Electrode cap grinding cycle energy consumption calculation system and method

Abstract

The invention discloses an electrode cap grinding cycle and energy consumption correlation analysis system which is characterized in that a feature information acquisition module is used for acquiring basic feature information of an electrode cap grinding cycle from welding spot main file data corresponding to a welding spot needing to be analyzed and a corresponding welding spot index file through big data analysis software; derivative characteristic information of energy consumption calculation of the electrode cap grinding cycle of each welding spot needing to be analyzed in the three stages is calculated by utilizing the basic characteristic information of the electrode cap grinding cycle; and the energy consumption calculation module obtains the electrode cap grinding cycle energy consumption of each welding spot in the three stages through a self-adaptive Simpson algorithm by utilizing the basic characteristic information of the electrode cap grinding cycle of each welding spot in the three stages and derivative characteristic information calculated by the energy consumption of the electrode cap grinding cycle. According to the method, through a big data mathematical statistical analysis means, mathematical statistical analysis is carried out on mass welding spot data, and the electrode cap grinding cycle energy consumption of the welding robot in the welding spot welding process is accurately calculated.

Description

technical field [0001] The invention relates to the technical field of auto parts manufacturing, in particular to a system and method for calculating energy consumption of an electrode cap grinding cycle. Background technique [0002] Welding is a common process in modern machinery manufacturing, 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 effici...

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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 high energy consumption

[0005] In the existing technical solutions, the judgment of the state of the solder joints is mainly through the installation of corresponding devices and sensors, online collection of solder joint data and photos of solder joint splash gestures, and research on energy consumption; in this method, it is necessary to install corresponding devices and Sensors, and online collection of solder joint data and solder joint spatter attitude information, transmitted to the computer through the CAN line, the method is complex

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