Convolutional neural network model for synchrotron radiation imaging datasets to automatically detect interfacial microstructure: An in situ process monitoring tool during solar PV ribbon fabrication

Designing means and methods to detect the presence of interfacial bubbles and intermetallic compounds (IMCs) during hot dipping solder coating of Cu ribbon, can help in the production of defect-free PV ribbons. A mechanistic study of Cu6Sn5 IMC grain growth and bubble morphology evolution at the solder-substrate interface is performed with phase field simulation. A machine learning model is utilized to identify the occurrence of bubble (s) and IMC at the material interface of liquid solder and solid Cu. The datasets for the microstructural images consisting of bubble(s), IMC and planar solder/Cu interface are generated using in situ synchrotron radiation (SR) imaging experiment techniques. The integration of in situ SR radiography based non-destructive testing experiments with convolutional neural network model to intelligently detect the interfacial microstructures paves the path for potential industrial application of this technique in the smart manufacturing of defect free and reliable PV ribbon material.

Automated summary

The study uses phase field simulation, machine learning, and in situ synchrotron radiation (SR) imaging experiment techniques to investigate and identify the bubbles and IMCs in Cu ribbon during hot dipping solder coating, potentially paving the way for smart manufacturing of defect-free PV ribbon material.