Open Access
Issue
MATEC Web Conf.
Volume 221, 2018
2018 3rd International Conference on Design and Manufacturing Engineering (ICDME 2018)
Article Number 01004
Number of page(s) 5
Section Functional Material Design and Analysis
DOI https://doi.org/10.1051/matecconf/201822101004
Published online 29 October 2018
  1. Sasikumar KSK, Arulshri KP, Ponappa K, Uthayakumar M (2016) A study on kerf characteristics of hybrid aluminium 7075 metal matrix composites machined using abrasive water jet machining technology. Proc Inst Mech Eng Part B J Eng Manuf 232:690–704. doi: 10.1177/0954405416654085 [CrossRef] [Google Scholar]
  2. Jagadish, Bhowmik S, Ray A (2015) Prediction of surface roughness quality of green abrasive water jet machining: a soft computing approach. J Intell Manuf 1–15. doi: 10.1007/s10845-015-1169-7 [Google Scholar]
  3. Chaturvedi V, Singh D (2015) Multi Response Optimization of Process Parameters of Abrasive Water Jet Machining for Stainless Steel AISI 304 Using VIKOR Approach Coupled with Signal to Noise Ratio Methodology. J Adv Manuf Syst 14:107–121. doi: 10.1142/S0219686715500080 [CrossRef] [Google Scholar]
  4. Gupta MK (2015) Optimization of machining parameters for turning AISI 4340 steel using Taguchi based grey relational analysis. Indian J Eng Mater Sci 22:679–685. [Google Scholar]
  5. Gupta MK, Singh G, Sood PK (2015) Modelling and Optimization of Tool Wear in Machining of EN24 Steel Using Taguchi Approach. J Inst Eng Ser C 96:269–277. doi: 10.1007/s40032-015-0175-z [CrossRef] [Google Scholar]
  6. Biswas SA, Datta S, Bhaumik S, Majumdar G (2009) MULTI-RESPONSE OPTIMIZATION : A CASE STUDY IN. 2009:26–28. [Google Scholar]
  7. Thakur DG, Ramamoorthy B, Vijayaraghavan L (2009) Optimization of Minimum Quantity Lubrication Parameters in High Speed Turning of Superalloy Inconel 718 for Sustainable Development. World Acad Sci Eng Technol 54:224–226. [Google Scholar]
  8. Neşeli S, Yaldız S, Türkeş E (2011) Optimization of tool geometry parameters for turning operations based on the response surface methodology. Measurement 44:580–587. doi: http://dx.doi.org/10.1016/j.measurement.2010.11.018 [CrossRef] [Google Scholar]
  9. Guodong L, Yong L, Quancun K, Hao T (2016) Selection and Optimization of Electrolyte for Micro Electrochemical Machining on Stainless Steel 304. Procedia CIRP 42:412–417. doi: 10.1016/j.procir.2016.02.223 [CrossRef] [Google Scholar]
  10. Yildiz AR, Öztürk F (2010) Hybrid Taguchi-Harmony Search Approach for Shape Optimization. In: Geem ZW (ed) Recent Adv. Harmon. Search Algorithm. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 89–98 [CrossRef] [Google Scholar]
  11. Gupta MK, Sood PK, Sharma VS (2016) Optimization of machining parameters and cutting fluids during nano-fluid based minimum quantity lubrication turning of titanium alloy by using evolutionary techniques. J Clean Prod 135:1276–1288. doi: 10.1016/j.jclepro.2016.06.184 [CrossRef] [Google Scholar]
  12. Lan T-S (2009) Taguchi optimization of multi-objective CNC machining using TOPSIS. Inf Technol J 8:917–922. [CrossRef] [Google Scholar]
  13. Gauri SK, Chakravorty R, Chakraborty S (2011) Optimization of correlated multiple responses of ultrasonic machining (USM) process. Int J Adv Manuf Technol 53:1115–1127. doi: 10.1007/s00170-010-2905-y [CrossRef] [Google Scholar]
  14. Vazquez E, Ciurana J, Rodríguez CA, et al. (2011) Swarm intelligent selection and optimization of machining system parameters for microchannel fabrication in medical devices. Mater Manuf Process 26:403–414. [CrossRef] [Google Scholar]
  15. Ciurana J, Arias G, Ozel T (2009) Neural network modeling and particle swarm optimization (PSO) of process parameters in pulsed laser micromachining of hardened AISI H13 steel. Mater Manuf Process 24:358–368. [CrossRef] [Google Scholar]
  16. Ulutan D, Özel T (2013) Multiobjective optimization of experimental and simulated residual stresses in turning of nickel-alloy IN100. Mater Manuf Process 28:835–841. [CrossRef] [Google Scholar]
  17. Pawar PJ, Rao R V, Davim JP (2010) Multiobjective optimization of grinding process parameters using particle swarm optimization algorithm. Mater Manuf Process 25:424–431. [CrossRef] [Google Scholar]
  18. Venkata Rao R, Kalyankar VD (2013) Parameter optimization of modern machining processes using teaching-learning-based optimization algorithm. Eng Appl Artif Intell 26:524–531. doi: 10.1016/j.engappai.2012.06.007 [CrossRef] [Google Scholar]
  19. Venkata Rao R, Kalyankar VD (2011) Parameter Optimization of Machining Processes Using a New Optimization Algorithm. Mater Manuf Process 27:978–985. doi: 10.1080/10426914.2011.602792. [CrossRef] [Google Scholar]

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