# Welding Bead Inspection Using Image and Multi-Sensor Fusion
## 2 Related Works  
Various inspection methods, such as sensor-based inspection and image inspection, as mentioned in the introduction, have been studied to assess the quality of welding beads. However, many small- and medium-sized enterprises still rely on visual inspection for welding quality assessments. This inspection method can suffer from reduced accuracy and slower inspection times, as the inspection results may vary depending on the inspector's condition and expertise. Consequently, there are limitations to performing extensive inspections solely through human visual inspection. In sensor-based inspection, data, such as real-time current, voltage, and gas flow rates, are primarily utilized for analysis. 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  
Figure 1: SRD production and inspection: (**a**) SRD products; (**b**) construction site; (**c**) production machine; and (**d**) inspection table. 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]. [PERSON] et al. proposed a digital twin system for weld sound detection [6]. This system utilized the SeCNN-LSTM model to recognize and classify welding quality based on the characteristics of strong acoustic signal time sequences. [PERSON] introduced a subjective evaluation model that can detect welding process defects based on the hearing of expert welders [7]. Furthermore, [PERSON] proposed an algorithm for classifying welding quality by analyzing the major acoustic signals of GMAW welding [8]. However, when solely relying on sensor data for welding quality inspection, it may be possible to determine the presence of welding defects but confirming the location and shape of welding beads solely based on sensor data can be challenging, potentially leading to defects. Additionally, converting sensor data into images involves complex calculations and lengthy inspection times, and acoustic signal extraction and analysis methods can be quite intricate, making inspections not straightforward. Therefore, solely relying on sensor-based inspection for quality inspection has its limitations. Recently, research on data analysis using multi-sensor/image fusion has also been presented. [PERSON] et al. introduced various machine learning-based multi-sensor information fusion studies that have recently been published [9]. Of note, they have introduced wearable sensors, smart wearable devices, and key application areas, and proposed fusion methods for multi-modal and multi-location sensors. [PERSON] et al. presented a multi-modal vision fusion model (MVFM) for comprehensive policy learning using object detection, depth estimation, and semantic segmentation [10]. However, when evaluating its practical suitability beyond the specific experimental setup, it is important to take into account its limitations related to platform specificity, data availability, and generalizability. Furthermore, [PERSON] et al. explored the integration of satellite data from various sensors at different resolutions to predict topsoil clay content, finding that fused spectral band images consistently outperform spectral indices, leading to a notable 10% improvement in prediction accuracy [11].