Laser Engraving Processes on AISI 304 Stainless Steel: A Multi-Objective Optimization Approach
A. S. Abegunde *
Department of Mechanical Engineering, Federal University of Technology, Akure, Nigeria.
O. O. Ojo
Department of Industrial and Production Engineering, Federal University of Technology, Akure, Nigeria.
P. K. Farayibi
Department of Industrial and Production Engineering, Federal University of Technology, Akure, Nigeria.
A.M Adeyinka
Department of Mechanical Engineering, Auburn University AL 36842, USA.
*Author to whom correspondence should be addressed.
Abstract
Laser engraving is becoming a preferred alternative to conventional machining for industrial post-processing due to its higher accuracy and elimination of tooling challenges. It is widely used for creating precise engravings on components in industries such as aeronautics, medical devices, printing, and general aesthetic applications. This research is developed around AISI 304 stainless steel based on its versatile use in various applications, investigating the influence of key laser engraving processing parameters such as laser power, scan speed, exposure time, and number of passes on output characteristics such as surface roughness, kerf width, engraving depth, and material removal rate. Analysis of variance and main effect plots were used to determine the optimal parameters for individual response while multi-response optimization to obtain a unified parametric combination was done by combining principal component analysis with a grey relational approach. The best combination of these processing parameters obtained for the multi-response optimization is (1, 0, 0, -1) which implies 15 W laser power, 28.57 mm/s scan speed, exposure time of 20 ms and (1) no of pass. The study has been able to establish that integration of grey relational analysis and principal component analysis shows a robust approach in investigating multi-objective optimization of processes.
Keywords: Laser engraving, AISI 304 stainless steel, multi-objective optimization, principal component analysis, grey relational approach