@thesis{thesis,
		author={Novrianto  Elfizar},
		title ={OPTIMIZATION OF PCB DRILLING CNC MACHINE CONTROL SYSTEM 
DESIGN BASED ON ANT COLONY OPTIMIZATION (ACO)},
		year={2024},
		url={http://repository.undar.ac.id/id/eprint/887/},
		abstract={Printed Circuit Board (PCB) is a micro (small) sized board that contains various 
electronic components that are used in an automatic circuit. PCB drilling is usually done 
manually with human power, which takes a lot of time when there are more and more holes 
in the PCB. And precision is needed when the drill bit touches the PCB board which creates 
frictional forces and can cause drilling errors. 
 
This research uses data collection stages after carrying out several simulation 
methods using Matlab 13a. The optimal method division includes without control, 
Conventional PID, auto PID and PID - ACO. The aim of this research is to determine the 
advantages of the Ant Colony Optimization (ACO) method in controlling Computer 
Numerical Control (CNC) machines.

The simulation results show that the best optimization method is produced by the PID - Ant Colony Optimization method which produces overshoot: 0.1199, undershoot: 0.0544, and 
settling time at 2.532 seconds which is the smallest value. Meanwhile, the design without 
control never reaches steady steady with the largest undershot. : 0.523. so PID - Ant Colony 
Optimization was chosen as the best method and is suitable for use in controlling PCB Drilling 
CNC Machines. By applying the PID - Ant Colony Optimization method to the CNC PCB 
Drilling Machine, it will be able to produce more precise drilling results. 
 
 
 
Key Words: CNC, PCB, PID, Ant Colony Optimization (ACO)}
		}