Determination of the Optimal Path of Three Axes Robot Using Genetic Algorithm

Article Preview

Abstract:

Drilling is a chip machining process widely used in manufacturing .The term drilling includes all methods for making cylindrical holes in a work piece with chip cutting tools. There are many applications where drilling is used, such as drilling holes in PCBs. Robotic systems are used today to perform the drilling process. A problem that affects the use of these systems is the drilling sequence, as there are usually a number of points to visit. The determination of the drilling sequence is similar to the problem of synchronization of movement and travel time. The main objective is to optimize the time and trajectory of the three axes robot equipped with an automatic drill that seeks the best performance. In this paper, we have built a genetic optimization and problem solving algorithms to shorten the machining time to drill a given group of holes and reduce machining costs in order to improve the efficiency of the machining process as well robotic machining with three axes without degradation of the precision of the movement. The results of the experiments show that the proposed approach is feasible and practical. It is particularly useful in planning and scheduling systems for real-time manufacturing processes.

You might also be interested in these eBooks

Info:

Pages:

135-149

Citation:

Online since:

August 2019

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2019 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] D. Mika and J. Michałowska, Normatywne pomiary czynników szkodliwych na stanowisku pracy operatora obrabiarek sterowanych numerycznie,, Przegląd Elektrotechniczny, vol. 12, (2016).

DOI: 10.15199/48.2016.12.25

Google Scholar

[2] V. Thevenot, L. Arnaud, G. Dessein, and G. Cazenave-Larroche, Integration of dynamic behaviour variations in the stability lobes method: 3D lobes construction and application to thin-walled structure milling,, The International Journal of Advanced Manufacturing Technology, vol. 27, pp.638-644, (2006).

DOI: 10.1007/s00170-004-2241-1

Google Scholar

[3] J. Feng, Y. Li, Y. Wang, and M. Chen, Design of a real-time adaptive NURBS interpolator with axis acceleration limit,, The International Journal of Advanced Manufacturing Technology, vol. 48, pp.227-241, (2010).

DOI: 10.1007/s00170-009-2261-y

Google Scholar

[4] L. B. Zhang, Y. P. You, J. He, and X. F. Yang, The transition algorithm based on parametric spline curve for high-speed machining of continuous short line segments,, The International Journal of Advanced Manufacturing Technology, vol. 52, pp.245-254, (2011).

DOI: 10.1007/s00170-010-2718-z

Google Scholar

[5] C. Tournier and E. Duc, A surface based approach for constant scallop heighttool-path generation,, The International Journal of Advanced Manufacturing Technology, vol. 19, pp.318-324, (2002).

DOI: 10.1007/s001700200019

Google Scholar

[6] C. Tournier and E. Duc, Iso-scallop tool path generation in 5-axis milling,, The International Journal of Advanced Manufacturing Technology, vol. 25, pp.867-875, (2005).

DOI: 10.1007/s00170-003-2054-7

Google Scholar

[7] S.-G. Lee, H.-C. Kim, and M.-Y. Yang, Mesh-based tool path generation for constant scallop-height machining,, The International Journal of Advanced Manufacturing Technology, vol. 37, pp.15-22, (2008).

DOI: 10.1007/s00170-007-0943-x

Google Scholar

[8] A. Can and A. Ünüvar, A novel iso-scallop tool-path generation for efficient five-axis machining of free-form surfaces,, The International Journal of Advanced Manufacturing Technology, vol. 51, pp.1083-1098, (2010).

DOI: 10.1007/s00170-010-2698-z

Google Scholar

[9] A. Gjelaj, J. Balič, and M. Fičko, Intelligent optimal tool selections for CNC programming of machine tools,, Transactions of FAMENA, vol. 37, pp.31-40, (2013).

Google Scholar

[10] K. D. N. R. Ramli, Application of artificial intelligence methods of tool path optimization in CNC machines: A review,, Research Journal of Applied Sciences, Engineering and Technology, vol. 8, pp.746-754, (2014).

DOI: 10.19026/rjaset.8.1030

Google Scholar

[11] L. W. Kariuki, G. N. Nyakoe, and B. W. Ikua, Generation and optimization of pocket milling tool paths-A review,, in Proceedings of Sustainable Research and Innovation Conference, 2014, pp.129-133.

Google Scholar

[12] A. Slimane, S. Kebdani, B. Bouchouicha, M. Benguediab, S. Slimane, K. Bahram, et al., An interactive method for predicting industrial equipment defects,, The International Journal of Advanced Manufacturing Technology, vol. 95, pp.4341-4351, (2018).

DOI: 10.1007/s00170-017-1416-5

Google Scholar

[13] J. A. Qudeiri, H. Yamamoto, and R. Ramli, Optimization of operation sequence in CNC machine tools using genetic algorithm,, Journal of Advanced Mechanical Design, Systems, and Manufacturing, vol. 1, pp.272-282, (2007).

DOI: 10.1299/jamdsm.1.272

Google Scholar

[14] A. Kumar and P. Pachauri, Optimization drilling sequence by genetic algorithm,, International journal of scientific and research publications, vol. 2, pp.1-7, (2012).

Google Scholar

[15] H. Q. Du and J. B. Qi, Application of a hybrid algorithm based on genetic algorithm and hill-climbing algorithm to tool path optimization in CNC machining,, in Advanced Materials Research, 2010, pp.681-685.

DOI: 10.4028/www.scientific.net/amr.102-104.681

Google Scholar

[16] V. Savas and C. Ozay, The optimization of the surface roughness in the process of tangential turn-milling using genetic algorithm,, The International Journal of Advanced Manufacturing Technology, vol. 37, pp.335-340, (2008).

DOI: 10.1007/s00170-007-0984-1

Google Scholar

[17] S.-A. Dahmane, A. Azzedine, A. Megueni, and A. Slimane, Quantitative and qualitative study of methods for solving the kinematic problem of a planar parallel manipulator based on precision error optimization,, International Journal on Interactive Design and Manufacturing (IJIDeM), pp.1-29, (2018).

DOI: 10.1007/s12008-018-0519-z

Google Scholar

[18] R. Saravanan and V. Janakiraman, Study on reduction of machining time in CNC turning centre by genetic algorithm,, in International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007), 2007, pp.481-486.

DOI: 10.1109/iccima.2007.92

Google Scholar

[19] D. Pezer, Using the Ant Colony Optimization method to find an optimal solution in drilling process,, in 8th International Scientific and Expert Conference of the International TEAM Society, (2016).

Google Scholar

[20] J. E. A. Qudeiri, Optimization and Program Generation of a Tool Path for Multi-Cutting Tool Operations in CNC Machines,, International Journal of Emerging Technology and Advanced Engineering, vol. 4, pp.15-23.

DOI: 10.24178/ijare.2015.1.2.07

Google Scholar

[21] N. K. A. Al-Sahib and H. F. Abdulrazzaq, Tool path optimization of drilling sequence in CNC machine using genetic algorithm,, Innov Syst Des Eng, vol. 5, p. 15e26, (2014).

Google Scholar

[22] A. Jameel, M. Minhat, and M. Nizam, Using genetic algorithm to optimize machining parameters in turning operation: a review,, International journal of scientific and research publications, vol. 3, pp.1-6, (2013).

Google Scholar

[23] J. Balic, M. Ficko, A. H. Salihu, and A. Gjelaj, Optimization of cutting tool path generation using genetic algorithm,, Annals of DAAAM & Proceedings, pp.569-571, (2011).

DOI: 10.2507/22nd.daaam.proceedings.281

Google Scholar

[24] M. Tolouei-Rad, Efficient CNC Milling by Adjusting Material Removal Rate,, World Academy of Science, Engineering and Technology, International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering, vol. 5, pp.1988-1992, (2011).

Google Scholar

[25] J. E. A. Qudeiri, A.-M. Raid, M. A. Jamali, and H. Yamamoto, Optimization hole-cutting operations sequence in CNC machine tools using GA,, in 2006 International conference on service systems and service management, 2006, pp.501-506.

DOI: 10.1109/icsssm.2006.320513

Google Scholar

[26] H.-T. Hsieh and C.-H. Chu, Improving optimization of tool path planning in 5-axis flank milling using advanced PSO algorithms,, Robotics and Computer-Integrated Manufacturing, vol. 29, pp.3-11, (2013).

DOI: 10.1016/j.rcim.2012.04.007

Google Scholar

[27] G. Al-Kindi and H. Zughaer, An approach to improved CNC machining using vision-based system,, Materials and Manufacturing Processes, vol. 27, pp.765-774, (2012).

DOI: 10.1080/10426914.2011.648249

Google Scholar