Welding spot standard curve band fitting system and method

Abstract

The invention discloses a welding spot standard curve band fitting system and method. A data preprocessing module of the system is used for conducting data preprocessing on welding spot basic data formed by a welding robot within a preset time period; the pre-fitting processing module is used for filtering and removing the abnormal resistance data through big data analysis software to obtain pre-fitting data; the fitting processing module is used for carrying out linear iteration fitting by adopting linear regression based on a decision tree to obtain an upper boundary and a lower boundary of a welding spot resistance curve of a welding spot needing to be analyzed in a welding time period; and the fitting post-processing module is used for performing curve denoising on the upper boundary and the lower boundary of the welding spot resistance curve to obtain the corresponding upper boundary and the lower boundary of the welding spot resistance standard curve. Through a big data mathematical statistical analysis means, the function relationship between the welding spot resistance and the time of the welding object and the welded object under the condition that disturbance factors are eliminated is reflected, and the method has important value for further research on the welding spot process.

Description

technical field [0001] The invention relates to the technical field of auto parts manufacturing, in particular to a welding point standard curve band fitting system and method. Background technique [0002] Welding is a common process in modern machinery manufacturing industry, and it is widely used in automobile manufacturing. The welding process uses a welding torch as a tool to connect two or more metal materials of the same or different types into one body in the form of solder joints. The quality of the solder joints has a great impact on the overall quality of the car. With the deepening of intelligent manufacturing, big data technology and artificial intelligence can be used to make welding data generate greater value. For example, it can quickly find quality defects, find out the main factors affecting quality abnormalities, and realize predictive maintenance such as quality inspection and process, so as to achieve the purpose of increasing efficiency, saving energy...

AI Technical Summary

Problems solved by technology

figure 1 The abnormal fluctuation (unsmooth) of the marked welding area resistance versus time curve indicates that "spatter" phenomenon has occurred in the current welding process. The larger the fluctuation jump, the more serious the "spatter" phenomenon, which leads to poor solder joints quality and higher energy consumption

[0005] At present, there are very few technologies for data mining of industrial solder joint big data by means of big data statistical analysis. Even if the research is mainly focused on solder joint spatter, energy consumption, etc., for the construction and research of standard curve bands, still blank

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