Long-distance oil and gas transmission pipelines are important energy infrastructures. Currently, there are deficiencies in the automatic tracking accuracy and adaptability of external welding machines during pipeline construction. Operators often need to manually adjust external welding equipment (welding torch) to ensure the quality of the joints. Improving the intelligence of the welding process is an effective way to improve the efficiency and joint qualification rate during the on-site laying of long oil and gas pipelines. This study proposes a detection and control algorithm for the welding torch position and posture, applicable to all position welding of workpieces with arbitrary spatial postures.

This study is the first to design a multisource sensor that combines laser-structured light vision sensing with dual-axis tilt sensing. This multisource sensor combines the advantages of both types of sensing, enabling it to detect the relative position information of the welding torch, as well as the posture information of the welding torch and workpiece. Using this multisource sensor, the algorithm performs integrated calculations of the welding groove size parameters and relative position and posture parameters of the welding torch under any workpiece posture through local groove surface reconstruction. This method fully uses laser line data from images to ensure stable, applicable, and accurate parameter calculations. Through coordinate transformation, the spatial posture (α_w and β_w) of the local workpiece can be obtained. These integrated feature parameters provide the basis for controlling the welding torch's spatial position and posture in any pipeline space all-position welding. Next, a pipeline intelligent welding system with five degrees of freedom based on multisource sensing is constructed. The system, combined with the designed algorithm, achieves real-time control of the welding torch position and posture (e, H, α, and β), meeting welding process requirements and enabling high-quality weld formation control during arc welding.

The experimental results show that the attitude angle feedback control error of the welding torch did not exceed 0.8°, the lateral position tracking deviation was within 0.25 mm, and the height tracking deviation did not exceed 0.63 mm during the pipeline all-position welding process. Compared to existing welding seam detection and tracking systems based on structured light-vision sensing, the proposed algorithm offers superior accuracy and stability. It detects not only the position deviation of the welding torch but also the posture of the welding joint on any unstructured surface with an unknown spatial posture.

The proposed algorithm for detecting and controlling the position and posture of the welding torch can be used to achieve accurate control during pipeline space all-position welding. This advancement significantly improves the intelligence level of pipeline external welding equipment and provides technical support for controlling the position and posture control of the welding torch when welding unknown posture-curved workpieces.

Rotary friction welding (RFW) is an extensively studied and applied solid-state welding method that can achieve high-quality welding between similar or dissimilar materials. RFW involves complex thermal, mechanical, and metallurgical processes, with heat generation occurring due to intense thermomechanical coupling. The stress and temperature histories at the friction interface considerably affect the microstructure evolution and mechanical properties of welded joints. Consequently, a primary focus of RFW research is deeply exploring the mechanisms and processes of thermomechanical coupling, obtaining the stress and temperature histories during welding to guide process-parameter selection and welded-joint-microstructure regulation. However, because of high-speed rotation and substantial plastic deformation during RFW, the temperature evolution and plastic deformation at the welding interface cannot be directly measured experimentally. Thus, mathematical models must be developed to study RFW. Currently, numerical simulation has become the predominant method for RFW theoretical research. With the development of computational technology, various numerical simulation methods have emerged, further elucidating the evolution laws of various physical fields during RFW and supporting theoretical research on the thermomechanical coupling behavior of RFW.

This paper reviews the research progress on the thermomechanical coupling behavior and numerical simulation technologies of RFW. It encompasses the theoretical underpinnings of the friction behavior of RFW, the development of heat-generation models, and the discussion of prevalent analytical and numerical methods for calculating temperature and stress fields during RFW. The proposal and research on RFW have a long history, resulting in the establishment of three friction-behavior theories: slide, stick, and slide-tick friction theories. These theories have informed the development of various thermomechanical coupling heat-generation models as well as material models. Analytical methods directly employ the thermomechanical coupling model to compute analytical solutions for the temperature and stress fields. These methods offer high computational efficiency and provide intuitive insights into heat generation and transfer processes, as well as material flow and deformation characteristics during RFW. However, analytical methods have challenges, such as their reliance on one-dimensional assumptions and simplified boundary conditions. Different stages of friction require distinct mathematical physics equations, complicating the achievement of coherent calculations for the entire welding process. Meanwhile, numerical simulation methods are more various, mainly including thermal conduction numerical models and the finite element method (FEM). As a mainstream numerical simulation method, the FEM can simulate material flow models and friction models, extending from two-dimensional to three-dimensional analyses. This allows for the obtainment of detailed information on temperature, stress and strain fields, residual stress distribution after welding, interface contact, and joint formation during RFW. In addition, the FEM can be effectively integrated with other simulation and prediction methods, such as microstructure evolution simulations and neural networks, offering comprehensive guidance for welded-joint-microstructure regulation and process-parameter selection.

Presently, the simulation accuracy of RFW highly depends on material parameters and boundary condition settings, and the prediction capability of simulation models remains limited. Therefore, further research on welding mechanisms and the introduction of various computational methods to enhance the efficiency and accuracy of numerical simulation technologies while reducing computational costs represent current challenges and developmental directions for RFW simulations.