Efectividad en Gestión Hídrica General

Riza [40] used a hybrid optimization approach based on PSO and receptor editing property of artificial immune system (AIS) in both design and manufacturing fields for multi-pass turning operation. A single-objective test problem, tension spring problem, pressure vessel design optimization problem taken from the literature and two case studies for multi-pass turning operations are solved by the proposed new hybrid approach to evaluate its performance.

Discussions and conclusions

In many real applications the makers face the problem of simultaneous optimization of several conflicting and incomparable objectives. From the reviewed studies, generally, it is clearly visible that particle swarm optimization techniques are more popular in comparison to artificial immune system techniques for turning processes optimization. Where many efforts are concentrated on optimization of surface roughness, production costs, material removal rate, machining time and tool wear. The reason is

156

because of PSO-based algorithms is simple and effective. However, there is lack of studies for other turning processes parameters such as cutting temperature, torque, geometrical accuracy and heat affected zone tool geometry. To the best of our knowledge, there are no studies dealing with AIS approaches and the other recent bio-inspired algorithms in optimizing cutting temperature parameter in turning operations and other machining operations such as milling, drilling, grinding.

157 References

[1] Z. CAR . (2009). GA based CNC turning center exploitation process parameters optimization , Metabk.

[2] Nafis Ahmad.(2001). Optimization of process planning parameters for rotational components by genetic algorithms, International conference on mechanical engineering, Bangladesh.

[3] T. Tamizharasan, J. Kingston Barnabas , J. Fakrudeen. (2002). Optimization of parameters in hard dry turning using DoE, DE and PSO.

[4] Yi-Tung Kao . (2008). A Hybrid genetic algorithm and particle swarm optimization for multimodal functions,Applied Soft Computing.

[5] H. Ganesan . (2011). Optimization of machining parameters in turning process using genetic algorithm and particle swarm optimization with experimental verification. International journal of Engineering Science and Technology.

[6] Shutong XIE .(2011). Intelligent Selection of Machining Parameters in Multi-pass Turnings Using a GA-based Approach.

[7] T.Srikanth . (2008). A Real Coded Genetic Algorithm for optimization of Cutting Parameters in Turning . IJCSNS International Journal of Computer Science and Network Security.

[8] A. Yildiz . (2009).. A novel particle swarm optimization approach for product design and manufacturing, Int J Adv Manuf Technol 40. pp. 617–628.

[9] N. Yusup, A. Mohd , S. Zaiton. Evolutionary techniques in optimizing machining parameters: Review and recent applications (2007–2011). (2012).

Expert Systems with Applications. pp. 9909–9927.

[10] D. Rini , S. Mariyam , S. Sophiyati . (2011). Particle Swarm Optimization:

Technique, System and Challenges .International Journal of Computer Applications. pp. 0975 – 8887.

[11] Y. Karpat , T. Özel. (2007). Multi-objective optimization for turning processes using neural network modeling and dynamic-neighborhood particle swarm optimization. Int J Adv Manuf Technol. pp. 234–247.

[12] Y. Karpat , T. Ozel. (2005). Hard turning optimization using neural network modeling and swarm intelligence. Department of Industrial and Systems

Engineering. pp. 179-186.

158

[13] Y. Karpat , T. Ozel. (2006). Swarm-Intelligent neural network system (SINNS) based multi-objective optimization of hard turning, Transactions of NAMRI/SME. pp. 1-8.

[14] Adam A. Cardi .(2008).Workpiece dynamic analysis and prediction during chatter of turning process. Mechanical Systems and Signal Processing.

[15] Srinivas, Giri, , Yang. (2009). Optimization of multi-pass turning using particle swarm intelligence, International Journal of Advanced Manufacturing Technology.

[16] Zheng, Ponnambalam. (2010). Optimization of multi pass turning operations using particle swarm optimization.ISMA'10 - 7th International Symposium on Mechatronics and its Applications. pp. 1-6.

[17] Costa, Celano , Fichera . (2011). Optimization of multi-pass turning

economies through a hybrid particle swarm optimization technique. International Journal of Advanced Manufacturing Technology. pp. 421-433.

[18] Tang , Lie. (2008). parallel turning process parameter optimization based on a novel heuristic approach, Journal of Manufacturing Science and Engineering.

[19] S. Bharathi , N. Baskar . (2011). Particle swarm optimization technique for determining optimal parameters of different work piece materials in turning operation, the international journal of advanced manufacturing technology. pp. 5-8.

[20] Kusum , Jagdish. (2007) Performance Analysis of Turning Process via Particle Swarm Optimization, Nature Inspired Cooperative Strategies for Optimization Studies in Computational Intelligence. pp. 453-460.

[21] Abukhshim, Mativenga ,Sheikh. (2005) Heat generation and temperature prediction in metal cutting: A review and implications for high speed machining.

International Journal of Machine Tools & Manufacture xx, pp. 1–19.

[22] U. Seker, İ. Korkut, Y. Turgut , M. Boy. The measurement of temperature during machining. International conference power transmissions 03.

[23] F.Aneiro , R. Coelho, L. Brandao. (2008). Turning Hardened Steel Using Coated Carbide at High Cutting Speeds. V.xxx.

[24] Luke , Huang, Joseph , Tao. (1999). Effect of Tool/Chip Contact Length on Orthogonal Turning Performance, journal of industrial technology.

[25] D. Sullivan.(2001). Temperature measurement in single point turning. Cork Institute Technology. pp. 301-308.

159

[26] O. Çolak. (2012). Tool-chip temperature simulation on high pressure jet assisted machining of Ti6Al-V4, Scientific Research and Essays. pp. 873-880.

[27] A. Amritkar,C. prakash , A. Kulkarni. (2012). Development of temperature measurement setup for machining. Conference on Advances in Mechanical Engineering (NCAME'12).

[28] L. Abhang, M. Hameedullah. (2010). The Measurement of chip-tool interface Temperature in the Turning of steel, International Journal of Computer Communication and Information System ( IJCCIS). pp. 976 –1349.

[29] M. Lazard , P. Corvisier. (2005). Inverse method for transient temperature estimation during machining, Proceedings of the 5th International Conference on Inverse Problems in Engineering. Cambridge, UK.

[30] Mahdavinejad, Sharifi . (2009). Optimization of surface roughness parameters in dry turning, Journal of achievements in materials and manufacturing engineering.

[31] Ueda, Sato, Hosokawa , Ozawa. (2008) . Development of infrared radiation pyrometer with optical fibers -Two-color pyrometer with non-contact fiber coupler. CIRP Annals - Manufacturing Technology. pp. 69–72.

[32] S. Carvalho , S. Lima E. Silva , A. Machadoa , G. Guimar. (2006) . Temperature determination at the chip–tool interface using an inverse thermal model considering the tool and tool holder, Journal of Materials Processing Technology. pp. 97–104.

[33] M. Abdulla, S. Mithun, N. Ranjan . (2011). Modeling of Chip Tool Interface Temperature in Machining Steel- An Artificial Intelligence (AI) Approach.

International Conference on Industrial Engineering and Operations Management.

January. pp. 22 – 24.

[34] Z. Jurković. (2011). Optimization of cutting parameters based on tool chip interface temperature in turning process using Taguchi’s method. 15th

International Research/Expert Conference. pp. 12-18.

[35] A. Salihu, N.Qehaja, H.Zeqir, A. Kyqyku, A. Bunjaku , F. Zeqiri. (2011).

The temperature research in increased speed processing by turning. 15th International Research/Expert Conference. pp. 12-18.

[36] U. Zuperl , F. Cus . (2000). Optimization of Cutting Conditions During Machining by Using Neural Networks. Production Engineering Institute Faculty of Mechanical Engineering. Maribor.

160

[37] S. Dolinšek. (2006). Mechanism and types of tool wear; particularities in advanced cutting materials. Journal of Achievements in Materials and

Manufacturing Engineering. pp. 11-18.

[38] L. Yi, Z. Ming, W. Qing. (2005). Parameter estimation of cutting tool

temperature nonlinear model using PSO algorithm. Journal of Zhejiang University SCIENCE.

[39] A. Yildiz . (2012) . A comparative study of population-based optimization algorithms for turning operations , Journal of Information Sciences. pp. 81–88.

[40] A. Riza. (2009). An effective hybrid immune– hill climbing optimization approach for solving design and manufacturing optimization problems in industry. J. Materials Processing Technology USA, pp. 2773-2780.