Abstract
The growth of microfluidics has significantly increased the demand for microfluidic products. CO2 lasers are employed to manufacture microfluidic devices using polymethyl methacrylate (PMMA) because of flexibility, time- and cost-effectiveness. However, optimization and defining the relationships among the parameters are challenging. High surface roughness negatively affects the quality of microfluidic devices. This work employed response surface methodology to investigate the effects of power (1.5, 3.0, and 4.5 W), speed (10, 15, and 20 mm/s), and pulse rate (800, 900, and 1000 pulses per inch) on surface roughness in CO2 laser fabrication of microchannels on PMMA coated with a 500 nm layer of 99.95% pure aluminium. A full quadratic model was developed. Analysis of variance (ANOVA) and optimization were done. Power is the most significant factor followed by speed and pulse rate. The optimization results were validated experimentally. The developed model is highly accurate with an absolute percentage error of 2.617%. Manufacturing engineers can use it to efficiently predict surface roughness values.
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Acknowledgements
The authors gratefully appreciate the support of the Science and Technology Development Fund (STDF-12417) project. Special appreciation is extended to JICA for the TICAD7 scholarship offered to the first author. The authors are exceptionally grateful to Asmaa Wadee, Shimaa Elsayed Ibrahim, and Moataz Abdel Karim for their incredible support.
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JLO: conceptualization, methodology, software, data curation, visualization, formal analysis, investigation, validation, writing-original draft preparation. AMFE-B: supervision, resources, project administration, writing-reviewing, and editing. MY: supervision, writing-reviewing, and editing. HAE-H: supervision, resources, project administration, writing-revision, and editing.
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Okello, J.L., El-Bab, A.M.R.F., Yoshino, M. et al. Optimization of surface roughness in CO2 laser ablation of aluminium-coated polymethyl methacrylate (PMMA) using response surface methodology. Multiscale and Multidiscip. Model. Exp. and Des. 6, 451–460 (2023). https://doi.org/10.1007/s41939-023-00158-9
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DOI: https://doi.org/10.1007/s41939-023-00158-9