Cycle time reduction is one of the crucial tasks in manufacturing that needs to be achieved to maximize productivity and profits. Laser drilling processes, depending on the size and complexity of the parts, require few hundreds to few thousands of holes to be drilled. Therefore, cycle time is directly related to in what order and manner the holes are visited. In this thesis, a method of cycle time reduction for 5-axis percussion laser drilling process is presented via generation of time-optimal trajectory and optimization of hole visiting sequence.

In percussion laser drilling, a series of laser pulses are fired to each hole while the workpiece is stationary. Once a hole is completely opened up, then drilling of the next hole continues by repositioning the workpiece with respect to the beam. This stop-and-go nature of the drilling process enables one to describe the sequence optimization problem as a well-known Traveling Salesman Problem (TSP) in combinatorial optimization. The objective of TSP is to find a minimum cost sequence of points when the point-to-point cost information for every possible pair is known. In the case of the minimum cycle time problem, the point-to-point cost is the travel time, and the objective of TSP is to find a sequence with the minimum overall travel time.

In planning of time-optimal trajectory for point-to-point motion under a specified path, industry uses CNC controller’s G00 (rapid traverse) + TRAORI (5-axis transformation and tool orientation retaining tactic) commands. To be practically beneficial, time-optimal trajectory generation strategies discussed in this thesis is focused on closely estimating these CNC controller’s behaviors. A total of four strategies are studied, and the most accurate strategy is chosen by comparing the results with the experimentally measured CNC trajectories. The most accurate one specifies the tool paths in Workpiece Coordinates followed by iterative velocity profiling of the tool path parameter to achieve minimum time trajectory under the machine’s velocity, acceleration, and jerk limits.

With every hole-to-hole travel time calculated from the above strategy, sequence optimization can be conducted. In this thesis, two methods from the industry partner, the proposed method, and the optimal solver method are discussed. Due to licensing limitations, the proposed method is developed in-house instead of using existing non-commercial TSP algorithms. The proposed method uses local search heuristics approach inspired by famous Lin-Kernighan heuristics. The results are compared to the optimal solutions generated from the non-commercial state-of-the-art TSP solver called Concorde for benchmarking purposes.

To understand the impact of the research in a real environment, one sample part and its original drilling process information have been made available by the industry partner. Although the full experimental results are not yet acquired at the moment of writing this thesis, the simulation results show that the proposed sequencing optimization in conjunction with the proposed hole-to-hole trajectory generation strategy for correct estimation of travel time improves the overall cycle time by 26.0 %.