A novel real-time measurement method for dynamic resistance signal in medium-frequency DC resistance spot welding

Hwang

et al. 2021

Metals

An efficient nondestructive testing method of resistance spot weld quality is essential in evaluating the weld quality of all welded joints in the automotive components of a car body production line. This study proposes a quality prediction algorithm for resistance spot welding that can predict the geometrical and physical properties of a spot-welded joint and evaluate weld quality based on quality acceptance criteria. To this end, four statistical models that predict the main geometrical and physical properties of a spot-welded joint, including tensile shear strength, indentation depth, expulsion occurrence, and failure mode, were estimated based on material information, dynamic resistance, and electrode displacement signals. The significance of the estimated models was then verified through an analysis of variance. The prediction accuracies of the models were 94.3%, 93.4%, 97.5%, and 85.0% for the tensile shear strength, indentation depth, expulsion occurrence, and failure modes, respectively. A weld quality evaluation methodology that can predict the properties of a spot-welded joint and evaluate the overall quality requirements based on authorized welding standards was proposed using the four statistical models.

Section: Methodsmentioning

confidence: 99%

Weld-Quality Prediction Algorithm Based on Multiple Models Using Process Signals in Resistance Spot Welding

Hwang

et al. 2021

Metals

“…The use of current and voltage data for process monitoring goes beyond arc welding for processes like laser welding, RSW, impact welding and even WAAM. Dynamic resistance, a parameter analyzed in RSW process, involves measuring current and voltage signals [7]. In impact welding processes like magnetic pulse welding (MPW), the current signal gives the circuit rise time, which is further used to calculate the parameters like impedance, overall efficiency, etc.…”

Section: Process Monitoring Applicationsmentioning

confidence: 99%

Digitization of Welding Processes

Kapil¹,

Quadir²,

Sharma³

2021

Preprint

Welding processes offer a unique capability with a wide range of applications in industries. In recent times, welding has established itself as a tool for large scale additive manufacturing. In general, the quality and repeatability assurance for welding and specifically for additive manufacturing necessitates integrating process monitoring techniques with existing welding and additive manufacturing processes. The process-specific signals such as welding current fluctuations, temperature, and acoustic, generated during the welding operations, make them a suitable candidate for digitization. This chapter comprehensively describes the process monitoring techniques relevant to welding and additive manufacturing. Firstly, various sensors used during welding are described for their construction and working. Subsequently, specific applications of the sensors in digitizing the welding processes are presented.

“…These advantages make this method one of the most used technologies for joining metals in industries such as aerospace or automotive industries [ 1 , 2 ]. For example, currently, over 90% of assembly work in a car body is completed by RSW, a vital joining process for automotive production [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Incipient Wear Detection of Welding Gun Secondary Circuit by Virtual Resistance Sensor Using Mahalanobis Distance

Ibañez

García²,

Soret

et al. 2023

Sensors

Wear of the secondary of the welding gun, caused by mechanical fatigue or due to a bad parameterization of the welding points, causes an increase in quality problems such as non-existent welds or a reduced weld nugget size. In addition to quality problems, this defect causes production stoppages that affect the final cost of the manufactured part. Different studies have focused on evaluating the importance of different welding parameters, such as current, in the final quality of the welding nugget. However, few studies have focused on preventing weld command parameters from degrading or changing. This investigation seeks to determine the wear of the secondary circuit to avoid variability in the current supplied to the welding point caused by this defect and the increase in circuit resistance, especially in industrial environments. In this work, a virtual sensor is developed to estimate the resistance of the welding arm based on previous research, which has shown the possibility of detecting secondary wear by analysing the duty cycle of the power circuit. From the data of the virtual sensor, an anomaly detection method based on the Mahalanobis distance is developed. Finally, an integral system for detecting secondary wear of welding guns in real production lines is presented. This system establishes performance thresholds based on the analysis of the Mahalanobis distance distribution, allowing monitoring of the secondary circuit wear condition after each welding cycle. The results obtained show how the system can detect incipient wear in welding guns, regardless of which part of the secondary the wear occurs, improving decision-making and reducing quality problems.