Abstract
In this study, the effects of processing parameters on the geometry of femtosecond laser fabricated microgrooves on 4H–SiC wafer were investigated. To achieve a fast, accurate and intelligent manufacturing process, response surface methodology (RSM) and artificial neural network (ANN) method were applied for modeling and predicting the geometric features of microgrooves. In addition, analysis of variance (ANOVA) was employed to determine the most significant input variables on the responses. The RSM result showed that the third-order polynomial model equation was in high accordance with the experiments. During the analysis, laser power, scanning speed, and scanning times were set as input variables, and the depth, width and surface roughness (Ra) were set as response/output variables. Finally, the predictive capabilities of the RSM and ANN models were compared with each other in terms of coefficient of determine (R2), root mean squared error (RMSE), and relatively error (RE). The results indicated that the ANN method have a higher accuracy compared to those of the RSM model.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
