Resistance Spot Weldability of Low Density Lightweight Steel according to Electrode Shape

Abstract

In this study, resistance spot weldability of lightweight steel with high Al contents was evaluated using various electrode shapes. The six types of electrode shape were prepared with different electrode face diameter and radius. The tensile shear tests were carried out to investigate the failure behaviors. Also, the nugget size and hardness were measured and compared with various electrode shapes. The experimental results show that the acceptable weld current region for low density lightweight steel could be obtained with 10mm electrode face diameter and 76mm electrode face radius.

Welding Bead Inspection Using Image and Multi-Sensor Fusion

“…This is because welding quality is determined through various parameters during the welding process, including voltage, current, gas levels, welding time, and temperature [1]. Among the inspection methods using sensor data, several studies have been conducted, including research using real-time current and voltage data to determine the presence of the welding bead, research establishing the criteria for classifying welding bead defects using artificial neural networks (ANNs), algorithms predicting three types of defective welding conditions occurring during spot resistance welding using artificial neural network algorithms, and studies that have converted sensor data into images and train ANNs to assess welding quality [2][3][4][5]. Additionally, methods for inspecting welding quality using acoustic signals, highly correlated with current magnitude and gas supply, have been proposed [6][7][8].…”

“…At this time, the sensor inspection mainly performs the inspection using voltage, current, and gas amounts that determine the welding quality [1]. In the inspection method using sensors, various studies have been published, including a method of determining and inspecting sensor data measured in real time using a deep learning model and an inspection method of analyzing the waveforms of sensor data through deep learning [2][3][4][5][6][7][8]. In the inspection method using 2D images, methods of inspecting products for defects after dividing the welding in images taken with 2D vision cameras have been announced, and recently, methods of inspecting quality using KNN (K-nearest neighbor), K-means, improved Grabcut, etc., have been announced [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28].…”