A. MAYYAS ET AL.
between the model outputs and the measured outputs.
The mean absolute relative errors were 0.82% for torque
and 2.89% for thrust force models, while MRA model
error values were 7.10% and 12.59%, respectively.
Hence, these models can be used efficiently for predic-
tion potentials for non-experimental patterns which, in
turn, save experimental time and cost. It was shown that
ANN performs well in mapping nonlinear relationships
between inputs and outputs. If both MRA and ANN
models are considered they will provide statistically sat-
isfactory prediction results. ANN methodology consumes
less time and gives higher accuracy. Hence, modeling the
drilling process using ANN is more effective compared
with MRA. The two proposed models are good in mod-
eling and predicting the drilling forces, which in turn can
provide a valuable tool for many similar applications of
modeling methods in engineering design and manufac-
turing. The developed modeling methods in this paper
can aid the prediction, optimization, and improvement of
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