Optimization of the multi-hole drilling path sequence for concentric circular patterns

Determination of the optimal path sequence in a multi-hole drilling operation is a challenging task in a manufacturing industry as it facilitates substantial reduction in tool travel distance (path length), machining time and machining cost. It is quite analogous to the travelling salesman problem, which is one of the most fundamental NP-hard optimization problems. In this paper, six well-known metaheuristics, i.e. ant colony optimization, artificial bee colony algorithm, particle swarm optimization, firefly algorithm, differential evolution and teaching learning-based optimization algorithm are applied to determine the optimal path sequences in computer numerically controlled multi-hole drilling operations. Two layouts consisting of four and five concentric circular patterns, and a heat exchanger tube sheet with 2600 holes are considered here as three different test problems. The minimum drill path lengths as estimated using these algorithms are observed to be better than that as determined by the spiral path method. Amongst them, teaching learning-based optimization algorithm performs best with respect to the derived optimal path length, consistency of the solution, convergence speed and computational time. Its distinctiveness over the others is also validated using the paired t -test.