Photodiode-based focus monitoring in ultrashort-pulsed laser structuring of graphite anodes for lithium-ion batteries

In recent years, there has been an increased demand for elaborate monitoring techniques in laser material processing. This has been driven by the need for fast and cost-efficient quality assurance processes. At the same time, ultrashort-pulsed (USP) laser radiation has emerged as a promising technology for creating intricate microstructures in lithium-ion battery graphite anodes due to its high precision and negligible thermal impact. However, the integration of process monitoring in USP laser applications for graphite anode structuring is still unexplored. There is a lack of clarity on suitable sensors, observable parameters, and extractable process-relevant insights. The presented study addressed this gap by demonstrating the capability of state-of-the-art photodiode-based monitoring systems in collecting process-relevant data and deriving valuable insights. A sensor equipped with three photodiodes was employed to address these challenges. Exploratory data analysis and machine learning methodologies were leveraged to develop a data pipeline for processing the acquired information. The data were used to train convolutional neural networks that could accurately predict the focal position. At the same time, the limitations of traditional regression approaches could be shown. The findings advanced the understanding of the possibilities of process monitoring in USP laser applications and emphasized the significance of data-driven approaches in optimizing manufacturing processes.