Resistance spot welding technology is a kind of point connection technology widely used in the manufacture of thin plate structure. Its principle is to apply a certain pressure between two electrodes and the workpiece to be welded, and use the resistance heat generated when the current passes through the workpiece to melt the local metal and form a welding spot process. Resistance spot welding is widely used in automotive, aerospace, home appliance and other manufacturing fields due to its advantages of low cost and high production efficiency[1].
With the development of industry, the quality of resistance spot welding heads is becoming more and more demanding in various applications. However, the resistance spot welding process involves force, heat, electricity, magnetism and flow, which is a highly nonlinear coupling process, which makes it difficult to accurately monitor the quality of the physical model[2]. At the same time, affected by interference factors such as assembly gaps and electrode wear, welding quality problems such as spatter and cracks appear, which increases the uncertainty of the welding process. Therefore, an effective resistance spot welding monitoring technology is needed to realize real-time monitoring of process elements in the welding process and ensure product welding quality.
Since resistance spot welding is a fully closed process, direct observation of the nucleation process is not possible, so the nucleation process can only be inferred by monitoring the physical phenomena accompanying the spot-welding process. The former process signals that can be applied to monitor spot weld quality in real time include dynamic resistance[3, 4],electrode pressure[5] and electrode displacement[6]. Dynamic resistance is one of the most widely used process signals in the field of resistance spot welding quality monitoring. In 1950, Roberts first experimentally discovered the change in dynamic resistance during resistance spot welding of mild steel [7]. The dynamic resistance signal is obtained by measuring the welding current and the voltage signal across the electrode and calculating it using Ohm's law. In the welding high current operating environment, the time-varying characteristics of the current lead to large induced noise in the voltage signal, and the traditional current-voltage ratio algorithm produces serious calculation errors. Gong[8] proposed the current-over-zero derivative ratio method to achieve the calculation of dynamic resistance by analyzing the voltage and current signals of the primary circuit of resistance spot welding and retrograde modeling, which requires calibration of the inductance value of the secondary circuit of the welding equipment before use. Su[9] et al. performed dynamic resistance measurements by a recursive least squares algorithm based on a forgetting factor, and optimized the genetic factor using sensitivity analysis, which can effectively eliminate inductive noise. Ji[10] et al. found a significant trend in electrode displacement during resistance spot welding of thin aluminum alloys. Panza[11] et al. used the analysis of the electrode unique signal from a non-contact sensor installed in the welding machine to monitor the electrode degradation during the welding process and thus analyze the quality of the welding process. Wang[12] established the HMM model for spot welding quality judgment by using the time series model to monitor the waveform curve of the electrode pressure during the resistance spot welding process. Tang’s[13] experimental studies have shown that different electrode pressure signals are obtained when spot welding with different welding equipment is applied to the same material, so the study of electrode pressure signals needs to be differentiated according to the type of welding equipment. Sensing and detection technology for different signals of resistance spot welding process has been relatively mature, however, achieving accurate measurement of dynamic resistance under variable current conditions has not been practically solved and failed to break through the electrode displacement measurement technology of the weld clamp mechanism.
At present, the application of intelligent technology in the field of welding from process optimization, welding quality monitoring and other aspects greatly promote the progress of welding technology. Wang[14] proposes a framework for spot weld quality using signal processing and artificial intelligence techniques that uses a non-invasive Rogowski coil to extract electrical signal features, an RNN to evaluate the size of the hot zone of the welding process, and a new self-organizing mapping-type classifier to detect the time of occurrence. Zhang[15] et al. proposed a new method of converting electrode displacement signals into binary images in order to improve the efficiency of acquiring resistance spot welding monitoring features, and while selecting displacement signals as monitoring welding process features, probabilistic neural networks were used to classify the quality of resistance spot welding welds, which retains as much information as possible about the quality of the weld and avoids the extraction of complex features of the welding process and the selection of intelligent algorithms. Dai[16] et al. proposed a GAN-based data enhancement technique to improve the quality of spot welding process. Firstly, BAGAN and gradient penalty (GP) were used to generate the welding process images, and secondly, a migration learning approach was used to construct an image classifier, and the images generated by BAGAN-GP were added to the training set to improve the classification performance of the classifier. Most of the above methods rely on historical data analysis in the process of welding monitoring, making it difficult to guide timely quality monitoring and subsequent process decisions.
The concept of the digital twin was originally developed by Grieves[17] and is used in the military and aerospace sectors. The digital twin technology has gained widespread attention and practical application in various fields of industry due to its features of virtual-real integration and real-time interaction, iterative operation and optimization, and full-factor data drive. Tao[18] et al. describe the digital twin as a virtual model of a physical entity created digitally to simulate the behavior of the physical entity in the real environment with the help of data, providing a more real-time, efficient and intelligent service oriented to the whole product lifecycle process.
In summary, most of the above-mentioned studies have contributed to the rapid development of resistance spot welding quality monitoring technology. However, there are limitations in the practical application of the current research results, and there is a mismatch between the actual welding conditions and the experimental conditions. To this end, this paper proposes a digital twin-based approach aimed at monitoring the resistance spot welding process in real time, establishing a high-precision quality evaluation model capable of adapting to a variety of situations, achieving dynamic control of welding quality, and ensuring product stability.
The main contributions of this paper are as follows. Establishing a multi-scale twin model based on a real resistance spot welding environment, synchronizing the twin model with the physical model in real time, and realizing resistance spot welding process optimization and quality monitoring on the basis of data collected in the field. Based on historical data and future data, the optimized process parameters are tested in twin space to verify the feasibility of the optimized solution afterwards. Feedback of process parameters to physical entities for online quality control of resistance spot welding to promote stability of product quality during production.