Abstract
This paper is concerned with the optimization of the tool path in a production line consisting of two machine tools. Existing computer – numerical control (CNC) time estimation methods are based either for a single machine or a single operation. As current methods don’t illustrate the necessity of the multiple operations with more than one machine, this paper presents a new method for CNC machining time estimation which predicts the optimal tool path sequence with minimal time for a 2M production line. The optimized sequence is determined by employing the most reliable hybrid method i.e., Genetic Algorithm (GA). Attention was focusing on the hole making operations where a hole may need multiple cutting tools to get the process finished. Each of the machines can do certain set of operations. So the non-productive time between two machines should be minimized and it is obtained by this intelligent sequence optimizer. This proposed technique is developed on a modified travel salesman problem algorithm with preceding constraints. The work also introduces a computational program based on this methodology. The numerical simulation conducted in this research shows that the proposed approach is feasible and practical. It is beneficial especially in real-time manufacturing process outlining and scheduling multiple systems.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Niazi, A., Dai, J.S., Balabani, S., Seneviratne, L.: Product cost estimation: technique classification and methodology review. J. Manuf. Sci. Eng. 128, 563–575 (2006)
Merchant, M.E.: World trends and prospects in manufacturing technology. Int. J. Veh. Des. 6, 121–138 (1985)
Qudeiri, J.E.A., Raid, A.-M., Jamali, M.A., Yamamoto, H.: Optimization hole-cutting operations sequence in CNC machine tools using GA. In: 2006 International Conference on Service Systems and Service Management, pp. 501–506. IEEE (2006)
Balic, J., Korosec, M.: Intelligent tool path generation for milling of free surfaces using neural networks. Int. J. Mach. Tools Manuf. 42, 1171–1179 (2002)
Ülker, E., Turanalp, M.E., Halkaci, H.S.: An artificial immune system approach to CNC tool path generation. J. Intell. Manuf. 20, 67–77 (2009)
Iberahim, F., Ramli, R., Narooei, K.D., Qudeiri, J.A.: Tool path optimization for drilling process by CNC milling machine using ant colony optimization (ACO). Aust. J. Basic Appl. Sci. 8, 385–389 (2014)
Hsieh, H.-T., Chu, C.-H.: Particle swarm optimisation (PSO)-based tool path planning for 5-axis flank milling accelerated by graphics processing unit (GPU). Int. J. Comput. Integr. Manuf. 24, 676–687 (2011)
Narooei, K.D., Ramli, R.: Application of artificial intelligence methods of tool path optimization in CNC machines: a review. Res. J. Appl. Sci. Eng. Technol. 8, 746–754 (2014). https://doi.org/10.19026/rjaset.8.1030
Qudeiri, J.A., Yamamoto, H., Ramli, R.: Optimization of operation sequence in CNC machine tools using genetic algorithm. J. Adv. Mech. Des. Syst. Manuf. 1, 272–282 (2007)
Pezer, D.: Efficiency of tool path optimization using genetic algorithm in relation to the optimization achieved with the CAM software. Proc. Eng. 149, 374–379 (2016)
Al-Sahib, N.K.A., Abdulrazzaq, H.F.: Tool path optimization of drilling sequence in CNC machine using genetic algorithm. Innov. Syst. Des. Eng. 5, 15e26 (2014)
Ghaiebi, H., Solimanpur, M.: An ant algorithm for optimization of hole-making operations. Comput. Ind. Eng. 52, 308–319 (2007)
McCall, J.: Genetic algorithms for modelling and optimisation. J. Comput. Appl. Math. 184, 205–222 (2005)
Jameel, A., Minhat, M., Nizam, M.: Using genetic algorithm to optimize machining parameters in turning operation: a review. Int. J. Sci. Res. Publ. 3, 1–6 (2013)
Car, Z., Mikac, T., Veža, I.: Utilization of GA for optimization of tool path on a 2D surface. In: 6th International Workshop on Emergent Synthesis (2006)
Qudeiri, J.E.A.: Optimization and program generation of a tool path for multi-cutting tool operations in CNC machines. Int. J. Emerg. Technol. Adv. Eng. 4, 15–23 (2014)
Grefenstette, J., Gopal, R., Rosmaita, B., Van Gucht, D.: Genetic algorithms for the traveling salesman problem. In: Proceedings of the First International Conference on Genetic Algorithms and Their Applications, pp. 160–168. Lawrence Erlbaum (1985)
Acknowledgement
The authors gratefully acknowledge the financial support provided by the Research Affairs Office at UAE University, Al-Ain United Arab Emirates, grant number 31N309.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Ahammed, T., Qudeiri, J.A., Mourad, AH., Ziout, A., Safieh, F. (2020). Intelligent Sequence Optimization Method for Hole Making Operations in 2M Production Line. In: Singh, P., Panigrahi, B., Suryadevara, N., Sharma, S., Singh, A. (eds) Proceedings of ICETIT 2019. Lecture Notes in Electrical Engineering, vol 605. Springer, Cham. https://doi.org/10.1007/978-3-030-30577-2_29
Download citation
DOI: https://doi.org/10.1007/978-3-030-30577-2_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30576-5
Online ISBN: 978-3-030-30577-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)