Rapid synthesis of nanopowders in high energy ball milling; Optimization of milling parameters
Source abstract
Abstract
Abstract An important factor in high energy ball milling process is to speed up the synthesis of metal/ceramic nanocomposites. In this work, for the first time, particle swarm optimization (PSO) as a new powerful algorithm was used for optimization of milling parameters in order to minimize the synthesis time of metal/ceramic nanocomposites in planetary mills. Accordingly, it is important to find a mathematical model to correlate the milling parameters with the synthesis time of metal/ceramic nanocomposites and then optimize the mentioned model. In this work, Burgio׳s mathematical model was chosen because it describes the energy supplied by a planetary mill using only analytical expressions. Based on Burgio׳s model eight design parameters in milling, namely, number of balls, ball diameter, vial radius, vial height, ball diameter distribution coefficient, plate spinning rate, vial spinning rate and distance between the center of the plate and the center of the vial, were optimized by the PSO algorithm. By optimizing the milling parameters and given the Burgio model, the synthesis time was minimized. At the end two test cases were solved to demonstrate the effectiveness and accuracy of the proposed design. Computational results showed that the proposed optimization algorithm is quite effective and powerful in optimizing the planetary mills to speed up the metal/ceramic nanocomposites synthesis.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Mineral Processing and Grinding · Advanced machining processes and optimization · Advanced Surface Polishing Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (39)
Show source-provided references
1. Rapid synthesis of MoSi2–Si3N4 nanocomposite via reaction milling of Si and Mo powder mixture10.1007/s12613-013-0841-1 ↗
2. Events and reaction mechanisms during synthesis of Al2O3–TiB2 nanocomposite via high energy ball milling10.1007/s11705-013-1325-6 ↗
3. Effect of cup and ball types on alumina–tungsten carbide nanocomposite powder synthesized by mechanical alloying10.1016/j.apt.2010.12.001 ↗
4. Influence of B source materials on the synthesis of TiB2–Al2O3 nanocomposite powders by mechanical alloying10.1007/s12613-013-0857-6 ↗
5. Mechanical alloying and reactive milling in a high energy planetary mill10.1016/j.jallcom.2008.12.021 ↗
6. Estimation of entrapped powder temperature during mechanical alloying10.1016/j.scriptamat.2004.02.011 ↗
7. Energy transfer in mechanical alloying (an overreview)10.2320/matertrans1989.36.123 ↗
8. Internal kinematics of tumbler and planetary ball mills: a mathematical model for the parameter setting10.2320/matertrans1989.39.1152 ↗
9. Mechanical work and conversion degree in mechanically induced processes10.1016/j.msea.2004.05.047 ↗
10. The application of a milling map in the mechanochemical synthesis of ceramic oxides10.1016/j.jeurceramsoc.2005.11.013 ↗
11. Ball milling: an experimental support to the energy transfer evaluated by the collision model10.1016/1359-6462(95)00465-3 ↗
12. Mechanical alloying of the Fe–Zr system: correlation between input energy and end products10.1007/BF02452130 ↗
13. Optimization of heat transfer in an air cooler equipped with classic twisted tape inserts using imperialist competitive algorithm10.1016/j.expthermflusci.2011.12.007 ↗
14. A flood forecasting neural network model with genetic algorithm10.1504/IJEP.2006.011211 ↗
15. Neural network and genetic programming for modeling coastal algal blooms10.1504/IJEP.2006.011208 ↗
16. Design and economic optimization of shell-and-tube heat exchangers using biogeography-based (BBO) algorithm10.1016/j.applthermaleng.2012.12.002 ↗
17. Biogeography based optimization technique for best compromise solution of economic emission dispatch10.1016/j.swevo.2012.06.001 ↗
18. Tabu search techniques: a tutorial and an application to neural networks10.1007/BF01720782 ↗
19. Finding a feasible course schedule using tabu search10.1016/0166-218X(92)90248-9 ↗
20. Battiti · 1992
21. Data mining with ant colony optimization algorithm10.1109/TEVC.2002.802452 ↗
22. C. Blum, M. Sampels, Ant colony optimization for FOP shop scheduling: a case study on different pheromone representations, in: Proceedings of the 2002 Congress on Evolutionary Computation, Honolulu, USA.
23. W. Ying, X. Jianying, Ant colony optimization for multicast routing, in: Proceedings of the 2000 IEEE Asia-Pacific Conference on Circuits and Systems, Tianjin, China.
24. D.T. Pham, A. Ghanbarzadeh, E. Koç, S. Otri, Application of the Bees algorithm to the training of radial basis function networks for control chart pattern recognition, in: Proceedings of the 5th CIRP International Seminar on Intelligent Computation in Manufacturing Engineering (CIRP ICME ׳06), Ischia, Italy, 2006.
25. T. Pham, E. Koç, A. Ghanbarzadeh, S. Otri, Optimisation of the weights of multilayered perceptrons using the Bees algorithm, in: Proceedings of the 5th International Symposium on Intelligent Manufacturing Systems, Turkey, 2006.
26. D.T. Pham, A.J. Soroka, A. Ghanbarzadeh, E. Koç, S. Otri, M. Packianather, Optimising neural networks for identification of wood defects using the Bees, in: Proceedings of the 2006 IEEE International Conference on Industrial Informatics, Singapore, 2006.10.1109/INDIN.2006.275855 ↗
27. Optimizing neural networks using very fast simulated annealing
28. A simulated annealing approach to the solution of minlp problems10.1016/S0098-1354(97)00015-X ↗
29. Simulated annealing based artificial bee colony algorithm for global numerical optimization10.1016/j.amc.2012.09.052 ↗
30. Routing and wavelength assignment in optical networks using artificial bee colony algorithm10.1016/j.ijleo.2012.03.022 ↗
31. Gandomi · Mixed variable structural optimization using Firefly Algorithm Computers & structures · 2011 · 89 · 2325
32. Y.Tian, W.Gao, S.Yan, An improved inertia weight firefly optimization algorithm and application, in: Proceedings of the Control Engineering and Communication Technology Conference, vol. 1, 2012, pp. 64–68.10.1109/ICCECT.2012.38 ↗
33. Milling maps and amorphization during mechanical alloying10.1016/0956-7151(94)00402-1 ↗
34. Eberhart · 2001
35. Y Fukuyama, H Yoshida, A particle swarm optimization for reactive power and voltage control in electric power systems, in: Proceedings of the IEEE Congress on Evolutionary Computation, vol. 1, 2001, pp. 87–93.10.1109/CEC.2001.934375 ↗
36. J Kennedy, Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance in: Proceedings of the IEEE Congress on Evolutionary Computation, vol. 3, 1999, pp. 1931–1938.10.1109/CEC.1999.785509 ↗
37. van den Bergh · 2001
38. Yuhui Shi, Russell Eberhart, A modified particle swarm optimizer in: Proceedings of the IEEE International Conference on Evolutionary Computation, vol. 1, 1998,pp. 69–73.
39. A profile refinement method for nuclear and magnetic structures10.1107/S0021889869006558 ↗