Method for determining force-magnetic characteristic parameters of material for strong and weak magnetic detection
A technology for parameter determination and magnetic properties, applied in the field of non-destructive testing, can solve problems such as stress identification result errors, and achieve the effect of broad engineering application prospects
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Embodiment 1
[0026] Embodiment 1: as attached figure 1 As shown, the method for determining the force and magnetic characteristic parameters of materials for strong and weak magnetic field detection includes the following steps:
[0027] The first step is to use the J-A model to calculate the relationship between the magnetization of the pipeline sample to be tested and the strength of the external magnetic field under different stresses;
[0028] The second step is to obtain the hysteresis loop I under different stresses according to the relationship between the external magnetic field strength and the magnetization strength calculated in the first step;
[0029] The third step is to intercept the pipeline sample to be tested, and measure the hysteresis loop II of the pipeline sample to be tested intercepted under the stress-free state through the hysteresis loop test;
[0030] The fourth step is to approximate the obtained hysteresis loop I and hysteresis loop II using the particle swar...
Embodiment 2
[0031] Embodiment 2: as attached image 3 Shown, as the optimization of above-mentioned embodiment, in the 4th step, the method that adopts particle swarm algorithm approximation is as follows: J-A model calculation data and the experimental data that hysteresis loop test gathers are approximated, obtain described calculation data and The mean square error of the experimental data, when the mean square error is less than the set value or the mean square error is the smallest, output the force and magnetic characteristic parameters of the J-A model.
Embodiment 3
[0032] Embodiment 3: as attached image 3 As shown, as the optimization of the above-mentioned embodiment 2, when the mean square error is not less than the set value or does not reach the minimum value, continue to use the particle swarm optimization algorithm for approximation until the mean square error is less than the set value or reaches the minimum value or the number of approximation calculations Reached the calculation times set value.
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