| dc.description.abstract | 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
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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 %. | en |
