Abstract
In order to solve the problem that there are many influencing factors and difficult to accurately predict the volume detection of irregular cavity parts, this paper takes advantage of CNN's easy processing of irregular data, fast training speed of GRNN neural network model, and few human intervention factors to establish a CNN_GRNN of CNN model and GRNN neural network embedded combination model. The volume of irregular cavity parts was predicted by this model, and compared with the results predicted by CNN model alone, it was found that the error of the prediction result of the combined model was significantly better than that predicted by the CNN model alone. Therefore, the proposed volume prediction model can effectively predict the volume of irregular cavity parts and improve the detection efficiency.
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