Akademik Veri Yönetim Sistemi
JOURNAL OF ENGINEERING RESEARCH, cilt.9, sa.3, ss.235-247, 2021 (SCI-Expanded)
Today, manufacturers attach importance to the production of machines
that allow for faster production, reduce labor costs, and minimize operation
errors in order to provide the increasing demand. The seek for such machines
leads manufacturing sector to automation.
In the present study, an automation-supported tapping machine prototype
was manufactured. Kinematic equations were used for determining the location of
the end effector in Cartesian space, whereas inverse kinematic equations were
used for angular positions in joint space relative to positions in Cartesian
space. Based on the results of the kinematic equations, the data obtained in
certain positions were taught to the system through ANN.
The position values for the angles known through the artificial
intelligence algorithm have been taught to the system. Then the position
coordinates to be reached by this manipulator, which has four degrees of
freedom, for the intermediate position coordinate values through artificial
neural networks (ANN) have been obtained. It is expected that the device
controlled by artificial intelligence will not be affected by the variables in
parameter or force changes requiring high working performance. With the control
of the positions through ANN, it has been ensured that the position control of
the tapping robot manipulator is predicted based on artificial intelligence
techniques depending on the angle values of the limbs, and the robot is
prevented from going to a position that is on a different trajectory. Accordingly,
the robot arm has been made controllable with ANN techniques. With ANN
modelling, the position of the end point to perform the tapping process was
estimated with high reliability. To light the way for future research, a rough
simulation was made to see whether the end point would go to a different
position in space.