메뉴 건너뛰기




Volumn 5, Issue 11, 2010, Pages 1274-1283

The use of artificial neural network for prediction of grain size of 17-4 pH stainless steel powders

Author keywords

Artificial neural network; Garin size; Mechanical milling

Indexed keywords


EID: 77954421340     PISSN: None     EISSN: 19922248     Source Type: Journal    
DOI: None     Document Type: Article
Times cited : (14)

References (29)
  • 1
    • 0002645810 scopus 로고
    • Mechanical alloying
    • Benjamin JS (1976). Mechanical alloying. Sci. Am. 234(5): 40-48.
    • (1976) Sci. Am , vol.234 , Issue.5 , pp. 40-48
    • Benjamin, J.S.1
  • 2
    • 0004525598 scopus 로고
    • Mechanical Alloying
    • A perspective, Proceedings conference of new materials by MA Technigues E D. By E. Arzt and L. Shultz, alw-Hirasu
    • Benjamin JS (1988). In: Mechanical alloying. A perspective, Proceedings conference of new materials by MA Technigues E D. By E. Arzt and L. Shultz, alw-Hirasu. pp. 3-7.
    • (1988) , pp. 3-7
    • Benjamin, J.S.1
  • 3
    • 0003613746 scopus 로고
    • New Materials By Mechanical Alloying Techniques
    • Arzt E, Schultz L, Editors. Germany: DGM Informationgesellschaft
    • Benjamin JS (1989). In: Arzt E, Schultz L, editors. New materials by mechanical alloying techniques. Oberursel, Germany: DGM Informationgesellschaft. pp. 3-18.
    • (1989) Oberursel , pp. 3-18
    • Benjamin, J.S.1
  • 4
    • 0025387278 scopus 로고
    • Mechanical Alloying-A Perspective
    • Metal Powder Report
    • Benjamin JS (1990). Mechanical Alloying-A Perspective. Metal Powder Report., 45(2): 122-127.
    • (1990) , vol.45 , Issue.2 , pp. 122-127
    • Benjamin, J.S.1
  • 5
    • 33751202692 scopus 로고    scopus 로고
    • An Investigation Into The Effect of Experimental Parameters on Powder GS of The Mechanically Milled 17-4 Ph Stainless Steel Powders
    • Materials & Design
    • Çetinkaya C, Findik T, Özbilen S (2006). An Investigation Into The Effect of Experimental Parameters on Powder GS of The Mechanically Milled 17-4 Ph Stainless Steel Powders. Materials & Design., 28: 773-782.
    • (2006) , vol.28 , pp. 773-782
    • Çetinkaya, C.1    Findik, T.2    Özbilen, S.3
  • 6
    • 0033882142 scopus 로고    scopus 로고
    • A neural network approach for selection of powder metallurgy materials and process parameters
    • Cherian RP, Smith LN, Midha PS (2000). A neural network approach for selection of powder metallurgy materials and process parameters. Artificial Intelligence Eng., 14: 39-44.
    • (2000) Artificial Intelligence Eng , vol.14 , pp. 39-44
    • Cherian, R.P.1    Smith, L.N.2    Midha, P.S.3
  • 7
    • 33750418451 scopus 로고    scopus 로고
    • An artificial neural network model for toughness properties in microalloyed steel in consideration of industrial production conditions
    • Çöl M, Ertunc HM, Yilmaz M (2007). An artificial neural network model for toughness properties in microalloyed steel in consideration of industrial production conditions. Mater. Design., 28: 488-495.
    • (2007) Mater. Design , vol.28 , pp. 488-495
    • Çöl, M.1    Ertunc, H.M.2    Yilmaz, M.3
  • 8
    • 33645543943 scopus 로고    scopus 로고
    • Mapping the input-output relationship in HSLA steels through expert neural network
    • Datta S, Banerjee MK (2006). Mapping the input-output relationship in HSLA steels through expert neural network. Mater. Sci. Eng., 20: 254-264
    • (2006) Mater. Sci. Eng , vol.20 , pp. 254-264
    • Datta, S.1    Banerjee, M.K.2
  • 9
    • 48849104204 scopus 로고    scopus 로고
    • Modeling of the effects of length to diameter ratio and nozzle number on the performance of counterflow Ranque-Hilsch vortex tubes using artificial neural Networks
    • Dincer K, Tasdemir S, Baskaya S, Uysal BZ (2008). Modeling of the effects of length to diameter ratio and nozzle number on the performance of counterflow Ranque-Hilsch vortex tubes using artificial neural Networks, Appl. Therm. Eng., 28: 2380-2390.
    • (2008) Appl. Therm. Eng , vol.28 , pp. 2380-2390
    • Dincer, K.1    Tasdemir, S.2    Baskaya, S.3    Uysal, B.Z.4
  • 10
    • 58649116927 scopus 로고    scopus 로고
    • Modeling of the Effects of Plug Tip Angle on the Performance of Counter-Flow Ranque-Hilsch Vortex Tubes Using Artificial Neural Networks
    • Dincer K, Taşdemir Ş, Başkaya Ş, Uysal BZ (2008). Modeling of the Effects of Plug Tip Angle on the Performance of Counter-Flow Ranque-Hilsch Vortex Tubes Using Artificial Neural Networks. J. Therm. Sci. Technol., 29: 1-7.
    • (2008) J. Therm. Sci. Technol , vol.29 , pp. 1-7
    • Dincer, K.1    Taşdemir, S.2    Başkaya, S.3    Uysal, B.Z.4
  • 11
    • 0019652372 scopus 로고
    • Yurchikov E. E, Barinov V. A
    • Ermakov AE (1981). Yurchikov E. E, Barinov V. A. Phys. Met. Metallogr. 52(6): 50-58.
    • (1981) Phys. Met. Metallogr , vol.52 , Issue.6 , pp. 50-58
    • Ermakov, A.E.1
  • 12
    • 2342488022 scopus 로고    scopus 로고
    • Analysis of stress ratio effects on fatigue propagation in a sintered duplex steel by experimentation and artificial neural network approaches
    • Iacoviello F, Iacoviello D, Cavallini M (2004). Analysis of stress ratio effects on fatigue propagation in a sintered duplex steel by experimentation and artificial neural network approaches. Int. J. Fatigue, 26: 819-828.
    • (2004) Int. J. Fatigue , vol.26 , pp. 819-828
    • Iacoviello, F.1    Iacoviello, D.2    Cavallini, M.3
  • 13
    • 34250726665 scopus 로고    scopus 로고
    • Determination of residual stresses based on heat treatment conditions and densities on a hybrid (FLN2-4405) powder metallurgy steel using artificial neural network
    • Kafkas F, Karataş Ç, Sozen A, Arcaklioglu E, Saritas S (2006). Determination of residual stresses based on heat treatment conditions and densities on a hybrid (FLN2-4405) powder metallurgy steel using artificial neural network. Mater. Design. 28: 2431-2442.
    • (2006) Mater. Design , vol.28 , pp. 2431-2442
    • Kafkas, F.1    Karataş, C.2    Sozen, A.3    Arcaklioglu, E.4    Saritas, S.5
  • 14
    • 0242269356 scopus 로고    scopus 로고
    • Artificial intelligence for the modeling and control of combustion processes: A review
    • Kalogirou SA (2003). Artificial intelligence for the modeling and control of combustion processes: A review. Progress in Energy and Combustion Sci., 29 (6): 515-566.
    • (2003) Progress in Energy and Combustion Sci , vol.29 , Issue.6 , pp. 515-566
    • Kalogirou, S.A.1
  • 16
    • 0032722381 scopus 로고    scopus 로고
    • Failure of carbon/epoxy composite tubes under combined axial and torsional loading 1. Experimental results and prediction of biaxial strength by the use of neural Networks
    • Lee CS, Hwang W, Park HC, Han KS (1999). Failure of carbon/epoxy composite tubes under combined axial and torsional loading 1. Experimental results and prediction of biaxial strength by the use of neural Networks. Composites Sci. Technol., 59: 1779-1788.
    • (1999) Composites Sci. Technol , vol.59 , pp. 1779-1788
    • Lee, C.S.1    Hwang, W.2    Park, H.C.3    Han, K.S.4
  • 17
    • 0348107289 scopus 로고    scopus 로고
    • Application of artificial neural networks for modeling correlations in titanium alloys
    • Malinov S, Sha W (2004). Application of artificial neural networks for modeling correlations in titanium alloys. Mater. Sci. Eng., 365: 202-211.
    • (2004) Mater. Sci. Eng , vol.365 , pp. 202-211
    • Malinov, S.1    Sha, W.2
  • 18
    • 77954421586 scopus 로고    scopus 로고
    • Modeling of Freeze Drying Behaviors of Strawberries by Using Artificial Neural Network
    • Menlik T, Kirmaci V, Usta H (2009). Modeling of Freeze Drying Behaviors of Strawberries by Using Artificial Neural Network. J. Therm. Sci. Technol., 29: 11-21.
    • (2009) J. Therm. Sci. Technol , vol.29 , pp. 11-21
    • Menlik, T.1    Kirmaci, V.2    Usta, H.3
  • 20
    • 0037427636 scopus 로고    scopus 로고
    • Prediction of the process parameters of metal powder preform forging using artificial neural network (ANN)
    • Ohdar RK, Pahsa S (2003). Prediction of the process parameters of metal powder preform forging using artificial neural network (ANN). J. Mater. Processing Technol., 132: 227-234.
    • (2003) J. Mater. Processing Technol , vol.132 , pp. 227-234
    • Ohdar, R.K.1    Pahsa, S.2
  • 21
    • 15844387704 scopus 로고    scopus 로고
    • Modeling of a continuous fluidized bed dryer using artificial neural Networks Int. Commun
    • Satish S., Pydi SY (2005). Modeling of a continuous fluidized bed dryer using artificial neural Networks, Int. Commun. Heat Mass Transf., 32: 539-547.
    • (2005) Heat Mass Transf , vol.32 , pp. 539-547
    • Satish, S.1    Pydi, S.Y.2
  • 22
    • 33749266885 scopus 로고    scopus 로고
    • Performance and exhaust emissions of a gasoline engine using artificial neural network
    • Sayin C, Ertunc HM, Hosoz M, Kilicaslan İ, Canakci M (2007). Performance and exhaust emissions of a gasoline engine using artificial neural network. Appl. Therm. Eng., 27(1): 46-54.
    • (2007) Appl. Therm. Eng , vol.27 , Issue.1 , pp. 46-54
    • Sayin, C.1    Ertunc, H.M.2    Hosoz, M.3    Kilicaslan, I.4    Canakci, M.5
  • 23
    • 33646387155 scopus 로고    scopus 로고
    • Mixtures flow boiling: Modeling heat transfer through artificial neural Networks
    • Scalabrin G., Condosta M, Marchi P (2006). Mixtures flow boiling: modeling heat transfer through artificial neural Networks. Int. J. Therm. Sci., 45(7): 664-680.
    • (2006) Int. J. Therm. Sci , vol.45 , Issue.7 , pp. 664-680
    • Scalabrin, G.1    Condosta, M.2    Marchi, P.3
  • 24
    • 33745910545 scopus 로고    scopus 로고
    • Artificial intelligent methods for thermodynamic evaluation of ammonia-water refrigeration systems
    • Sencan A (2006). Artificial intelligent methods for thermodynamic evaluation of ammonia-water refrigeration systems. Energy Conversion and Manage., 47: 3319-3332.
    • (2006) Energy Conversion and Manage , vol.47 , pp. 3319-3332
    • Sencan, A.1
  • 25
    • 0037080085 scopus 로고    scopus 로고
    • A neural network approach for solution of the inverse problem for selection of powder metallurgy materials
    • Smith LN, German RM, Smith ML (2002). A neural network approach for solution of the inverse problem for selection of powder metallurgy materials. J. Mater. Processing Technol., 20: 419-425.
    • (2002) J. Mater. Processing Technol , vol.20 , pp. 419-425
    • Smith, L.N.1    German, R.M.2    Smith, M.L.3
  • 26
    • 3543083930 scopus 로고    scopus 로고
    • Performance prediction of a vapour-compression heat-pump
    • Sozen A, Arcaklioǧlu E, Erisen A, Akçayol MA (2004). Performance prediction of a vapour-compression heat-pump. Appl. Energy, 79(3): 327-344.
    • (2004) Appl. Energy , vol.79 , Issue.3 , pp. 327-344
    • Sozen, A.1    ArcaklioǧLu, E.2    Erisen, A.3    Akçayol, M.A.4
  • 27
    • 0034742774 scopus 로고    scopus 로고
    • Mechanical alloying and milling
    • Suryanarayana C (2001). Mechanical alloying and milling. Progress in Mater. Sci., 46: 1-184.
    • (2001) Progress in Mater. Sci , vol.46 , pp. 1-184
    • Suryanarayana, C.1
  • 28
    • 70349097968 scopus 로고    scopus 로고
    • Prediction of surface roughness using artificial neural network in Lathe. CompSysTech'08: International Conference on Computer Systems
    • Bulgaria
    • Taşdemir Ş, Neşeli S. Saritaş, İ., Yaldiz S (2008). Prediction of surface roughness using artificial neural network in Lathe. CompSysTech'08: International Conference on Computer Systems, Bulgaria, IIIB.6-1- IIIB.6-8.
    • (2008)
    • Taşdemir, S.1    Neşeli, S.2    Saritaş, I.3    Yaldiz, S.4
  • 29
    • 0345102492 scopus 로고    scopus 로고
    • Prediction of the amount of PCA for mechanical milling
    • Zhang YF, Lu L, Yap SM (1999). Prediction of the amount of PCA for mechanical milling. J. Mater. Processing Technol., 89-90: 260-265.
    • (1999) J. Mater. Processing Technol , vol.89-90 , pp. 260-265
    • Zhang, Y.F.1    Lu, L.2    Yap, S.M.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.