Article,
Genetic algorithm based optimisation of end milling parameters
Machine Engineering, 3 (1/2): 116--126 (2003)
Abstract
The paper proposes a new optimization technique based
on genetic algorithms for the determination of the
cutting parameters in machining operations. In metal
cutting processes, cutting conditions have an influence
on reducing the production cost and time and deciding
the quality of a final product. This paper presents a
new methodology for continual improvement of cutting
conditions with GA (Genetic Algorithms). It performs
the following: the modification of recommended cutting
conditions obtained from a machining data, learning of
obtained cutting conditions using neural networks and
the substitution of better cutting conditions for those
learned previously by a proposed GA. Operators usually
select the machining parameters according to handbooks
or their experience, and the selected machining
parameters are usually conservative to avoid machining
failure. Compared to traditional optimisation methods,
a GA is robust, global and may be applied generally
without recourse to domain-specific heuristics.
Experimental results show that the proposed genetic
algorithm- based procedure for solving the optimisation
problem is both effective and efficient, and can be
integrated into an intelligent manufacturing system for
solving complex machining optimisation problems.
