Smart Machining: A Machine Learning Perspective on Carbon Dioxide Based Laser Ablation of PMMA for Microchannel Fabrication

Carbon dioxide based laser writing machines have revolutionized the microfabrication industry. PMMA is widely used as an alternative to glass-based microfluidic devices owing to its Accuracy and straightforward fabrication process. Carbon dioxide based laser writing has outdated the conventional mask-based technique, where mask is used to selectively expose photoresist coated surface to the irradiation. Maskless carbon dioxide writing technique has made the microfabrication fast but challenges of synchronizing various parameters of laser writing system still exist. This paper studies the effects of various parameters of the laser writing system like intensity, speed, and focus level on features of micro patterns. Machine learning-based models are used for predicting carbon dioxide Laser Machine’s parameters based on desired dimensional requirements. The model is also tested with artificially generated data. Artificially generated data is qualified using Kolmogorov–Smirnov Test and Chi-Square test.