Evaluation of compaction equations and prediction using adaptive neuro-fuzzy inference system on compressibility behavior of AA 6061100-x-x wt.% TiO2 nanocomposites prepared by mechanical alloying

Powder Technology

Volumn 209, Issue 1-3, 2011, Pages 124-137

  Sivasankaran, S ^a   Sivaprasad, K ^a   Narayanasamy, R ^a   Iyer, Vijay Kumar ^b  

^a NATIONAL INSTITUTE OF TECHNOLOGY   (India)

^b Powder Metallurgy Shop   (India)

Author keywords

Adaptive neuro fuzzy inference system; Compaction equations; Compressibility; Nanocomposites

Indexed keywords

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM; ANFIS MODEL; COMPACTION BEHAVIOR; COMPACTION PRESSURE; EXPERIMENTAL DATA; FITTING CURVES; MATRIX; MULTIPLE REGRESSION ANALYSIS; NANO-TITANIA; NANOCOMPOSITE POWDER; NANOCRYSTALLINE MATRIX; NON-LINEAR; POWDER COMPACTIONS; REGRESSION COEFFICIENT; RELATIVE DENSITY; ROOM TEMPERATURE; TIO;

COMPACTION; COMPRESSIBILITY; CURVE FITTING; EXPERT SYSTEMS; FUZZY INFERENCE; GERMANIUM; MECHANICAL ALLOYING; METALLURGY; NANOCOMPOSITES; POWDERS; REGRESSION ANALYSIS; TITANIUM DIOXIDE;

FUZZY SYSTEMS;

ALLOY; NANOCOMPOSITE; NANOCRYSTAL; TITANIUM DIOXIDE;

ACCURACY; ARTICLE; COMPRESSION; EXPERT SYSTEM; FUZZY SYSTEM; INTERMETHOD COMPARISON; LINEAR SYSTEM; MATHEMATICAL ANALYSIS; MECHANICS; MULTIPLE REGRESSION; NONLINEAR SYSTEM; POWDER; PREDICTION; PRESSURE; ROOM TEMPERATURE; SYNTHESIS;

EID: 79953025288     PISSN: 00325910     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.powtec.2011.02.020     Document Type: Article

References (37)

1. Bouvard D. Densification behaviour of mixtures of hard and soft powders under pressure. Powder Technol. 2000, 111:231-239.
2. Çomoglu Tansel An overview of compaction equations. J. Fac. Pharm. Ankara 2007, 36(2):123-133.
3. Denny P.J. Compaction equations: a comparison of the Heckle and Kawalita equations. Powder Technol. 2002, 127:162-172.
4. Tavakoli A.H., Simchi A., Seyed Reihani S.M. Study of the compaction behavior of composite powders under monotonic and cyclic loading. Compos. Sci. Technol. 2005, 65:2094-2104.
5. Zhou Z., Peng H.X., Fan Z., Li D.X. MMCs with controlled non-uniform distribution of submicrometer Al2O3 particles in 6061 aluminum alloy matrix. Mater. Sci. Technol. 2000, 16:908-912.
6. Kang Y.C., Chan S.L. Tensile properties of nanometric Al2O3 particulate-reinforced aluminum matrix composites. Mater. Chem. Phys. 2004, 85:438-443.
7. Sivasankarana S., Sivaprasad K., Narayanasamy R., Iyer V.K. An investigation on flowability and compressibility of AA 6061100-x-x wt.% TiO2 micro and nanocomposite powder prepared by blending and mechanical alloying. Powder Technol. 2010, 201:70-82.
8. Slipenyuk A., Kuprin V., Milman Yu., Goncharuk V., Eckert J. Properties of P/M processed particle reinforced metal matrix composites specified by reinforcement concentration and matrix-to-reinforcement particle size ratio. Acta Mater. 2006, 54(1):157-166.
9. Ogel B., Gurbuz R. Microstructural characterization and tensile properties of hot pressed Al-SiC composites prepared from pure Al and Cu powders. Mater. Sci. Eng., A 2001, 301:213-220.
10. Sivasankarana S., Sivaprasad K., Narayanasamy R., Vijay Kumar Iyer Synthesis, structure and sinterability of 6061 AA100-x-x wt.% TiO2 composites prepared by high-energy ball milling. J. Alloys Compd. 2010, 491(1-2):712-721.
11. Sivasankarana S., Sivaprasad K., Narayanasamy R., Iyer V.K. Effect of strengthening mechanisms on cold workability and instantaneous strain hardening behavior during grain refinement of AA 6061-10wt.% TiO2 composite prepared by mechanical alloying. J. Alloys Compd. 2010, 507:236-244.
12. Hafizpour H.R., Simchi A., Parvizi S. Analysis of the compaction behavior of Al-SiC nanocomposites using linear and non-linear compaction equations. Adv. Powder Technol. 2010, 21(3):273-278.
13. Panelli R., Ambrosio Filho F. Densification of Al powder and Al-Cu matrix composite (reinforced with 15% Saffil short fibres) during axial cold compaction. Powder Technol. 2001, 114:255-261.
14. Balshin M.Yu. Theory of compacting. Vestn. Metalloprom 1938, 18:124-137.
15. Heckel R.W. Trans, density-pressure relationships in powder compaction. Metall. Soc. AIME 1961, 221:671-675.
16. Ge R. A new powder compaction equation. Int. J. Powder Metall 1991, 27(3):211-216.
17. Kawakita K., Lüdde K. Some considerations on powder compression equations. Powder Technol. 1971, 4:61-68.
18. Shapiro I. Compaction of powders XII. Evaluation of published compaction equations for modeling purposes. Adv. Powder Metall. Part. Mat. 1997, 1:22.
19. Cooper A.R., Eaton L.E. Compaction behaviour of several ceramic powders. J. Am. Ceram. Soc. 1962, 45:97-101.
20. Van Der Zwan J., Siskens C.A.M. The compaction and mechanical properties of agglomerated material. Powder Technol. 1982, 33(1):43-54.
21. Sivasankaran S., Narayanasamy R., Ramesh T., Prabhakar M. Analysis of workability behavior of Al-SiC P/M composites using backpropagation neural network model and statistical technique. Comput. Mater. Sci. 2009, 47:46-59.
22. Zeng Qingfeng, Zhang Litong, Yongdong Xu., Cheng Laifei, Yan Xiutian, Jiakui Zu., Dai Guanzhong Designing expert system for in situ toughened Si3N4 based on adaptive neural fuzzy inference system and genetic algorithms. Mater. Des. 2009, 30(2):256-259.
23. Kalaichelvi V., Sivakumar D., Karthikeyan R., Palanikumar K. Prediction of the flow stress of 601 Al-15% SiC-MMC composites using adaptive network based fuzzy inference system. Mater. Des. 2009, 30:1362-1370.
24. Topcu I.B., Saridemir M. Prediction of properties of waste AAC aggregate concrete using artificial neural network. Comput. Mater. Sci. 2008, 41:305-311.
25. Sivasankaran S., Narayanasamy R., Jeyapaul R., Loganathan C. Modelling of wrinkling in deep drawing of different grades of annealed commercially pure aluminium sheets when drawn through a conical die using artificial neural network. Mater. Des. 2009, 30(8):3193-3205.
26. Ho S.-Y., Lee K.-C., Chen S.-S., Ho S.-J. Accurate modeling and prediction of surface roughness by computer vision in turning operations using an adaptive neuro-fuzzy inference system. Int. J. Mach. Tools Manuf 2002, 42:1441-1446.
27. Tsai K.-M., Wang P.-J. Comparisons of neural network models on material removal rate in electrical discharge machining. J. Mater. Process. Technol. 2001, 117:111-124.
28. Dweiri F., Al-Jarrah M., Al-Wedyan H. Fuzzy surface roughness modeling of CNC down milling of Alumic-79. J. Mater. Process. Technol. 2003, 133:266-275.
29. Cheng C.-B., Lee E.S. Applying fuzzy adaptive network to fuzzy regression analysis. Comput. Math. Appl. 1999, 38:123-140.
30. Lo Ship-Peng An adaptive-network based fuzzy inference system for prediction of workpiece surface roughness in end milling. J. Mater. Process. Technol. 2003, 142:665-675.
31. Martin C.L., Bouvard D. Isostatic compaction of bimodal powder mixtures and composites. Acta Mater. 2003, 51:373-386.
32. Skrinjar Olle, Larsson Per-Lennart Cold compaction of composite powders with size ratio. Acta Mater. 2004, 52:1871-1884.
33. Cambronero L.E.G., Sánchez E., Ruiz-Roman J.M., Ruiz-Prieto J.M. Mechanical characterisation of AA7015 aluminium alloy reinforced with ceramics. J. Mater. Process. Technol. 2003, 143-144:378-383.
34. Grey R.O., Beddow J.K. On the Hausner Ratio and its relationship to some properties of metal powders. Powder Technol. 1968, 2:323-326.
35. Razavi Hesabi Z., Hafizpour H.R., Simchi A. An investigation on the compressibility of aluminum/nano-alumina composite powder prepared by blending and mechanical milling. Mater. Sci. Eng., A. 2007, 454-455:89-98.
36. Fogagnolo J.B., Ruiz-Navas E.M., Robert M.H., Torralba J.M. Effect of mechanical alloying on the morphology, microstructure and properties of aluminium matrix composite powders. Mater. Sci. Eng., A 2003, 342:131-143.
37. Uros Z., Franc C., Edi K. Adaptive network based inference system for estimation of flank wear in end-milling. J. Mater. Process. Technol. 2009, 209(3):1504-1511.