Thermal drilling is a novel sheet-metal-hole-making technique that utilizes the heat produced at the interface of the rotating conical tool and workpiece in order to soften the workpiece and pierce a hole into it.In this work, experiments with thermal drilling of galvanized steel were conducted based on the Taguchi L27 orthogonal array.Significant process parameters such as rotational speed, tool angle and workpiece thickness were varied during the experimentation.In thermal drilling, the thermal-drill tool pushes aside a large amount of workpiece material to form a sleeve, which is often referred to as the bushing length.A predictive model for the bushing length was developed using a feed-forward artificial neural network based on experimental data.As the bushing length is closely associated with the tapping process, the influences of the input process parameters play a vital role in fastening galvanized steel with threaded fasteners in diverse engineering applications.The optimization problem was solved by implementing a genetic algorithm under constraint limits to maximize the bushing length.Further, a confirmation test was conducted with the intention to compare the optimum value and its corresponding bushing length predicted by the genetic algorithm.Good agreement was observed between the predicted and the experimental values.
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Hynes et al. (2017) studied this question.