An Automated Visual Inspection System for the Classification of the Phases of Ti-6Al-4V Titanium Alloy

1. Malamas, E.N., Petrakis, E.G.M., Zervakis, M., Petit, L., Legat, J.D.: A survey on industrial vision systems, applications and tools. Image and Vision Computing 21(2), 171–188 (2003)

   Article  Google Scholar 

2. Tan, W., Chengdong, C., Zhao, C., Li, S.: Study on key technology of metallographical image processing and recognition. In: Control and Decision Conference, CCDC 2008, pp. 1832–1837. Chinese (July 2008)

   Google Scholar 

3. Gegner, J., Öchsner, A.: Digital image analysis in quantitative metallography. Pract. Metallogr. 38(9), 499–513 (2001)

   Google Scholar 

4. Komenda, J.: Automatic recognition of complex microstructures using the Image Classifier. Materials Characterization 46(2-3), 87–92 (2001)

   Article  Google Scholar 

5. Topalova, I., Mihailov, A., Tzokev, A.: Automated classification of heat resistant steel structures based on neural networks. In: IEEE 25th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2008, December 3-5, pp. 437–440 (2008)

   Google Scholar 

6. Chatterjee, O., Das, K., Dutta, S., Datta, S., Saha, S.K.: Phase extraction and boundary removal in dual phase steel micrographs. In: 2010 Annual IEEE India Conference (INDICON), December 17-19, pp. 1–5 (2010)

   Google Scholar 

7. Codaro, E.N., Nakazato, R.Z., Horovistiz, A.L., Ribeiro, L.M.F., Ribeiro, R.B., Hein, L.R.O.: An image analysis study of pit formation on Ti–6Al–4V. Materials Science and Engineering: A 341(1-2), 202–210 (2003)

   Article  Google Scholar 

8. Chrapoński, J., Szkliniarz, W.: Quantitative metallography of two-phase titanium alloys. Materials Characterization 46(2-3), 149–154 (2001)

   Article  Google Scholar 

9. Tejrzanowski, T., Spychalski, W., Rozniatowski, K.K.: Image Based Analysis of Complex Microstructures of Engineering Materials. Int. J. Appl. Math. Comput. Sci. 18(1), 33–39 (2008)

   Google Scholar 

10. Sha, W., Malinov, S.: Titanium Alloy: Modelling of Microstructure, Properties and Applications. Woodhead Publishing Series in Metals and Surface Engineering, vol. 31 (April 2009)

    Google Scholar 

11. Bruschi, S., Poggio, S., Quadrini, F., Tata, M.E.: Workability of Ti–6Al–4V alloy at high temperatures and strain rates. Materials Letters 58(27-28), 3622–3629 (2004)

    Article  Google Scholar 

12. Buffa, G., Ducato, A., Fratini, L.: Numerical procedure for residual stresses prediction in friction stir welding. Finite Elements in Analysis and Design 47(4), 470–476 (2011)

    Article  Google Scholar 

13. Fratini, L., Buffa, G., Shivpuri, R.: Mechanical and metallurgical effects of in process cooling during friction stir welding of AA7075-T6 butt joints. Acta Materialia 58(6), 2056–2067 (2010)

    Article  Google Scholar 

14. Serrano, N., Savakis, A., Luo, J.: Improved scene classification using efficient low-level features and semantic cues. Pattern Recognition 37, 1773–1784 (2004)

    Article  MATH  Google Scholar 

15. Tamura, H., Mori, S., Yamawaki, T.: Texture features corresponding to visual perception. IEEE Trans. Syst. Man Cybern. 8(6), 460–473 (1978)

    Article  Google Scholar 

16. Won, C.S., Park, D.K.: Efficient Use of MPEG-7 Edge Histogram Descriptor. ETRI Journal 24(1), 23–30 (2002)

    Article  Google Scholar 

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