The forming process of welds involves various factors such as environment, equipment, process, and materials. Among them, the influence of process parameters is the most obvious. Welding process parameters are the basis for affecting the size of the formed part, and each parameter will interact with each other to affect the geometric size of the weld seam. The welding process parameters play a crucial role in the shape of the molten pool, the formation of the weld seam, and the microstructure and properties, as they determine the strength and quality of the welded parts. The welding process parameters of arc welding robots are welding current or wire feeding speed, welding voltage, welding speed, wire diameter, wire extension length, gas flow rate, etc. This article adopts the experimental testing method of secondary universal rotation assembly to establish a secondary regression model between weld bead size and welding voltage, welding current, and welding speed. This model can effectively predict weld bead size, using a reasonable width to improve the quality, fatigue strength, shear strength, etc. of the weld seam.
For the complex test environment and uneven road surface, a sensor multisource fusion framework was used for point cloud map construction and localization. The internal parameters of IMU were first calibrated, and then the spatial parameters of LiDAR and IMU were calibrated. The calibrated multi-sensor fusion algorithm has better map building effect and stronger localization robustness, which is valuable for mobile robot localization and navigation applications, through experiments at Taian Jichuang robot test site.
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