Abstract: An
adaptive learning control scheme intended to the on-line
optimization of sculptured surface cutting process is presented.
The scheme uses a back-propagation neural network to learn the
relationships between process inputs and process states. The
cutting parameters of the process model are optimized through a
genetic algorithms (GA). The capacity of the proposed scheme for
determining optimum process inputs under a variety of process
conditions and optimization strategies is evaluated on the basis
of milling of a sculptured surface using a ball-end mill. The
experimental results show that the neural network could model
the cutting process efficiently, and the cutting conditions such
as spindle speed could be regulated for achieving high
efficiency and high quality. Therefore the proposed approach can
be well applied to the manufacturing of dies and molds.
Key words: Neural
network Genetic algorithm Surface cutting
Received December 12, 2000;
received in revised form December 3, 2001; accepted January 8, 2002
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