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With the rapid development of artificial intelligence and intelligent manufacturing, the traditional teaching-playback mode and the off-line programming (OLP) mode cannot meet the flexible and fast modern manufacturing mode. Therefore, the intelligent welding robots have been widely developed and applied into the industrial production lines to improve the manufacturing efficiency. The sensing system of welding robots is one of the key technologies to realize the intelligent robot welding. Due to its unique characteristics of good robustness and high precision, the structured light sensor has been widely developed in the intelligent welding robots. To adapt to different measurement tasks of the welding robots, many researchers have designed different structured light sensors and integrated them into the intelligent welding robots. Therefore, the latest research and application work about the structured light sensors in the intelligent welding robots is analyzed and summarized, such as initial weld position identification, parameter extraction, seam tracking, monitoring of welding pool, and welding quality detection, to provide a comprehensive reference for researchers engaged in these related research work as much as possible.

Welding process, as one of the crucial industrial technologies in ship construction, accounts for approximately 70% of the workload and costs account for approximately 40% of the total cost. The existing welding quality prediction methods have hypothetical premises and subjective factors, which cannot meet the dynamic control requirements of intelligent welding for processing quality. Aiming at the low efficiency of quality prediction problems poor timeliness and unpredictability of quality control in ship assembly-welding process, a data and model driven welding quality prediction method is proposed. Firstly, the influence factors of welding quality are analyzed and the correlation mechanism between process parameters and quality is determined. According to the analysis results, a stable and reliable data collection architecture is established. The elements of welding process monitoring are also determined based on the feature dimensionality reduction method. To improve the accuracy of welding quality prediction, the prediction model is constructed by fusing the adaptive simulated annealing, the particle swarm optimization, and the back propagation neural network algorithms. Finally, the effectiveness of the prediction method is verified through 74 sets of plate welding experiments, the prediction accuracy reaches over 90%.

This study proposed an approach to optimize the process parameters using the entropy weight method combining regression analysis in the resistance spot welding process. Based on the central composite experimental design, tests were carried out with three levels of process parameters for spot-welded titanium alloy sheets. Multiple quality characteristics, namely nugget diameter, maximum displacement, tensile shear load, and failure energy, were converted into a comprehensive welding quality index. The weight for each quality index to obtain the comprehensive welding quality index was determined based on the grey entropy method. The welding heat input for each welding joints was calculated based on the dynamic power signal in the welding process. The mathematical model correlating process parameters and the comprehensive welding quality index was established on the basis of regression analysis. The relationship between the welding process parameters and welding heat was also quantified using a regression model. The effects of welding process parameters on welding quality and welding heat were also discussed. To optimize multi-performance characteristics, the desirability function was employed. The verification test results proved that the method proposed in this paper effectively optimized the welding parameters and kept the welding heat input as low as possible at the same time. Welding current is the most significant parameter affecting the welding quality followed by welding time. This can be owing to its direct influence on the amount of heat supplied to the welding zone during the welding process. The method proposed in this study can serve as a guidance and recommendation for resistance spot welding welders to guarantee welding quality and meet the needs of high production and effective energy saving.

In this investigation, the welding quality of TC2 titanium alloy with 0.4 mm thickness was predicted using two regression models and an artificial neural network model. The welding current and the voltage between the upper and lower electrodes were obtained using the Rogowski coil and a line voltage sensor. And then the variations of the dynamic resistance curve and the effects of the welding current and welding time on the dynamic resistance signals were investigated. The principal component analysis (PCA) was employed to eliminate the redundant information in the dynamic resistance curve and characterize the shape information of the entire dynamic resistance. A linear regression model quantifying the relationship between the nugget diameter and the principal components was established. The results of the analysis of variance indicated that the performance of this regression equation was very good. Some statistical characteristics of the dynamic resistance signal were also extracted to investigate the relationship between the nugget diameter and dynamic resistance. The results indicated that the regression model established based on the PCA technique was much more robust than the model developed on the basis of the features manually extracted from the dynamic resistance signal. The neural network model was also used to predict the nugget diameter of the welding joints utilizing the extracted features. The performances of the three established prediction models were compared and their behavioral discrepancies were also investigated. The PCA technique not only can minimize the prior assumptions about the certain shape of the dynamic resistance curve and remove the subjective factors caused by the manual extraction method, but it also can assess and monitor the welding quality with a good level of reliability.

Friction Stir Welding (FSW) is an innovative and reliable welding technique. Since this method is environmentally beneficial, it has received much attention and development over the past few decades. This study aims to revise the conceptual facts of FSW and evaluate the most recent improvements and developments in its applications. This review also assesses the influences of design parameters such as rotational and welding speeds on weld quality and joint efficiency. Existing challenges associated with applying FSW in various contexts, as well as the potential advantages that might lead to further study and broader FSW applications, are addressed. It has been concluded that FSW allows for optimising the rotating speed based on the preferred welding speed to achieve the greatest tensile strength in the welded materials. Despite FSW being established as effective in laboratory and small-scale applications, utilising FSW for large structures poses challenges. These challenges include maintaining consistent weld quality, controlling heat dissipation, and ensuring joint integrity during FSW. Consequently, further research is required to resolve these challenges and make FSW a promising welding method in contemporary production sectors.

Electrode misalignment, produced by mechanical fatigue or bad adjustments of the welding gun, leads to an increase in expulsions, deformations and quality problems of the welding joints. Different studies have focused on evaluations of the influence of a misalignment of the electrodes and the final quality of the weld nugget. However, few studies have focused on determining a misalignment of the electrodes to avoid problems caused by this defect, especially in industrial environments. In this paper, a method for performing the condition monitoring of electrode alignment degradation was developed following previous research, which has shown the relationship between the misalignment of short-circuited electrodes and the magnetic field generated by them. This method was carried out by means of a device capable of measuring the magnetic field. Finally, an integral system for the detection of misalignments in real production lines is presented. This system set behavior thresholds based on the experimentation, allowing the condition monitoring of the alignment after each welding cycle.

The magnetic pulse welding method using a uniform pressure electromagnetic actuator can effectively weld dissimilar metal plates. However, the existing uniform pressure welding method leads to serious thinning of the plate at the chamfer, lack of welding at the center, and bulging of the welded sample, which seriously affects the quality of the welded joint. To solve these problems and improve the quality of welded joints, a new uniform pressure welding method of dissimilar metal plates based on pre-deformation was developed in this work. In this study, using the welding of an AA1060 aluminum plate and an SS304 steel plate with a thickness of 1 mm as an example, it was confirmed through numerical simulation and experimental research that the pre-deformation of the flyer plate controlled the impact angle of the central area of the plate, and it effectively suppressed central non-welding and serious bulge issues. Further, the welding method also reduced the height of the cushion blocks on both sides, thus mitigating aluminum plate thinning at the chamfer, ultimately improving the tensile strength of the joint. Additionally, a microscopic observation showed that the welding interface formed a wavy composite interface, and the connection strength was good. This welding method can also be extended to welding other dissimilar metal plates.

The comparative detailed study of tungsten inert gas (TIG)-welded aluminium 5083 alloy using two options of welding current, direct (DC) and alternative (AC), is presented in this study. The main motivation for starting this study was the general recommendation to use DC only for TIG welding of mild or stainless steel while AC for aluminium welding. Therefore, it was decided to compare the properties, together with the peculiarities of the welding process and the change in the geometry of the joint. To determine the influence of plate thickness in both processes, 5–10–15-mm-thick plates were selected. As welding practice indicates, DC does not require special preparation of plate edges, while in AC welding, special attention is paid to preparation. One pass is not enough to weld even 5-mm-thick plates in AC, 4 passes were used, and even 18 passes were used when welding 15-mm-thick plates. Using DC saves process time from 2 times and even up to 17 times when welding 5- and 15-mm-thick plates, respectively. When evaluating the hardness of the joints, no difference was observed between the AC and DC samples. The radiographic results showed an obvious advantage of DC-welded joints. The angular distortion measurement summarised the results of the presented comparative study, highlighting the superiority of DC again. As much as 10° of angular distortion was observed in AC welding of 15-mm-thick plates, while in DC welding, a small displacement of 0.2° was barely visible to the naked eye, which is 10 times less in the AC case. Finally, the study proved that the only limiting factor in the use of DC for aluminium welding is the experience and qualification of the welder.

Thermoplastics are widely used in industry and life fields. Laser transmission welding (LTW) provides a solution to improve comprehensive performances of different thermoplastics. This paper reviews LTW process of thermoplastics according to classification of composition and structure of materials, such as the fiber-reinforced thermoplastics (FRP), polycarbonate (PC), acrylonitrile/butadiene/styrene (ABS), polyethylene terephthalate (PET), polypropylene (PP), and polymethyl methacrylate (PMMA). The effects of typical laser parameters, such as laser power and scanning speed on the welding strength of materials, are discussed. The impact of reinforcement or additive on the laser welding process is also elaborated. As an auxiliary to experimental tools, the modeling and simulation methods involved in the welding process are also presented and it is concluded that finite element method (FEM) is the primary techniques employed in modeling and simulations. Response surface methodology (RSM) and artificial neural network (ANN) have also shown advantages in the welding research. Mixed method (MM), which combines various kinds of modeling and simulation methods, can be employed to obtain optimized process parameters more efficiently. In general, still, significant research is needed to improve the welding quality of thermoplastics by combining experiments with modeling and simulations.

In the modern manufacturing industry, the welding quality is one of the key factors which affect the structural strength and the comprehensive quality of the products. It is an important part to establish the standard of welding quality detection and evaluation in the process of production management. At present, the detection technologies of welding quality are mainly performed based on the 2D image features. However, due to the influence of environmental factors and illumination conditions, the welding quality detection results based on grey images are not robust. In this paper, a novel welding detection system is established based on the 3D reconstruct technology for the arc welding robot. The shape from shading (SFS) algorithm is used to reconstruct the 3D shapes of the welding seam and the curvature information is extracted as the feature vector of the welds. Furthermore, the SVM classification method is adopted to perform the evaluation task of welding quality. The experimental results show that the system can quickly and efficiently fulfill the detection task of welding quality, especially with good robustness for environmental influence cases. Meanwhile, the method proposed in this paper can well solve the weakness issues of conventional welding quality detection technologies.