A. MAYYAS ET AL.

1048

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

drilling processes and the selection of cutting parameters

in the case of drilling aluminum-based materials.

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