Computer-Aided Genetic Algorithm Based Multi-Objective Optimization of Laser Trepan Drilling

The laser trepan drilling (LTD) has proven to produce better quality holes in advanced materials as compared with laser percussion drilling (LPD). But due to thermal nature of LTD process, it is rarely possible to completely remove the undesirable effects such as recast layer, heat affected zone and micro cracks. In order to improve the hole quality, these effects are required to be minimized. This research paper presents a computer-aided genetic algorithm-based multi-objective optimization (CGAMO) methodology for simultaneous optimization of multiple quality characteristics. The optimization results of the software CGAMO has been tested and validated by the published literature. Further, CGAMO has been used to simultaneously optimize the recast layer thickness (RLT) at entrance and exit in LTD of nickel based superalloy sheet. The predicted results show minimization of 99.82% and 85.06% in RLT at entrance and exit, respectively The effect of significant process parameters on RLT has also been discussed.