Akademik Veri Yönetim Sistemi
SADHANA - ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES, cilt.2023, sa.48, ss.1-16, 2023 (SCI-Expanded)
In this study, the effect of drilling 6061-T651 aluminum alloy with different lengths of indexable
insert drills, called U drills, on thrust force, torque, and surface roughness was investigated. As input parameters,
length-to-diameter ratio, feed rate, and cutting speed were chosen for experimental works. The optimum values
of the test parameters were determined by the ratio of signal to noise. In addition, output responses were
modeled and compared with Taguchi, artificial neural networks (ANN), and the adaptive neuro-fuzzy inference
system (ANFIS) methods. Both the experimental results and the signal-to-noise ratios derived from the
experimental results were employed in the modeling process. The models with the highest accuracy were created
using ANN when the predicted results from the models were compared to the experimental findings. The MAPE
values of the ANN model created with the SN ratio were obtained as 0.18% for thrust force, 0.17% for torque,
and 1.79% for surface roughness. Converting the output responses to SN ratios and using them in the models
enabled the estimation of thrust, torque, and surface roughness with less error and satisfactory reliability. With
the method proposed in this study, output responses according to input variables can be predicted with higher
precision, resulting in the efficiency and reliability required by the industry.