Optimization of Laser Micromachining using Advanced Predictive Deep Learning
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Keywords:
Ultrashort Pulse Laser Micromachining, DNN, MSE, MAEAbstract
Ultrashort pulse laser micromachining is a transformative technology for precision
manufacturing, enabling intricate microchannel fabrication across diverse applications such as
semiconductors and microfluidics. This research presents a novel Deep Neural Network (DNN)-based
simulator designed to autonomously predict optimal laser processing parameters, enhancing energy
efficiency and precision. Implemented in Python within a Jupyter Note- book environment, the simulator
leverages critical inputs, including microchannel dimensions, refractive index, optical absorption
coefficient, and propagation loss, to optimize laser settings for materials like fluorides, germanates, and
silicates. The model achieves high predictive accuracy, with R² scores exceeding 0.98 for pulse duration,
repetition rate, and speed, and 0.93 for pulse energy, as validated through metrics like Mean Absolute
Error (MAE) and Mean Squared Error (MSE). This work establishes a robust framework for automated
parameter optimization, reducing experimental trials and advancing smart manufacturing. Future
enhancements include real-time parameter adjustment and expanded material compatibility, positioning
the simulator as a pivotal tool for industrial and academic applications.
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