Abstract:
This paper investigates the capability of Laser Surface Textruing (LST) to induce texture on Ti-6Al-4V, aiming on
optimizing process parameters viz. average power, pulse frequency, scanning speed, and gas pressure using the
Driving Training-based Optimization (DTBO) algorithm. Both single and multi-objective optimizations are
conducted to determine optimal parametric settings. The study systematically examines the impression of these
LBM process parameters on various responses. Comparative analyses was performed with five other meta
heuristic algorithms such as Ant colony optimization, Particle swarm optimization, Differential evolution, Firefly
algorithm, Teaching-learning-based optimization, and Artificial bee colony. Furthermore, statistical validation
via paired t-tests confirms the unique effectiveness of the DTBO algorithm. Detailed examination through
developed box plots and convergence diagrams consistently demonstrates DTBO superior performance in terms
of accuracy, minimal variability in optimal solutions, and reduced computational effort. The DTBO achieves a
higher MRR by 35.7 %, 20 %, 11.9 %, 54.7 %, and 33.3 % compared to ABC, ACO, FA, DE, and TLBO,
respectively. Simultaneously, DTBO also achieves a lower ATW by 13.6 %, 14.8 %, 3.02 %, 15.9 %, and 16.1 %
compared to the same algorithms. These results underscore DTBO’s superior performance in achieving improved
MRR values and reduced ATW values across the considered optimization algorithms. Hence, The DTBO algo
rithm demonstrates robustness and applicability in optimizing LBM processes in context of laser texturing, which
may enhance manufacturing efficiency and product quality significantly.
