Penetration/keyhole status prediction and model visualization based on deep learning algorithm in plasma arc welding

Accurate keyhole status prediction is critical for realizing the closed-loop control of the keyhole plasma arc welding (K-PAW) processes for acquiring full-penetration weld joints with high efficiency. Visually captured weld pool images from topside provide sufficient information of the liquid metal as well as keyhole behaviors. Weld pool, plasma arc, and keyhole entrance could be clearly recognized reflecting the different features during different keyholing stages. It was proposed to extract the image features automatically based on a deep learning algorithm rather than manually selecting characteristic parameters. Since directly training the deep CNN (convolutional neural network) model using the acquired data led to convergence failure, a well-trained generalized model was employed and fine-tuned accordingly to more easily extract the K-PAW image features. Model training was conducted using obtained dataset, which took weld pool images as input and penetration/keyhole status (partial penetration with a blind keyhole or full penetration with a through keyhole) as output. Underlying correlations between the penetration/keyhole status and topside weld pool images were established. For further verifying the effectiveness and reliability of the trained model, experiments were designed acquiring typical slow keyholing under constant welding current and rapid keyhole switching under pulse welding current. Based on the given data, the verified 90% accuracy was achieved for correctly predicting the keyhole/penetration status. Finally, the visualization of the convolutional layers was carried out, and displayed the features clearly, which is of great significance for understanding the internal mechanism of the neural network.