for feature extraction, such as dynamic resistance [
11], welding power [
12], electrode force [
13], electrode displacement [
14] and multiple signals [
15]. Dynamic resistance has wide application because of the low cost and convenient installation of sensors. In recent years, machine learning models have been found to be effective for predicting weld quality, including artificial network [
16,
17], decision tree [
18], random forest [
19] and SVM [
20]. The rapid development of machine learning technology provides effective decision support tools for weld quality evaluation.
Despite comprehensive studies on the weld quality of MRSW and RSW, specific applications require further investigation. First, compared with RSW or MRSW of metal sheets, MRSW of enameled wire to pad is quite a different physical process and has rarely been explored before. Additionally, a class imbalance problem exists in the quality classification of MRSW in the production. This refers to data within which one or more of the classes (majority class) have a much greater number of samples than the others (minority class) [
21]. As a result of quality control, abnormal welds are generally far fewer than normal welds in the manufacture of micro components by MRSW. Traditional classification models cannot provide good performance for class-imbalanced data, which requires imbalanced data processing methods like anomaly detection algorithms [
21].
Based on an imbalanced dataset from industry, this work focuses on the quality monitoring for MRSW of micro enameled wire to pad. The welding process was analyzed in terms of dynamic resistance and heat input, and they were adopted for feature extraction. A classification approach based on isolation forest (iForest) is proposed to assist in the quality inspection.
3. Results and Discussion
The particularity of the quality classification in this study can be illustrated by the feature distribution of normal welds and abnormal welds shown in
Figure 8. The Lim
L and Lim
U of Δ
Q10 are selected to –0.1 and 0.1, respectively, because 97.5% welds are at this interval. According to the statistical analysis, 52,291 welds meet the conditions of Δ
Q10N ∈[0.4, 0.6) and Δ
S10N ∈[0.0, 0.2), and 79.6% (89, 094) welds meet the conditions of Δ
Q10N ∈[0.3, 0.7) and Δ
S10N ∈[0.0, 0.5). This reflects the fact that most welds are similar to their ‘near neighbors’ in terms of
QE and
Rt profile.
It can be observed that in this imbalanced dataset, abnormal welds overlap with normal welds in part of the two-dimensional characteristic space. The combination of class imbalance and class overlap makes it difficult to construct the frontier of each class, which adds complexity for quality classification. The dataset of normal welds may contain certain abnormal welds because of misjudgment, which may make abnormal welds overlap ‘normal’ (misjudged) welds. However, the amount of such polluted data was limited, since the weld quality was inspected by two inspectors in this experiment.
The weld failure detection methods based on traditional classification models appear to be unsuitable to detect abnormal welds from the imbalanced data. First, such models generally adopt global performance measures such as prediction accuracy in the learning process. If all welds are predicted as normal welds, a high accuracy score (>99.75%) can be obtained because of the low defect rate (<0.25%), leading to poor detection of weld failure in this application. Second, traditional classification models are based on supervised learning for the class-balanced data in general, while there is not enough real data of weld failure available for model training and testing in this study, since the abnormal welds do not often occur in reality. Third, the problem of class overlap also makes it difficult to distinguish between normal welds and abnormal welds by traditional classification models.
It is an essential task to find out abnormal welds in the quality monitoring of MRSW.
Figure 9 shows the performance difference of anomaly detection for three models and four defect types. The parameter
c or
nu provided in the packages is used to adjust the model decision function.
For the detection of incomplete fusion welds, both iForest and OCSVM can distinguish all the incomplete fusion welds from normal welds, at the cost of the decrease in the specificity. The LOF model appears to be unsuitable for this application because its recall of incomplete fusion welds cannot reach 100% when c is near its upper limit 0.5. For the detection of other defect types, none of these models can identify the abnormal welds effectively, since no valid features have been extracted from ut, it and variables calculated from them so far.
To analyze the classification of incomplete fusion welds and normal welds further, AUC, specificity and processing time are adopted, as listed in
Table 6. The average training time and average test time over the range of
c or
nu are used to compare the model efficiency. The LOF model is time-saving for model construction in this application, but it cannot identify all the incomplete fusion welds. Hence, its AUC is regarded as NA. The performance of OCSVM for anomaly detection is not poor; however, it is relatively time-consuming to train and test the model. The identification of incomplete fusion welds and normal welds can be performed effectively and efficiently by the iForest model. It has a high AUC score of 0.9525, and it takes 1.79 s to train the model with over 60,000 welds in the training set, and 1.11 s to test over 47,000 welds.
In addition to identifying abnormal welds, the interpretation of anomalies is also important for quality monitoring. For incomplete fusion welds, one of them and its previous 10 welds are shown as an example in
Figure 10a,c,d. It can be observed that this abnormal weld has a different
Rt profile and smaller
QE. The previous welds have an ‘Up&Down’ profile in the 1st pulse, while the abnormal weld has an ‘Up–Down’ profile in the double pulses. This implies that the insulation coating was not properly removed as designed in the 1st pulse for this abnormal weld. Since the welding time was fixed, this weld lacked enough energy to join the wire to the pad after the insulation coating was removed. Based on the assumption that normal welds are ‘many and similar’ and anomalies are ‘few and different’, the iForest model can detect incomplete fusion welds with good performance in this application.
For other defect types, one from abnormal wire welds is analyzed as an example (
Figure 10b). It was caused by the deviation of wire position. As shown in
Figure 10e,f, it overlaps with its previous 10 welds in terms of
Rt profile. This is related to the characteristics of the MRSW process for micro enameled wire joining, implying that the case of abnormal wire which occurs in the early and middle stage of electrode life does not obviously influence the temperature rise of electrode tip and tinned pad. Therefore, there is no remarkable difference in either Δ
Q10N or Δ
S10N between normal welds and these abnormal welds, meaning that the models cannot detect them effectively.
Although only incomplete fusion can be detected by the present iForest model with good performance, it is favorable for the quality monitoring research for MRSW. Its detection requires inspectors to be experienced and focused, which makes it relatively difficult to perform visual testing in the production line, while the deviation of wire position can be identified as abnormal wire with relative ease by a machine vision system. To further separate various abnormal welds from normal welds, more features from other process signals are required to fully represent the welding process. Similar to RSW, the quality monitoring based on multiple signals may be better for MRSW than that based on electrical signals only. Electrode displacement, dynamic force and images before and after welding are expected to be added in future work to improve quality monitoring.