Phase Change Analysis of Robot Laser Drilling with Accuracy Enhancement by Deep Learning

Jihad Kadhim AbdAli, Maysoon Khazaal Abbas Maaroof
International Journal of Analytical, Experimental and Finite Element Analysis
Volume 9: Issue 3, Sept 2022, pp 50-61

Author's Information

Jihad Kadhim AbdAli1,2 

Corresponding Author
1Ph.D. Power Mech. Engg., Field Crops Dept., Agricultural College, Basic Education College, Al.Qasim Green University, Babil, Iraq
jihadhpvit@gmail.com

Maysoon Khazaal Abbas Maaroof2

2MSc. Information Technology, University of Babylon, Babil, Iraq
Article -- Peer Reviewed
Published online – 14 October 2022

Open Access article under Creative Commons License

Cite this article – Jihad Kadhim AbdAli, Maysoon Khazaal Abbas Maaroof, “Phase Change Analysis of Robot Laser Drilling with Accuracy Enhancement by Deep Learning”, International Journal of Analytical, Experimental and Finite Element Analysis, RAME Publishers, vol. 9, issue 3, pp. 50-61, Sept 2022.
https://doi.org/10.26706/ijaefea.3.9.2211163

Abstract:-
In this investigations, heat transfer mechanism of laser drilling process is analyzed by using Matlab program 8.5. Calculations of the heat impacted zone were also done. The moving boundary condition generates phase shift and influences heat transfer during the laser drilling process. The classical approach is used to study heat conduction in solids, with modifications made to account for the boundary condition transition from Stefan to continuous heat flow. According to the suggested model, for a given laser beam intensity and pulse time, the drilling hole profiles in a certain material will be quite near to one another. Robot placement errors are complicated since there are many different sources for them, programming in the python language examined. The positioning precision of the robot is improved, and its application capabilities are increased, to reinforced machine learning. The suggested methodology offers a simple and direct method for real-time robot position modification in industrial settings during production setup or readjustment situations. a deep learning approach used to increase the operational positioning precision of an articulated robot. Approximately 300 iterations later, the placement accuracy had significantly improved.
Index Terms:-
laser drilling, phase change phenomenon, deep learning, position error .
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