Prediction of Cu-Al weld status using convolutional neural network

Welding copper (Cu) and aluminum (Al) result in brittle intermetallic (IMC) phases, which reduces the joint performance. The key for a strong joint is to maintain an optimum amount of Al and Cu composition in the joint. To implement this without the destruction of the sample is a challenge. For this purpose, high-resolution images of the weld zone are utilized after welding. With the image processing technique, the presence of (Al/Cu) material melted is distinguished. Therefore, the different weld type/status like insufficient melt, optimum melt, and excessive melt is detected from the images.
This paper analyses the weld images and applies the convolutional neural network technique to predict the weld type. The microstructure and Energy Dispersive X-ray Spectroscopy (EDS) analysis of the fusion zone for each weld type are correlated to the weld images.