Simulation approach of cutting tool movement using artificial intelligence method

H. Abdullah, Rizauddin Ramli, Dzuraidah Abd. Wahab, J. A. Qudeiri

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

In recent years, the productivity of machine tools has been significantly improved by using computer-based CAD/CAM systems for Computer Numerical Control (CNC). Various types of CAM software in the market that provide tool path programming and can be applied for different types of the machining process such as drilling, milling, and turning. However, sometimes the default tool path generated in the CAD/CAM system is not the optimal tool path which produces longer distance and increase the machining time. In this paper, we present cutting tools movement by Genetic Algorithm (GA) and Ant Colony Optimization (ACO) method in generation of shortest tool path. For observation of the performance of both methods, comparisons with conventional method have been carried out. The shortest path of drilling tool path adapts Travelling Salesman Problem (TSP) in determining the distance during machining. The simulation result shows that ACO and GA based tool path optimization is useful to find a lower distance of tool path generation for holes drilling process.

Original languageEnglish
Pages (from-to)35-44
Number of pages10
JournalJournal of Engineering Science and Technology
Volume10
Issue numberSpec. Issue on 4th International Technical Conference (ITC) 2014
Publication statusPublished - 2015

Fingerprint

Cutting tools
Artificial intelligence
Computer aided manufacturing
Drilling
Machining
Ant colony optimization
Computer aided design
Computer systems
Genetic algorithms
Traveling salesman problem
Milling (machining)
Computer programming
Machine tools
Productivity

Keywords

  • Ant colony optimization
  • Genetic algorithm
  • Simulation
  • Tool path

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Abdullah, H., Ramli, R., Abd. Wahab, D., & Qudeiri, J. A. (2015). Simulation approach of cutting tool movement using artificial intelligence method. Journal of Engineering Science and Technology, 10(Spec. Issue on 4th International Technical Conference (ITC) 2014), 35-44.

Simulation approach of cutting tool movement using artificial intelligence method. / Abdullah, H.; Ramli, Rizauddin; Abd. Wahab, Dzuraidah; Qudeiri, J. A.

In: Journal of Engineering Science and Technology, Vol. 10, No. Spec. Issue on 4th International Technical Conference (ITC) 2014, 2015, p. 35-44.

Research output: Contribution to journalArticle

Abdullah, H, Ramli, R, Abd. Wahab, D & Qudeiri, JA 2015, 'Simulation approach of cutting tool movement using artificial intelligence method', Journal of Engineering Science and Technology, vol. 10, no. Spec. Issue on 4th International Technical Conference (ITC) 2014, pp. 35-44.
Abdullah H, Ramli R, Abd. Wahab D, Qudeiri JA. Simulation approach of cutting tool movement using artificial intelligence method. Journal of Engineering Science and Technology. 2015;10(Spec. Issue on 4th International Technical Conference (ITC) 2014):35-44.
Abdullah, H. ; Ramli, Rizauddin ; Abd. Wahab, Dzuraidah ; Qudeiri, J. A. / Simulation approach of cutting tool movement using artificial intelligence method. In: Journal of Engineering Science and Technology. 2015 ; Vol. 10, No. Spec. Issue on 4th International Technical Conference (ITC) 2014. pp. 35-44.
@article{026ceba08a98438fb5f3aa9de3126dc3,
title = "Simulation approach of cutting tool movement using artificial intelligence method",
abstract = "In recent years, the productivity of machine tools has been significantly improved by using computer-based CAD/CAM systems for Computer Numerical Control (CNC). Various types of CAM software in the market that provide tool path programming and can be applied for different types of the machining process such as drilling, milling, and turning. However, sometimes the default tool path generated in the CAD/CAM system is not the optimal tool path which produces longer distance and increase the machining time. In this paper, we present cutting tools movement by Genetic Algorithm (GA) and Ant Colony Optimization (ACO) method in generation of shortest tool path. For observation of the performance of both methods, comparisons with conventional method have been carried out. The shortest path of drilling tool path adapts Travelling Salesman Problem (TSP) in determining the distance during machining. The simulation result shows that ACO and GA based tool path optimization is useful to find a lower distance of tool path generation for holes drilling process.",
keywords = "Ant colony optimization, Genetic algorithm, Simulation, Tool path",
author = "H. Abdullah and Rizauddin Ramli and {Abd. Wahab}, Dzuraidah and Qudeiri, {J. A.}",
year = "2015",
language = "English",
volume = "10",
pages = "35--44",
journal = "Journal of Engineering Science and Technology",
issn = "1823-4690",
publisher = "Taylor's University College",
number = "Spec. Issue on 4th International Technical Conference (ITC) 2014",

}

TY - JOUR

T1 - Simulation approach of cutting tool movement using artificial intelligence method

AU - Abdullah, H.

AU - Ramli, Rizauddin

AU - Abd. Wahab, Dzuraidah

AU - Qudeiri, J. A.

PY - 2015

Y1 - 2015

N2 - In recent years, the productivity of machine tools has been significantly improved by using computer-based CAD/CAM systems for Computer Numerical Control (CNC). Various types of CAM software in the market that provide tool path programming and can be applied for different types of the machining process such as drilling, milling, and turning. However, sometimes the default tool path generated in the CAD/CAM system is not the optimal tool path which produces longer distance and increase the machining time. In this paper, we present cutting tools movement by Genetic Algorithm (GA) and Ant Colony Optimization (ACO) method in generation of shortest tool path. For observation of the performance of both methods, comparisons with conventional method have been carried out. The shortest path of drilling tool path adapts Travelling Salesman Problem (TSP) in determining the distance during machining. The simulation result shows that ACO and GA based tool path optimization is useful to find a lower distance of tool path generation for holes drilling process.

AB - In recent years, the productivity of machine tools has been significantly improved by using computer-based CAD/CAM systems for Computer Numerical Control (CNC). Various types of CAM software in the market that provide tool path programming and can be applied for different types of the machining process such as drilling, milling, and turning. However, sometimes the default tool path generated in the CAD/CAM system is not the optimal tool path which produces longer distance and increase the machining time. In this paper, we present cutting tools movement by Genetic Algorithm (GA) and Ant Colony Optimization (ACO) method in generation of shortest tool path. For observation of the performance of both methods, comparisons with conventional method have been carried out. The shortest path of drilling tool path adapts Travelling Salesman Problem (TSP) in determining the distance during machining. The simulation result shows that ACO and GA based tool path optimization is useful to find a lower distance of tool path generation for holes drilling process.

KW - Ant colony optimization

KW - Genetic algorithm

KW - Simulation

KW - Tool path

UR - http://www.scopus.com/inward/record.url?scp=84949634968&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84949634968&partnerID=8YFLogxK

M3 - Article

VL - 10

SP - 35

EP - 44

JO - Journal of Engineering Science and Technology

JF - Journal of Engineering Science and Technology

SN - 1823-4690

IS - Spec. Issue on 4th International Technical Conference (ITC) 2014

ER -