Kanak Kalita

Optimizing process parameters for laser beam micro-marking using genetic algorithm and particle swarm optimization

ABSTRACT Laser micro-marking is an efficient technique for permanent marking and logo printing on materials. This study details the selection of an optimal parametric combination for laser micro-marking. In this work, markings were performed on Gallium Nitride (GaN) with varying the levels of marking parameters. The parameters considered in the present work are current (A), pulse frequency (Hz), and scanning speed (mm/sec). This experiment was designed using a “central composite design,” grounded in the response surface methodology. Mark intensity, which is a prominent response in laser marking, was considered the output response. The data interpretation involved analysis of variance (ANOVA) and mathematical modelling between the input parameters. It is essential to determine the relationship and significance of input-output variation. The interaction effect of various input parameters on mark intensity was also studied. Finally, two techniques, namely genetic algorithm (GA) and particle swarm optimization (PSO), were applied, and the optimal settings of input constraints were predicted.

Robust genetically optimized skew laminates

The present research work explores genetically optimized skew laminates, whose stacking sequence has been varied to maximize their fundamental frequencies with the help of an efficient optimization algorithm. Genetic algorithm, rather than being applied blindly with empirical parameters, is tuned with respect to the problem at hand. Following an extensive study, genetic algorithm parameters are selected carefully so as to ensure a robust optimized stacking sequence. The sensitivity of ply angles is also investigated so as to warrant against marginal manufacturing perturbations. The safe limit for variation of ply angles without much hampering the frequency is also recommended. Validation with existing solutions illustrates the efficiency of the procedure. A wide range of results with rectangular/ skew plates of different layers having symmetric/ antisymmetric ply-orientations, subject to different boundary conditions are solved, which demonstrate the efficacy of the approach.

Simultaneous prediction of delamination and surface roughness in drilling GFRP composite using ANN

Delamination in the drilling of polyester composite reinforced with chopped fiberglass is a problematic phenomenon. The material’s structural integrity is reduced by delamination, which results in poor tolerance during assembly and is a primary reason for decreased performance. Surface roughness is another important factor to consider when drilling fiber-reinforced plastics, as surface roughness causes failures by inducing high stresses in rivets and screws. Due to the random orientation of fiberglass and the non-homogenous, anisotropic properties of this material, an exact mathematical model has not been developed yet. Instead, modelling by artificial neural networks (ANNs) is adopted. In the present work, a multilayer perception ANN architecture has been developed with a feed-forward back-propagation algorithm. The algorithm uses material thickness, drill diameter, spindle speed, and feed rate as input parameters and delamination factor (Fd) at the entrance of the drilled hole, average surface roughness (Ra), and root mean square surface roughness (Rq) as the output parameters. The ANN model is then used to develop response surfaces to examine the influence of various input parameters on different response parameters. The developed model predicts that surface roughness increases with increases in feed rate and that a smaller-diameter drill will be advantageous in reducing surface roughness. A reduced feed rate will minimize delamination as well.