Electric Resistance Spot Welding evaluation by A-scan and B-scan ultrasound analyses

Abstract

The aim is to discuss the ultrasound technique applied to the inspection of resistance spot welding, and to demonstrate the advantages of this technique over the conventionally standardized destructive tests. For this, two experimental procedures were organized, the first using the a-scan (single element transducer) ultrasound technique, in which the effects of the indentation of the spot weld were studied, one of the parameters that can be easily detected by this technique. The second experiment seeks to demonstrate the detection capacity of the ultrasonic b-scan technique (matrix transducer), in which the methodology was applied to seek the correlation between the results obtained by the equipment with the results found in the peel test and also in the macrographic results conventionally known. With these experiments, it was possible to prove the reliability and reproducibility of this technique, showing an increase in precision when related to the normalized known tests, besides the quantitative evaluation that can be made, allowing the statistical collection of data.

Solder Joint Defect Detection in the Connectors Using Improved Faster-RCNN Algorithm

“…However, when k value is increased to a certain extent, the accuracy is basi-cally unchanged, while too many suggestion boxes will greatly reduce the computational efficiency of the network. Therefore, the value range of k is generally defined in [2,10]. The average Intersection-over-Union (IOU) of anchor boxes generated by k-means clustering under different k values are shown in Figure 7.…”

“…Once the above situation occurs, it will greatly reduce the service life of the cable, and even cause a short circuit of the cable due to misconnection and other phenomena, resulting in serious industrial accidents. In order to eliminate such hidden dangers, experts and scholars have successively developed automatic detection systems, committed to replacing people with instruments for completing the boring and repetitive work of cable welding defect detection, and proposed a series of detection methods, such as resistance measurement method [2], voltage drop measurement method and X-ray detection method [3]. However, these methods have not been widely used in the industrial field because of low detection accuracy and high cost.…”

“…Although the target detection algorithm based on deep learning has been widely used in the industry, there are few researches on solder joint defect detection of connectors. The reasons are as follows: (1) there are various kinds of solder joint defects and it is difficult to classify them; (2) The connector pin arrangement is complex, easy to block the solder joints, and difficult to obtain images; (3) The detection accuracy cannot meet the industrial requirements. Based on the above reasons and actual industrial requirements, this paper will adopt Faster-RCNN as the basic target detection algorithm to carry out appropriate algorithm improvement and finally improve the detection accuracy of five solder joint types (one qualified solder joint and four defective solder joints).…”