engleski

Using the Ant Colony Optimization method to find an optimal solution in drilling process

The aim of each production process is to reduce the processing time, which ultimately leads to reduction of the total costs in production. Considering the stated objective, Ant Colony Optimization (ACO) method for searching of the optimal tool path in the process of drilling, was used in this paper. In this case the efficiency of production depends on the time required for drilling. ACO method is used to solve NP hard combinatorial optimization problems inspired by the behavior of different species of ants in searching for the shortest route. One of the most extensively studied combinatorial problem is the Traveling Salesman Problem (TSP). Analysis of this technological task is also based on this principle, and we consider an open tour in drilling process where at the end of the process of drilling, tool does not return to the starting point (which depends on the random selection of the proposed algorithm). With the proposed ACO algorithm, a satisfactory solution was achieved in a relatively short time, and the algorithm has shown that is reliable to use. The solution achieved with ACO algorithm was obtained by MATLAB program, and results are compared with the result obtained by manual programming. Simulation of drilling was performed by educational program EMCO WinNC for Sinumerik 840D Mill control unit.

Ant Colony Optimization ; Tool path planing ; CNC programming ; Drilling

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