Method for determining fusion mugget area of resistance spot welding for allautal through technique of syncretizing multiple informations

Abstract

Based on principles of wavelet transform and energy spectrum, information entropy, and modal analysis, characteristic quantities of electropolar voltage, current, displacement and sound signal in spot welding procedure are calculated out. A neural network model created is trained through characteristic quantities and nugget area. Comparing the nugget area calculated from neural network model with the area actual measured determines error value, from which the neural network model is adjusted, is carried out until the error is within required region. The invention provides measured result with high accuracy and small error, suitable to online testing and controlling spot welding resistances of allautals and other metal plates.

Description

technical field [0001] The invention relates to a method for determining the nugget area of ​​resistance spot welding aluminum alloy plates by multi-information fusion technology, which belongs to the control and detection technology of resistance spot welding of aluminum alloy materials. technical background [0002] Many fields of modern industry involve the resistance spot welding technology of metal sheets, especially in the automobile industry. In terms of enhancing the technological competitiveness of enterprises, saving energy, reducing energy consumption and protecting the environment, improving the quality detection technology of resistance spot welding is to achieve its purpose. One of the main ways. At present, the methods of resistance spot welding quality inspection can be divided into two categories: on-line inspection and off-line inspection. 1. Off-line detection, which belongs to post-event inspection. It can provide quantitative data of various solder nug...

AI Technical Summary

Problems solved by technology

In terms of quality parameter selection, most of the current online inspection spot welding monitoring devices cannot provide quantitative data of the nugget, and their quality judgment parameters are not the quantity describing the size of the nugget (such as area, diameter, etc.), but the parameters of resistance spot welding. Process parameters (such as voltage, current, displacement, etc.), that is, indirect parameters that cause process changes due to changes in the nugget, these parameters can only indirectly reflect changes in the nugget; the quality criterion for the nugget is within a predetermined range , as long as the process detection parameters exceed the range, the solder joints will be judged as unqualified, such as the resistance quality monitoring device developed by Waters et al. with a microprocessor as the core, which uses voltage, current and displacement signals as detection parameters, but In terms of quality judgment, a specified range of process detection parameters is used to distinguish qualified and unqualified solder joints, and the accurate value of nugget changes is not given.

[0003] In terms of the number of detection parameters, most of the existing resistance spot welding quality monitoring devices use single, double, and a small number of three detection parameters, and the collected nugget information is less. For example, Hao et al. used current and electrode voltage parameters to measure To study the quality of aluminum alloy resistance spot welding, they extracted some characteristic quantities from the detection parameters, and then used the neural network model to establish a detection system for detecting the quality of aluminum alloy solder joints with the nugget diameter as the detection parameter, but the detection parameters of this system are relatively low. In addition, due to the good electrical and thermal conductivity of aluminum alloy, the nugget is prone to irregular shapes, and the diameter of the nugget is used as the quality inspection parameter, which is likely to cause measurement errors

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