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POROZİTE ANALİZİNE DERİN ÖĞRENME YAKLAŞIMI: U-NET İLE DİNAMİK EŞİKLEME

Year 2024, Volume: 27 Issue: 3, 1069 - 1077, 03.09.2024

Saim Ervural , Emre Burak Ertuş , Hüseyin Furkan Ceran

https://doi.org/10.17780/ksujes.1422819

Abstract

Gözenekli malzemelerin porozite değerinin belirlenmesinde birçok fiziksel yöntem kullanılmaktadır ve bu yöntemler genellikle maliyetli cihazlar marifetiyle uygulanmaktadır. Ayrıca malzemelerde farklı seviyelerde (mikro, mezo ve makro) gözeneklilik bulunması kullanılacak yöntem seçimini de etkilemektedir. Bunun yanında görüntü işleme yöntemleri kullanılarak da porozite değeri hesaplanabilmekte, böylece hem zaman hem de maliyet tasarrufu sağlanabilmektedir. Bu çalışmada görüntü işleme tekniğindeki eşik belirleme aşamasında ImageJ programı kullanılarak sayısal porozitesi eşikli görüntü olarak görüntü verisine aktarılmıştır. Oluşturulan eşikli etiket verileri ile girdi SEM görüntüleri eşlenmiş ve oluşturulan veriseti veri artırma teknikleri kullanılarak genişletilmiştir. Çalışmada evrişimli sinir ağlarının özelleşmiş bir versiyonu olan U-Net mimarisi kullanılmış ve U-Net mimarisi, mikroskop görüntülerini segmentlere ayırarak gözenekli bölgeleri belirlemiş ve bu segmentlerin eşiklenmiş görüntülerine dayalı olarak gözeneklilik değerleri hesaplanmıştır. Uygulamada literatürden elde edilen gözenekli malzemelerin SEM görüntüleri kullanılmış, etiket görüntüleri olarak ise Arşimet prensibindeki porozite değerlerine göre gözenekli malzemenin ikili çıktıları manuel olarak eşiklenerek kaydedilmiştir. Çalışma sonucunda genel olarak fiziki ölçümlerle korelasyon sağlamış ve derin öğrenmeden faydalanılan dinamik eşikleme sayesinde klasik görüntü işleme yöntemlerine göre daha başarılı sonuçlar elde edilmiştir.

Keywords

Gözenekli malzemeler porozite derin öğrenme görüntü işleme

References

  • Ahn, J., Jung, J., Kim, S., & Han, S.-I. (2014). X-ray image analysis of porosity of pervious concretes. GEOMATE Journal, 6(11), 796-799.
  • Aly, A. F., Agameia, A., Eldesouky, A. S., & Sharaf, M. A. (2011). Scaffold development and characterization using CAD system. Am. J. Biomed. Sci, 3(4), 268-277.
  • Arena, E., Rueden, C., Hiner, M., Wang, S., Yuan, M., & Eliceiri, K. (2017). Quantitating the cell: turning images into numbers with ImageJ, Wiley Interdiscip. Rev. Dev. Biol., 6.
  • Barea, R., Osendi, M. I., Ferreira, J. M., & Miranzo, P. (2005). Thermal conductivity of highly porous mullite material. Acta materialia, 53(11), 3313-3318.
  • Barmala, M., Moheb, A., & Emadi, R. (2009). Applying Taguchi method for optimization of the synthesis condition of nano-porous alumina membrane by slip casting method. Journal of Alloys and Compounds, 485(1-2), 778-782.
  • Buckman, J., Bankole, S. A., Zihms, S., Lewis, H., Couples, G., & Corbett, P. W. (2017). Quantifying porosity through automated image collection and batch image processing: case study of three carbonates and an aragonite cemented sandstone. Geosciences, 7(3), 70.
  • Cardoso, V. G., da Silva Barros, E. N., & Barbosa, J. A. (2020). Porosity features extraction based on image segmentation technique applying k-means clustering algorithm. Rio Oil & Gas.
  • Castilho, M., Gouveia, B., Pires, I., Rodrigues, J., & Pereira, M. (2015). The role of shell/core saturation level on the accuracy and mechanical characteristics of porous calcium phosphate models produced by 3Dprinting. Rapid Prototyping Journal, 21(1), 43-55.
  • Chen, F., Zhao, J., Wang, H., Li, H., Yin, G., Cai, M., . . . Shen, Q. (2023). Oil-Retention and Oil-Bearing Tribological Properties of Nanoporous Copper Prepared Using a Chemical Dealloying Method. Metals, 13(7), 1232.
  • Chen, M., Zhu, L., Dong, Y., Li, L., & Liu, J. (2016). Waste-to-resource strategy to fabricate highly porous whisker-structured mullite ceramic membrane for simulated oil-in-water emulsion wastewater treatment. ACS Sustainable Chemistry & Engineering, 4(4), 2098-2106.
  • Chinnam, R., Bernardo, E., Will, J., & Boccaccini, A. (2015). Processing of porous glass ceramics from highly crystallisable industrial wastes. Advances in Applied Ceramics, 114(sup1), S11-S16.
  • Deng, W., Yu, X., Sahimi, M., & Tsotsis, T. T. (2014). Highly permeable porous silicon carbide support tubes for the preparation of nanoporous inorganic membranes. Journal of Membrane Science, 451, 192-204.
  • Dı́az, A., & Hampshire, S. (2004). Characterisation of porous silicon nitride materials produced with starch. Journal of the European Ceramic Society, 24(2), 413-419.
  • Elia, P., Nativ-Roth, E., Zeiri, Y., & Porat, Z. e. (2016). Determination of the average pore-size and total porosity in porous silicon layers by image processing of SEM micrographs. Microporous and Mesoporous Materials, 225, 465-471.
  • Ertuş, E. B. (2020). Production and characterization of hierarchically porous transparent glasses.
  • Ervural, S. (2021). Sınırlı veri setiyle sınıflama uygulamalarına yeni bir yaklaşım.
  • Fukushima, M., Nakata, M., Zhou, Y., Ohji, T., & Yoshizawa, Y.-i. (2010). Fabrication and properties of ultra highly porous silicon carbide by the gelation–freezing method. Journal of the European Ceramic Society, 30(14), 2889-2896.
  • Fukushima, M., & Yoshizawa, Y. i. (2014). Fabrication of highly porous silica thermal insulators prepared by gelation–freezing route. Journal of the American Ceramic Society, 97(3), 713-717.
  • Garfi, G., John, C. M., Berg, S., & Krevor, S. (2020). The sensitivity of estimates of multiphase fluid and solid properties of porous rocks to image processing. Transport in Porous Media, 131(3), 985-1005.
  • Ghasemi‐Mobarakeh, L., Semnani, D., & Morshed, M. (2007). A novel method for porosity measurement of various surface layers of nanofibers mat using image analysis for tissue engineering applications. Journal of applied polymer science, 106(4), 2536-2542.
  • Gültekin, E. E., Topateş, G., & Kurama, S. (2017). The effects of sintering temperature on phase and pore evolution in porcelain tiles. Ceramics International, 43(14), 11511-11515.
  • Haines, T. J., Neilson, J. E., Healy, D., Michie, E. A., & Aplin, A. C. (2015). The impact of carbonate texture on the quantification of total porosity by image analysis. Computers & geosciences, 85, 112-125.
  • Ishizaki, K., Komarneni, S., & Nanko, M. (2013). Porous Materials: Process technology and applications (Vol. 4): Springer science & business media.
  • Kazup, Á., Fegyverneki, G., & Gácsi, Z. (2022). Evaluation of the Applicability of Computer-Aided Porosity Testing Methods for Different Pore Structures. Metallography, Microstructure, and Analysis, 11(5), 774-789.
  • Kim, C. E., Yoon, J. S., & Hwang, H. J. (2009). Synthesis of nanoporous silica aerogel by ambient pressure drying. Journal of sol-gel science and technology, 49, 47-52.
  • Koç, A. B., & Akgün, D. (2021). U-net mimarileri ile glioma tümör segmentasyonu üzerine bir literatür çalışması. Avrupa Bilim ve Teknoloji Dergisi(26), 407-414.
  • Lacerda, L. D., Souza, D. F., Nunes, E. H., & Houmard, M. (2018). Macroporous alumina structures tailored by freeze-casting using naphthalene–camphor as freezing vehicle. Ceramics International, 44(13), 16010-16016.
  • Lee, E.-J., Koh, Y.-H., Yoon, B.-H., Kim, H.-E., & Kim, H.-W. (2007). Highly porous hydroxyapatite bioceramics with interconnected pore channels using camphene-based freeze casting. Materials Letters, 61(11-12), 2270-2273.
  • Lee, M., & Sordelet, D. (2006). Nanoporous metallic glass with high surface area. Scripta materialia, 55(10), 947-950.
  • Leu, L., Berg, S., Enzmann, F., Armstrong, R. T., & Kersten, M. (2014). Fast X-ray micro-tomography of multiphase flow in berea sandstone: A sensitivity study on image processing. Transport in Porous Media, 105(2), 451-469.
  • Li, Z., Zhang, Z., Zhao, W., Li, X., Han, G., & Zhang, J. (2020). A simple method to control the pore structure and shape of freeze-cast porous SiC ceramics. Ceramics International, 46(16), 26078-26084.
  • Lin, X., Gong, H., Zhang, Y., Bi, J., Feng, Y., Liu, Y., & Wang, S. (2019). Dielectric properties of porous SiC/Si3N4 ceramics by polysilazane immersion-pyrolysis. Progress in Natural Science: Materials International, 29(2), 184-189.
  • Malik, J., Kiranyaz, S., Al-Raoush, R. I., Monga, O., Garnier, P., Foufou, S., . . . Baveye, P. C. (2022). 3D Quantum Cuts for automatic segmentation of porous media in tomography images. Computers & Geosciences, 159, 105017.
  • Mazón, P., & Piedad, N. (2018). Porous scaffold prepared from α′ L-Dicalcium silicate doped with phosphorus for bone grafts. Ceramics International, 44(1), 537-545.
  • Nakahira, A., Tamai, M., Miki, S., & Pezzotti, G. (2002). Fracture behavior and biocompatibility evaluation of nylon-infiltrated porous hydroxyapatite. Journal of materials science, 37, 4425-4430.
  • Novais, R. M., Seabra, M., & Labrincha, J. (2014). Ceramic tiles with controlled porosity and low thermal conductivity by using pore-forming agents. Ceramics International, 40(8), 11637-11648.
  • Ogura, K., Yamada, M., Hirahara, O., Mita, M., Erdman, N., & Nielsen, C. (2010). Gigantic montages with a fully automated FE-SEM (serial sections of a mouse brain tissue). Microscopy and Microanalysis, 16(S2), 52-53.
  • Peng, Y., Chen, J., Song, A. Y., Catrysse, P. B., Hsu, P.-C., Cai, L., . . . Wu, D. S. (2018). Nanoporous polyethylene microfibres for large-scale radiative cooling fabric. Nature sustainability, 1(2), 105-112.
  • Pinto, J., Dumon, M., Rodriguez-Perez, M. A., Garcia, R., & Dietz, C. (2014). Block copolymers self-assembly allows obtaining tunable micro or nanoporous membranes or depth filters based on PMMA; fabrication method and nanostructures. The Journal of Physical Chemistry C, 118(9), 4656-4663.
  • Rezaei, F., Izadi, H., Memarian, H., & Baniassadi, M. (2019). The effectiveness of different thresholding techniques in segmenting micro CT images of porous carbonates to estimate porosity. Journal of Petroleum Science and Engineering, 177, 518-527. doi:https://doi.org/10.1016/j.petrol.2018.12.063
  • Schneider, C. A., Rasband, W. S., & Eliceiri, K. W. (2012). NIH Image to ImageJ: 25 years of image analysis. Nature methods, 9(7), 671-675.
  • Sosa, J. M., Huber, D. E., Welk, B., & Fraser, H. L. (2014). Development and application of MIPAR™: a novel software package for two-and three-dimensional microstructural characterization. Integrating materials and manufacturing innovation, 3, 123-140.
  • Sulem, J., & Ouffroukh, H. (2006). Shear banding in drained and undrained triaxial tests on a saturated sandstone: Porosity and permeability evolution. International Journal of Rock Mechanics and Mining Sciences, 43(2), 292-310.
  • Wu, Z., Sun, L., Pan, J., & Wang, J. (2018). Highly porous Y2SiO5 ceramic with extremely low thermal conductivity prepared by foam‐gelcasting‐freeze drying method. Journal of the American Ceramic Society, 101(3), 1042-1047.
  • Xie, B., Zhao, H., Long, H., Peng, J., & Liu, R. (2019). 3D characteristics of pores in SiC particle preforms with different starch contents by X-ray micro-computed tomography. Ceramics International, 45(18), 23924-23933.
  • Yang, H., Li, J., Zhou, Z., & Ruan, J. (2013). Structural preparation and biocompatibility evaluation of highly porous Tantalum scaffolds. Materials Letters, 100, 152-155.
  • Zawrah, M., Khattab, R., Girgis, L. G., El Shereefy, E., & Sawan, S. A. (2014). Effect of CTAB as a foaming agent on the properties of alumina ceramic membranes. Ceramics International, 40(4), 5299-5305.
  • Zhang, R., Fang, D., Pei, Y., & Zhou, L. (2012). Microstructure, mechanical and dielectric properties of highly porous silicon nitride ceramics produced by a new water-based freeze casting. Ceramics International, 38(5), 4373-4377.
  • Zhang, Y., Yokogawa, Y., Feng, X., Tao, Y., & Li, Y. (2010). Preparation and properties of bimodal porous apatite ceramics through slip casting using different hydroxyapatite powders. Ceramics International, 36(1), 107-113.
  • Zou, Y., & Malzbender, J. (2016). Development and optimization of porosity measurement techniques. Ceramics International, 42(2), 2861-2870.
  • Zuo, K. H., Zhang, Y., Zeng, Y.-P., & Jiang, D. (2011). Pore-forming agent induced microstructure evolution of freeze casted hydroxyapatite. Ceramics International, 37(1), 407-410.

A DEEP LEARNING APPROACH TO POROSITY ANALYSIS: DYNAMIC THRESHOLDING WITH U-NET

Year 2024, Volume: 27 Issue: 3, 1069 - 1077, 03.09.2024

Saim Ervural , Emre Burak Ertuş , Hüseyin Furkan Ceran

https://doi.org/10.17780/ksujes.1422819

Abstract

Many physical methods are used to determine the porosity of materials and these methods are generally applied by employing high cost devices. Also, the existence of variable levels of porosity (micro, meso and macro) in the material affects the type of method to be used. The porosity value can also be calculated using image processing methods, thus saving both time and money. In this study, the numerical porosity value was transferred to the image data as a thresholded image by using ImageJ software during the threshold determination phase in the image processing technique. The generated thresholded label data and the input SEM images were mapped, and the generated dataset was enhanced using the data augmentation methods. The U-Net architecture, a specialised version of convolutional neural networks, was used in the study. The U-Net architecture segmented the microscope images to identify porous regions and calculated porosity values based on the thresholded images of these segments. SEM images of porous materials obtained from the literature were used in the application, and the binary outputs of the porous material according to the porosity values in Archimedes' principle were manually thresholded and recorded as label images. Results were generally correlated with physical measurements and more successful results were obtained than classical image processing methods, thanks to dynamic thresholding using deep learning.

Keywords

Porous materials porosity deep learning image processing

References

  • Ahn, J., Jung, J., Kim, S., & Han, S.-I. (2014). X-ray image analysis of porosity of pervious concretes. GEOMATE Journal, 6(11), 796-799.
  • Aly, A. F., Agameia, A., Eldesouky, A. S., & Sharaf, M. A. (2011). Scaffold development and characterization using CAD system. Am. J. Biomed. Sci, 3(4), 268-277.
  • Arena, E., Rueden, C., Hiner, M., Wang, S., Yuan, M., & Eliceiri, K. (2017). Quantitating the cell: turning images into numbers with ImageJ, Wiley Interdiscip. Rev. Dev. Biol., 6.
  • Barea, R., Osendi, M. I., Ferreira, J. M., & Miranzo, P. (2005). Thermal conductivity of highly porous mullite material. Acta materialia, 53(11), 3313-3318.
  • Barmala, M., Moheb, A., & Emadi, R. (2009). Applying Taguchi method for optimization of the synthesis condition of nano-porous alumina membrane by slip casting method. Journal of Alloys and Compounds, 485(1-2), 778-782.
  • Buckman, J., Bankole, S. A., Zihms, S., Lewis, H., Couples, G., & Corbett, P. W. (2017). Quantifying porosity through automated image collection and batch image processing: case study of three carbonates and an aragonite cemented sandstone. Geosciences, 7(3), 70.
  • Cardoso, V. G., da Silva Barros, E. N., & Barbosa, J. A. (2020). Porosity features extraction based on image segmentation technique applying k-means clustering algorithm. Rio Oil & Gas.
  • Castilho, M., Gouveia, B., Pires, I., Rodrigues, J., & Pereira, M. (2015). The role of shell/core saturation level on the accuracy and mechanical characteristics of porous calcium phosphate models produced by 3Dprinting. Rapid Prototyping Journal, 21(1), 43-55.
  • Chen, F., Zhao, J., Wang, H., Li, H., Yin, G., Cai, M., . . . Shen, Q. (2023). Oil-Retention and Oil-Bearing Tribological Properties of Nanoporous Copper Prepared Using a Chemical Dealloying Method. Metals, 13(7), 1232.
  • Chen, M., Zhu, L., Dong, Y., Li, L., & Liu, J. (2016). Waste-to-resource strategy to fabricate highly porous whisker-structured mullite ceramic membrane for simulated oil-in-water emulsion wastewater treatment. ACS Sustainable Chemistry & Engineering, 4(4), 2098-2106.
  • Chinnam, R., Bernardo, E., Will, J., & Boccaccini, A. (2015). Processing of porous glass ceramics from highly crystallisable industrial wastes. Advances in Applied Ceramics, 114(sup1), S11-S16.
  • Deng, W., Yu, X., Sahimi, M., & Tsotsis, T. T. (2014). Highly permeable porous silicon carbide support tubes for the preparation of nanoporous inorganic membranes. Journal of Membrane Science, 451, 192-204.
  • Dı́az, A., & Hampshire, S. (2004). Characterisation of porous silicon nitride materials produced with starch. Journal of the European Ceramic Society, 24(2), 413-419.
  • Elia, P., Nativ-Roth, E., Zeiri, Y., & Porat, Z. e. (2016). Determination of the average pore-size and total porosity in porous silicon layers by image processing of SEM micrographs. Microporous and Mesoporous Materials, 225, 465-471.
  • Ertuş, E. B. (2020). Production and characterization of hierarchically porous transparent glasses.
  • Ervural, S. (2021). Sınırlı veri setiyle sınıflama uygulamalarına yeni bir yaklaşım.
  • Fukushima, M., Nakata, M., Zhou, Y., Ohji, T., & Yoshizawa, Y.-i. (2010). Fabrication and properties of ultra highly porous silicon carbide by the gelation–freezing method. Journal of the European Ceramic Society, 30(14), 2889-2896.
  • Fukushima, M., & Yoshizawa, Y. i. (2014). Fabrication of highly porous silica thermal insulators prepared by gelation–freezing route. Journal of the American Ceramic Society, 97(3), 713-717.
  • Garfi, G., John, C. M., Berg, S., & Krevor, S. (2020). The sensitivity of estimates of multiphase fluid and solid properties of porous rocks to image processing. Transport in Porous Media, 131(3), 985-1005.
  • Ghasemi‐Mobarakeh, L., Semnani, D., & Morshed, M. (2007). A novel method for porosity measurement of various surface layers of nanofibers mat using image analysis for tissue engineering applications. Journal of applied polymer science, 106(4), 2536-2542.
  • Gültekin, E. E., Topateş, G., & Kurama, S. (2017). The effects of sintering temperature on phase and pore evolution in porcelain tiles. Ceramics International, 43(14), 11511-11515.
  • Haines, T. J., Neilson, J. E., Healy, D., Michie, E. A., & Aplin, A. C. (2015). The impact of carbonate texture on the quantification of total porosity by image analysis. Computers & geosciences, 85, 112-125.
  • Ishizaki, K., Komarneni, S., & Nanko, M. (2013). Porous Materials: Process technology and applications (Vol. 4): Springer science & business media.
  • Kazup, Á., Fegyverneki, G., & Gácsi, Z. (2022). Evaluation of the Applicability of Computer-Aided Porosity Testing Methods for Different Pore Structures. Metallography, Microstructure, and Analysis, 11(5), 774-789.
  • Kim, C. E., Yoon, J. S., & Hwang, H. J. (2009). Synthesis of nanoporous silica aerogel by ambient pressure drying. Journal of sol-gel science and technology, 49, 47-52.
  • Koç, A. B., & Akgün, D. (2021). U-net mimarileri ile glioma tümör segmentasyonu üzerine bir literatür çalışması. Avrupa Bilim ve Teknoloji Dergisi(26), 407-414.
  • Lacerda, L. D., Souza, D. F., Nunes, E. H., & Houmard, M. (2018). Macroporous alumina structures tailored by freeze-casting using naphthalene–camphor as freezing vehicle. Ceramics International, 44(13), 16010-16016.
  • Lee, E.-J., Koh, Y.-H., Yoon, B.-H., Kim, H.-E., & Kim, H.-W. (2007). Highly porous hydroxyapatite bioceramics with interconnected pore channels using camphene-based freeze casting. Materials Letters, 61(11-12), 2270-2273.
  • Lee, M., & Sordelet, D. (2006). Nanoporous metallic glass with high surface area. Scripta materialia, 55(10), 947-950.
  • Leu, L., Berg, S., Enzmann, F., Armstrong, R. T., & Kersten, M. (2014). Fast X-ray micro-tomography of multiphase flow in berea sandstone: A sensitivity study on image processing. Transport in Porous Media, 105(2), 451-469.
  • Li, Z., Zhang, Z., Zhao, W., Li, X., Han, G., & Zhang, J. (2020). A simple method to control the pore structure and shape of freeze-cast porous SiC ceramics. Ceramics International, 46(16), 26078-26084.
  • Lin, X., Gong, H., Zhang, Y., Bi, J., Feng, Y., Liu, Y., & Wang, S. (2019). Dielectric properties of porous SiC/Si3N4 ceramics by polysilazane immersion-pyrolysis. Progress in Natural Science: Materials International, 29(2), 184-189.
  • Malik, J., Kiranyaz, S., Al-Raoush, R. I., Monga, O., Garnier, P., Foufou, S., . . . Baveye, P. C. (2022). 3D Quantum Cuts for automatic segmentation of porous media in tomography images. Computers & Geosciences, 159, 105017.
  • Mazón, P., & Piedad, N. (2018). Porous scaffold prepared from α′ L-Dicalcium silicate doped with phosphorus for bone grafts. Ceramics International, 44(1), 537-545.
  • Nakahira, A., Tamai, M., Miki, S., & Pezzotti, G. (2002). Fracture behavior and biocompatibility evaluation of nylon-infiltrated porous hydroxyapatite. Journal of materials science, 37, 4425-4430.
  • Novais, R. M., Seabra, M., & Labrincha, J. (2014). Ceramic tiles with controlled porosity and low thermal conductivity by using pore-forming agents. Ceramics International, 40(8), 11637-11648.
  • Ogura, K., Yamada, M., Hirahara, O., Mita, M., Erdman, N., & Nielsen, C. (2010). Gigantic montages with a fully automated FE-SEM (serial sections of a mouse brain tissue). Microscopy and Microanalysis, 16(S2), 52-53.
  • Peng, Y., Chen, J., Song, A. Y., Catrysse, P. B., Hsu, P.-C., Cai, L., . . . Wu, D. S. (2018). Nanoporous polyethylene microfibres for large-scale radiative cooling fabric. Nature sustainability, 1(2), 105-112.
  • Pinto, J., Dumon, M., Rodriguez-Perez, M. A., Garcia, R., & Dietz, C. (2014). Block copolymers self-assembly allows obtaining tunable micro or nanoporous membranes or depth filters based on PMMA; fabrication method and nanostructures. The Journal of Physical Chemistry C, 118(9), 4656-4663.
  • Rezaei, F., Izadi, H., Memarian, H., & Baniassadi, M. (2019). The effectiveness of different thresholding techniques in segmenting micro CT images of porous carbonates to estimate porosity. Journal of Petroleum Science and Engineering, 177, 518-527. doi:https://doi.org/10.1016/j.petrol.2018.12.063
  • Schneider, C. A., Rasband, W. S., & Eliceiri, K. W. (2012). NIH Image to ImageJ: 25 years of image analysis. Nature methods, 9(7), 671-675.
  • Sosa, J. M., Huber, D. E., Welk, B., & Fraser, H. L. (2014). Development and application of MIPAR™: a novel software package for two-and three-dimensional microstructural characterization. Integrating materials and manufacturing innovation, 3, 123-140.
  • Sulem, J., & Ouffroukh, H. (2006). Shear banding in drained and undrained triaxial tests on a saturated sandstone: Porosity and permeability evolution. International Journal of Rock Mechanics and Mining Sciences, 43(2), 292-310.
  • Wu, Z., Sun, L., Pan, J., & Wang, J. (2018). Highly porous Y2SiO5 ceramic with extremely low thermal conductivity prepared by foam‐gelcasting‐freeze drying method. Journal of the American Ceramic Society, 101(3), 1042-1047.
  • Xie, B., Zhao, H., Long, H., Peng, J., & Liu, R. (2019). 3D characteristics of pores in SiC particle preforms with different starch contents by X-ray micro-computed tomography. Ceramics International, 45(18), 23924-23933.
  • Yang, H., Li, J., Zhou, Z., & Ruan, J. (2013). Structural preparation and biocompatibility evaluation of highly porous Tantalum scaffolds. Materials Letters, 100, 152-155.
  • Zawrah, M., Khattab, R., Girgis, L. G., El Shereefy, E., & Sawan, S. A. (2014). Effect of CTAB as a foaming agent on the properties of alumina ceramic membranes. Ceramics International, 40(4), 5299-5305.
  • Zhang, R., Fang, D., Pei, Y., & Zhou, L. (2012). Microstructure, mechanical and dielectric properties of highly porous silicon nitride ceramics produced by a new water-based freeze casting. Ceramics International, 38(5), 4373-4377.
  • Zhang, Y., Yokogawa, Y., Feng, X., Tao, Y., & Li, Y. (2010). Preparation and properties of bimodal porous apatite ceramics through slip casting using different hydroxyapatite powders. Ceramics International, 36(1), 107-113.
  • Zou, Y., & Malzbender, J. (2016). Development and optimization of porosity measurement techniques. Ceramics International, 42(2), 2861-2870.
  • Zuo, K. H., Zhang, Y., Zeng, Y.-P., & Jiang, D. (2011). Pore-forming agent induced microstructure evolution of freeze casted hydroxyapatite. Ceramics International, 37(1), 407-410.
There are 51 citations in total.

Details

Primary Language Turkish
Subjects Image Processing, Deep Learning, Materials Science and Technologies, Computational Material Sciences
Journal Section Materials Science and Engineering
Authors

Saim Ervural 0000-0003-4104-1928

Emre Burak Ertuş 0000-0002-3897-2409

Hüseyin Furkan Ceran 0009-0006-2169-4680

Publication Date September 3, 2024
Submission Date January 23, 2024
Acceptance Date August 28, 2024
Published in Issue Year 2024Volume: 27 Issue: 3

Cite

APA Ervural, S., Ertuş, E. B., & Ceran, H. F. (2024). POROZİTE ANALİZİNE DERİN ÖĞRENME YAKLAŞIMI: U-NET İLE DİNAMİK EŞİKLEME. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 27(3), 1069-1077. https://doi.org/10.17780/ksujes.1422819
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