Intelligent characterization of spark-assisted chemical engraving (SACE) process using time series classification

Spark-Assisted Chemical Engraving (SACE) requires precise control over key factors to overcome gas film instability and achieve reproducible optimal resolution and machining speed. This paper presents a substantial advancement in the SACE micromanufacturing technique by introducing a composite algorithm. This algorithm leverages deep learning and time series classification for sequence-to-sequence intelligent classification. Two distinct architectures, the Temporal Convolutional Network (TCN) and Long Short-Term Memory (LSTM), were subjected to evaluation. They were trained and optimized using Bayesian optimization, resulting in impressive accuracies of 97.18% for TCN and 96.44% for LSTM. The algorithm relies on TCN due to its superior performance for classifying current data points and subsequently computing derived parameters like gas film formation time, lifetime, mean discharge current and energy. Its versatility is demonstrated across various experimental conditions, showcasing its potential for rapid and accurate systematic studies. By highlighting the algorithm’s applicability in real-time process control for SACE, this study establishes a foundation for future advancements in the field of glass micromanufacturing.