A Novel Integrating Between Tool Path Optimization Using An ACO Algorithm And Interpreter For Open Architecture CNC System

Yusof, Yusri and Latif, Kamran and Hatem, Noor and A. Kadir, Aini Zuhra and Mohammed, M.A (2021) A Novel Integrating Between Tool Path Optimization Using An ACO Algorithm And Interpreter For Open Architecture CNC System. Expert Systems with Applications, 178. pp. 1-19. ISSN 0957-4174

Text 1-S2.0-S0957417421004292-MAIN.PDF Restricted to Registered users only Download (6MB)

Abstract

This article proposes an effective methodology to reduce manufacturing time through the creation of quasioptimal G command sequences, based on initial machining codes derived from CAD/CAM Service-Oriented Architecture (SOA) software. The optimization steps involved minimizing travel path time for CNC manufacturing machine milling and drilling. Also, identifying the optimal order of operation that enabled the shortest travel path for the cutting tool. These steps resulted in consistent enhancements of approximately 10.41% and 16.58% for milling and drilling machining, respectively. Furthermore, high-performing Ant Colony Optimization (ACO) algorithms were used to optimize the travel path time. In the context of automatic NC program production, the optimization of the cutting tool’s travel path could be achieved by integrating the ACO algorithm and the Open Architecture Control (OAC) into commercial CAD/CAM. In this paper, a new optimized generation of CNC systems, based on combining the Open Architecture Control (OAC) technology supported by the G-code data model and the optimization algorithm are presented

Item Type:	Article
Uncontrolled Keywords:	Milling, Drilling, G Code, ACO Algorithm, Tool Path Optimization, Traveling Salesman Problem
Divisions:	Faculty of Mechanical and Manufacturing Engineering Technology > Department of Manufacturing Engineering Technology
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	14 Mar 2022 12:18
Last Modified:	14 Mar 2022 12:18
URI:	http://eprints.utem.edu.my/id/eprint/25681
Statistic Details:	View Download Statistic

Actions (login required)

View Item