An AI-based automatic weld path recognition method

Abstract

The invention is an AI-based automatic welding seam path identification method, which imports the three-dimensional model data of the workpiece to be welded into the welding robot controller, and the imported three-dimensional model data of the workpiece includes the overall structure of the workpiece to be welded and the welding seam in the workpiece. Compared with traditional automatic welding technology, the application of this method can reduce a lot of programming and teaching time and improve production efficiency; it can quickly generate welding paths for different workpieces, reducing the design of complex tooling links; Existing machining errors or positional deviations in the discharge, this method can also be used to avoid this problem; for different welding types such as fillet welding, vertical welding, lap welding, etc., this method can also achieve different types of welding. The welding method is automatically adjusted.

Description

technical field [0001] The invention relates to the field of robot welding, in particular to an AI-based automatic welding seam path identification method. Background technique [0002] At present, when using a welding robot for welding, it is necessary to manually teach the entire robot motion and the welding trajectory of the welding seam. Therefore, when the structure of the welding workpiece or the shape and position of the welding seam change greatly, manual teaching costs a lot of money. Due to the existence of processing errors, there are also certain differences in workpieces and welds of the same structure, and the use of the original manual teaching trajectory may affect the welding quality. At the same time, due to the lack of welding seam parameter detection equipment in traditional welding robots, the welding parameters cannot be adjusted according to the different welding seams, and the welding quality cannot be effectively guaranteed. In recent years, deep le...

AI Technical Summary

Problems solved by technology

[0002] At present, when using a welding robot for welding, it is necessary to manually teach the entire robot movement and weld trajectory. Therefore, when the structure of the welding workpiece or the shape and position of the weld change greatly, manual teaching will consume a lot of labor cost and time cost; due to the existence of processing errors, there are certain differences between the same structural workpiece and weld seam, and the use of the original manual teaching trajectory may affect the welding quality

At the same time, due to the lack of welding seam parameter detection equipment, traditional welding robots cannot adjust welding parameters according to different weld seams, and cannot effectively guarantee welding quality

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