Publication Type : Journal Article
Publisher : SAGE Publications
Source : Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering
Url : https://doi.org/10.1177/09544089241231093
Campus : Haridwar
School : School of Computing
Year : 2024
Abstract : A comprehensive analysis investigated the impact of cutting speed, nozzle diameter, gas pressure and the addition of SiC and ZrO2 particles on the surface quality of aluminum alloy 6062. The correlation between experimental and predicted values was established using deep neural network (DNN), support vector machine regression and response surface methodology. To validate the models, root mean squared error and mean absolute error were computed for four hidden layers with the DNN approach. The surface roughness was significantly affected by the higher cutting speed (3000 mm/min) and lower nitrogen gas pressure (10 bar). The results from the developed models closely matched experimental data. Additionally, the study analyzed the impact of laser parameters on crack width due to rapid thermal changes. The scanning electron microscopy, energy-dispersive X-ray spectroscopy and optical microscopy were utilized to examine the laser-cut surface's microstructure for crack formation analysis.
Cite this Research Publication : Vikas Sharma, Jaiinder Preet Singh, Roshan Raman, Gourav Bathla, Abhineet Saini, Machine learning approach for prediction analysis of aluminium alloy on the surface roughness using CO2 laser machining, Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, SAGE Publications, 2024, https://doi.org/10.1177/09544089241231093