Study on Adaptive Temperature Control During Intergranular Corrosion Test of Corrosion Resistant Alloys Based on Deep Learning
The deep learning computer vision technology is applied on adaptive temperature control during intergranular corrosion test of cor-rosion-resistant alloys.Firstly,the surface boiling motion feature images of the test solution is obtained through image processing to form a training dataset.Then,a convolutional neural network(CNN)is built and trained with the training dataset,and a collaborative temperature control algorithm is developed based on the CNN model and modbus TCP protocol.Finally,an experimental system based on the developed temperature control algorithm was built and tested.Results show that the trained CNN model can accurately recognize the boiling state of the solution with the accuracy of more than 95％.During the intergranular corrosion test,the developed adaptive temperature control algorithm can give correct control instructions according to the boiling state of the solution.The adaptive temperature control with the change of boiling state during the test is achieved.
万方数据
维普
