| Summary: | The use of a micro-compressed air-volume-detection method to detect the volume of irregularly cavitied components has the characteristics of multi-variable coupling and nonlinearity. To solve this problem, a volume-prediction model of irregularly cavitied components based on gray correlation and a particle-swarm-optimization support-vector machine is proposed. In this paper, the gray-correlation method was used to extract the detection parameters that have the greatest correlation with the cavity volume. On the basis of the obtained detection parameters, the SVM algorithm was used to build an irregularly cavitied components volume-prediction model. During model training, since the regression accuracy and generalization performance of the SVM model depend on the proper setting of its two parameters (the penalty-parameter C and the kernel-parameter <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>σ</mi></semantics></math></inline-formula>), and especially on the interaction of the parameters, this paper presents an optimal-selection approach towards the SVM parameters, based on the particle-swarm-optimization (PSO) algorithm. Experiments showed that the prediction model can better predict the volume of irregularly cavitied components, and the prediction accuracy was high, which played a guiding role in intellectual nondestructive testing of the volume of the irregularly cavitied components.
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