Modelling of Surface Roughness in CO2 Laser Ablation of Aluminium-Coated Polymethyl Methacrylate (PMMA) Using Adaptive Neuro-Fuzzy Inference System (ANFIS)

Abstract

High surface roughness hinders the flow of fluids in microchannels leading to low accuracy and poor-quality products. In this work, the adaptive neuro-fuzzy inference system (ANFIS) was used to examine surface roughness in CO2 laser fabrication of microchannels on polymethyl methacrylate (PMMA). The PMMA substrates were coated with a 500 nm layer of 99.95% pure aluminium. The inputs were speed (10, 15, and 20 mm/s), power (1.5, 3.0, and 4.5 W), and pulse rate (800, 900, and 1000 pules per inch) while the output was surface roughness. A 3-level full factorial design of experiments was used, and 27 experiments were conducted. Using the gaussian membership function (gaussmf), the ANFIS model was developed using the ANFIS toolbox in MATLAB R2022a. Analysis of variance was performed to examine the significance of the inputs. Power is the most significant followed by speed and pulse rate. The mean relative error (MRE), mean absolute error (MAE), and the correlation coefficient (R) were used to examine the accuracy and viability of the model. MRE, MAE, and R were found to be 0.257, 0.899, and 0.9957 (R2 = 0.9914) respectively. The root mean square error (RMSE) was 0.0022 and 3.6099 for the training data and checking data respectively. Hence, the developed model can predict the values of the average surface roughness with high accuracy.