Araştırma Makalesi
BibTex RIS Kaynak Göster

Regression Modelling to Study Wear Properties of Experimental Produced Porcelain Ceramics

Yıl 2023, Cilt: 2 Sayı: 2, 25 - 31, 14.02.2024

Ahmet Gürkan Yüksek Tahsin Boyraz Ahmet Akkuş

Öz

In this study, the production and wear properties of porcelain ceramics produced by powder metallurgy method were examined and modelling with regression were studied using the experimental data obtained. Porcelain ceramics were prepared by the powder metallurgy route. Mixtures prepared by mechanical alloying method in alumina ball mills were produced by sintering under normal atmospheric conditions after being shaped in a dry press. After drying, the powders were compressed by uniaxial pressing at 200 MPa. The green compacts were sintered at 1100-1200 oC for 1-5 h in air. Then, characterization studies of the sintered samples were carried out and the wear experimental results obtained were converted into data suitable for modelling with regression. In the continuation of the study, experimental wear results using regression was analysed and modelled. Wear load, wear time, sintering temperature and sintering time were used as regression input variables. Wear values were taken as output variables of regression. An regression was established for the prediction of wear properties of porcelain ceramic composites. As a result, the training results and test results were compared with the actual values to control the network performance. A good agreement was observed between the experimental and regression model results. After the regression estimation, confirmation tests were performed to confirm the experimental results.

Anahtar Kelimeler

Ceramic Porcelain Wear Regression.

Kaynakça

  • Abdullah, Ö., 2005. Türkiye’deki Ham ve İşlenmiş Petrol Ürünü Fiyatlarının Makro Ekonomik Büyüklüklere Etkisi, Adnan Menderes Üniversitesi Sosyal bilimler Enstitüsü, Doktora Tezi, Aydın.
  • Akkuş, A. and Boyraz, T., 2019. Fabrication and characterization of aluminium titanate and mullite added Porcelain ceramics, J. Ceram. Proc. Res., 20[1] 54-58.
  • Akkuş, A. and Boyraz, T., 2018. Investigation of wear properties of CaO, MgO added stabilized zirconia ceramics produced by different pressing methods, J. Ceram. Proc. Res., 19[3] 249~252.
  • Al-Ghandoor, A., Jaber, et al., 2009. Residential Past and Future Energy Consumption: Potential Savings and Environmental Impact. Renewable and Sustainable Energy Reviews, 13 [6-7] 1262-1274.
  • Aydin, T., Bican, O., & Gümrük, R. (2020). Investigation of wear resistance of the porcelain tile bodies by solid particle impingement using alumina particles. Journal of the Australian Ceramic Society, 56, 525-531.
  • Basavaraju, A., Du, J., Zhou, F., & Ji, J. (2019). A machine learning approach to road surface anomaly assessment using smartphone sensors. IEEE Sensors Journal, 20(5), 2635-2647.
  • Baudín, C., Tricoteaux, A., & Joire, H. (2014). Improved resistance of alumina to mild wear by aluminium titanate additions. Journal of the European Ceramic Society, 34(1), 69-80.
  • Bernard, J. T., Idoudi, N., Khalaf, L., & Yélou, C. (2007). Finite sample multivariate structural change tests with application to energy demand models. Journal of Econometrics, 141(2), 1219-1244.
  • Binder, J. J., 1985. On the use of the multivariate regression model in event studies. Journal of Accounting Research, 370-383.
  • Boyraz, T. and Akkuş, A. 2021. Investigation of wear properties of mullite and aluminium titanate added porcelain ceramics, Journal of Ceramic Processing Research, 22(2), 226-231.
  • Breiman, L., 2001. Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author), Stat. Sci., 16/3.
  • Buckley, D. H. and Miyoshi, K., 1984. Friction and wear of ceramics, Wear, 100[1–3], 333-353.
  • Bueno, S., Micele, L., Melandri, C., Baudin, C., & De Portu, G., 2011. Improved wear behaviour of alumina–aluminium titanate laminates with low residual stresses and large grained interfaces. Journal of the European Ceramic Society, 31(4), 475-483.
  • Catalina, T., Virgone, J., & Blanco, E., 2008. Development and validation of regression models to predict monthly heating demand for residential buildings. Energy and buildings, 40(10), 1825-1832.
  • Demirbuğan, M. A., 2006. Konut Sektörü İçin Linyit Kömürü ‘Tüketici Fazlası”, Madencilik Dergisi, 45, 29-40.
  • Doğan, T. and Beyza, V., 2009. Türkiye Birincil Enerji Kaynaklarının Piyasasının Zaman Serileri İle İstatistiksel Analizi, Marmara Üniversitesi, Yüksek Lisans Tezi, İstanbul.
  • Fausett, L. V., 1994. Fundamentals of neural networks: architectures, algorithms, and applications. Englewood Cliffs, NJ: Prentice-Hall.
  • McCulloch, W. S., & Pitts, W., 1943. A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 5, 115-133.
  • Guresen E., and Kayakutlu, G., 2011. Definition of artificial neural networks with comparison to other networks, Procedia Comput. Sci., 3, 426-433.
  • Gurney, K., 2018. An Introduction to Neural Networks, CRC Press.
  • Hassan, A. M., Alrashdan, A., Hayajneh, M. T., & Mayyas, A. T., 2009. Prediction of density, porosity and hardness in aluminum–copper-based composite materials using artificial neural network. Journal of materials processing technology, 209(2), 894-899.
  • Haykin, S., 1994. Neural Networks: A Comprehensive Foundation. Macmillan Publishing, New York.
  • Huang, C. Z., Zhang, L., He, L., Sun, J., Fang, B., Zou, B., ... & Ai, X., 2002. A study on the prediction of the mechanical properties of a ceramic tool based on an artificial neural network. Journal of materials processing technology, 129(1-3), 399-402.
  • Iqbal, Y. and Lee, E.J., 2000. ‘’Microstructural evolution in triaxial porcelain’’, J. Am. Ceram. Soc. 83, 3121–3127.
  • Karaca, C. and Karacan, H., 2016. Çoklu Regresyon Metoduyla Elektrik Tüketim Talebini Etkileyen Faktörlerin İncelenmesi, Selçuk Üniversitesi Mühendislik Fakültesi Bilim ve Teknoloji Dergisi. 4, 183-194.
  • Kelleher, J. D., Mac Namee, B., & D'Arcy, A., 2015. Fundamentals of machine learning for predictive data analytics: algorithms. Worked examples, and case studies.
  • Kitouni, S. and Harabi, A., 2011. Sintering and mechanical properties of porcelains prepared from algerian raw materials, Cerâmica, 57, 453-460.
  • Kong, Y., Yang, Z., Zhang, G., & Yuan, Q., 1998. Friction and wear characteristics of mullite, ZTM and TZP ceramics. Wear, 218(2), 159-166.
  • Kubat, M., 1999. Neural networks: a comprehensive foundation by Simon Haykin, Macmillan, 1994, ISBN 0-02-352781-7., Knowl. Eng. Rev., 13, 409-412.
  • Lopez, S. Y. R., Rodriguez, J. S., & Sueyoshi, S. S., 2011. Determination of the activation energy for densification of porcelain stoneware. Journal of Ceramic Processing Research, 12(3), 228-232.
  • Luo, H. H., Zhang, F. C., & Roberts, S. G., 2008. Wear resistance of reaction sintered alumina/mullite composites. Materials Science and Engineering: A, 478(1-2), 270-275.
  • Madhiarasan, M. and Louzazni M., 2022. Analysis of Artificial Neural Network: Architecture, Types, and Forecasting Applications, Journal of Electrical and Computer Engineering, V.2022, ID 5416722.
  • Martín‐Márquez, J., De la Torre, A. G., Aranda, M. A., Rincón, J. M., & Romero, M., 2009. Evolution with temperature of crystalline and amorphous phases in porcelain stoneware. Journal of the American Ceramic Society, 92(1), 229-234.
  • Martín-Márquez, J., Rincón, J. M., & Romero, M., 2008. Effect of firing temperature on sintering of porcelain stoneware tiles. Ceramics International, 34(8), 1867-1873.
  • Özkan, G., 2016. Düzenleyici Reformların Türkiye Enerji Piyasası Üzerindeki Ekonomik Etkileri, Kırıkkale Üniversitesi, Yüksek Lisans Tezi, Kırıkkale.
  • Öztürk, Ç., Akpınar, S., & Tığ, M., 2022. Effect of calcined colemanite addition on properties of porcelain tile. Journal of the Australian Ceramic Society, 58(1), 321-331.
  • Pramod, R., Kumar, G. V., Gouda, P. S., & Mathew, A. T., 2018. A study on the Al2O3 reinforced Al7075 metal matrix composites wear behavior using artificial neural networks. Materials Today: Proceedings, 5(5), 11376-11385.
  • Ramanathan, A., Pullum, L. L., Hussain, F., Chakrabarty, D., & Jha, S. K., 2016. Integrating symbolic and statistical methods for testing intelligent systems: Applications to machine learning and computer vision. In 2016 Design, Automation & Test in Europe Conference & Exhibition (DATE) (pp. 786-791). IEEE.
  • Sacli, M., Onen, U. M. U. T., & Boyraz, T. A. H. S. İ. N., 2015. Microstructural characterization and thermal properties of aluminium titanate/porcelain ceramic matrix composites. Acta Physica Polonica A, 127(4).
  • Serragdj, I., Harabi, A., Kasrani, S., Foughali, L., & Karboua, N., 2019. Effect of ZrO 2 additions on densification and mechanical properties of modified resistant porcelains using economic raw materials. Journal of the Australian Ceramic Society, 55, 489-499.
  • Shekhawat, D., Agarwal, P., Singh, A., & Patnaik, A., 2022. Prediction of thermal and thermo‐mechanical behavior of nano‐zirconia reinforced aluminium matrix composites. Materialwissenschaft und Werkstofftechnik, 53(10), 1216-1228.
  • Sykes A. O., 1993. An Introduction to Regression Analysis, Coase-Sandor Institute for Law & Economics Working Paper No: 20, 1 – 33.
  • Taktak, S. and Baspinar, M.S., 2005. Wear and friction behaviour of alumina/mullite composite by sol–gel infiltration technique, Materials and Design, 26, 459–464.
  • Turkmen, O., Kucuk, A., & Akpinar, S., 2015. Effect of wollastonite addition on sintering of hard porcelain. Ceramics International, 41(4), 5505-5512.
  • Yu, H. Y., Cai, Z. B., Ren, P. D., Zhu, M. H., & Zhou, Z. R., 2006. Friction and wear behavior of dental feldspathic porcelain. Wear, 261(5-6), 611-621.
  • Yuksek, A. G., Tuzemen, E. S., & Elagoz, S., 2015. Modeling of reflectance properties of ZnO film using artificial neural networks. Journal of Optoelectronics and Advanced Materials, 17(11-12), 1615-1628.
  • Yumurtacı, Z. and Asmaz, E., 2004. Electric Energy Demand of Turkey for the Year 2050, Energy Sources, XXVI, 12, 1157-1164.
  • Yüzük, F., 2019. Çoklu Regresyon Analizi Ve Yapay Sinir Ağlari İle Türkiye Enerji Talep Tahmini, YL tezi, Sivas Cumhuriyet Üniversitesi, Sosyal Bilimler Enstitüsü, İşletme Ana Bilim Dalı Başkanlığı

Deneysel Olarak Üretilen Porselen Seramiklerin Aşınma Özelliklerini İncelemek için Regresyon Modellemesi

Yıl 2023, Cilt: 2 Sayı: 2, 25 - 31, 14.02.2024

Ahmet Gürkan Yüksek Tahsin Boyraz Ahmet Akkuş

Öz

Bu çalışmada toz metalurjisi yöntemi ile üretilen porselen seramiklerin üretim ve aşınma özellikleri incelenmiş ve elde edilen deneysel veriler kullanılarak regresyon ile modelleme çalışması yapılmıştır. Porselen seramikler toz metalurjisi yöntemi ile hazırlanmıştır. Alümina bilyalı değirmenlerde mekanik alaşımlama yöntemi ile hazırlanan karışımlar kuru preste şekillendirildikten sonra normal atmosferik koşullarda sinterlenerek üretilmiştir. Kurutulduktan sonra tozlar 200 MPa'da tek eksenli presleme ile sıkıştırılmıştır. Yeşil kompaktlar 1100-1200 oC'de 1-5 saat süreyle havada sinterlenmiştir. Ardından sinterlenen numunelerin karakterizasyon çalışmaları gerçekleştirilmiş ve elde edilen aşınma deneysel sonuçları regresyon ile modellemeye uygun verilere dönüştürülmüştür. Çalışmanın devamında regresyon kullanılarak deneysel aşınma sonuçları analiz edilmiş ve modellenmiştir. Regresyon girdi değişkenleri olarak aşınma yükü, aşınma süresi, sinterleme sıcaklığı ve sinterleme süresi kullanılmıştır. Aşınma değerleri regresyonun çıktı değişkenleri olarak alınmıştır. Porselen seramik kompozitlerin aşınma özelliklerinin tahmini için bir regresyon oluşturulmuştur. Sonuç olarak, ağ performansını kontrol etmek için eğitim sonuçları ve test sonuçları gerçek değerlerle karşılaştırılmıştır. Deneysel ve regresyon modeli sonuçları arasında iyi bir uyum gözlenmiştir. Regresyon tahmininden sonra, deneysel sonuçları doğrulamak için doğrulama testleri yapılmıştır.

Anahtar Kelimeler

Seramik Porselen Aşınma Regresyon

Kaynakça

  • Abdullah, Ö., 2005. Türkiye’deki Ham ve İşlenmiş Petrol Ürünü Fiyatlarının Makro Ekonomik Büyüklüklere Etkisi, Adnan Menderes Üniversitesi Sosyal bilimler Enstitüsü, Doktora Tezi, Aydın.
  • Akkuş, A. and Boyraz, T., 2019. Fabrication and characterization of aluminium titanate and mullite added Porcelain ceramics, J. Ceram. Proc. Res., 20[1] 54-58.
  • Akkuş, A. and Boyraz, T., 2018. Investigation of wear properties of CaO, MgO added stabilized zirconia ceramics produced by different pressing methods, J. Ceram. Proc. Res., 19[3] 249~252.
  • Al-Ghandoor, A., Jaber, et al., 2009. Residential Past and Future Energy Consumption: Potential Savings and Environmental Impact. Renewable and Sustainable Energy Reviews, 13 [6-7] 1262-1274.
  • Aydin, T., Bican, O., & Gümrük, R. (2020). Investigation of wear resistance of the porcelain tile bodies by solid particle impingement using alumina particles. Journal of the Australian Ceramic Society, 56, 525-531.
  • Basavaraju, A., Du, J., Zhou, F., & Ji, J. (2019). A machine learning approach to road surface anomaly assessment using smartphone sensors. IEEE Sensors Journal, 20(5), 2635-2647.
  • Baudín, C., Tricoteaux, A., & Joire, H. (2014). Improved resistance of alumina to mild wear by aluminium titanate additions. Journal of the European Ceramic Society, 34(1), 69-80.
  • Bernard, J. T., Idoudi, N., Khalaf, L., & Yélou, C. (2007). Finite sample multivariate structural change tests with application to energy demand models. Journal of Econometrics, 141(2), 1219-1244.
  • Binder, J. J., 1985. On the use of the multivariate regression model in event studies. Journal of Accounting Research, 370-383.
  • Boyraz, T. and Akkuş, A. 2021. Investigation of wear properties of mullite and aluminium titanate added porcelain ceramics, Journal of Ceramic Processing Research, 22(2), 226-231.
  • Breiman, L., 2001. Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author), Stat. Sci., 16/3.
  • Buckley, D. H. and Miyoshi, K., 1984. Friction and wear of ceramics, Wear, 100[1–3], 333-353.
  • Bueno, S., Micele, L., Melandri, C., Baudin, C., & De Portu, G., 2011. Improved wear behaviour of alumina–aluminium titanate laminates with low residual stresses and large grained interfaces. Journal of the European Ceramic Society, 31(4), 475-483.
  • Catalina, T., Virgone, J., & Blanco, E., 2008. Development and validation of regression models to predict monthly heating demand for residential buildings. Energy and buildings, 40(10), 1825-1832.
  • Demirbuğan, M. A., 2006. Konut Sektörü İçin Linyit Kömürü ‘Tüketici Fazlası”, Madencilik Dergisi, 45, 29-40.
  • Doğan, T. and Beyza, V., 2009. Türkiye Birincil Enerji Kaynaklarının Piyasasının Zaman Serileri İle İstatistiksel Analizi, Marmara Üniversitesi, Yüksek Lisans Tezi, İstanbul.
  • Fausett, L. V., 1994. Fundamentals of neural networks: architectures, algorithms, and applications. Englewood Cliffs, NJ: Prentice-Hall.
  • McCulloch, W. S., & Pitts, W., 1943. A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 5, 115-133.
  • Guresen E., and Kayakutlu, G., 2011. Definition of artificial neural networks with comparison to other networks, Procedia Comput. Sci., 3, 426-433.
  • Gurney, K., 2018. An Introduction to Neural Networks, CRC Press.
  • Hassan, A. M., Alrashdan, A., Hayajneh, M. T., & Mayyas, A. T., 2009. Prediction of density, porosity and hardness in aluminum–copper-based composite materials using artificial neural network. Journal of materials processing technology, 209(2), 894-899.
  • Haykin, S., 1994. Neural Networks: A Comprehensive Foundation. Macmillan Publishing, New York.
  • Huang, C. Z., Zhang, L., He, L., Sun, J., Fang, B., Zou, B., ... & Ai, X., 2002. A study on the prediction of the mechanical properties of a ceramic tool based on an artificial neural network. Journal of materials processing technology, 129(1-3), 399-402.
  • Iqbal, Y. and Lee, E.J., 2000. ‘’Microstructural evolution in triaxial porcelain’’, J. Am. Ceram. Soc. 83, 3121–3127.
  • Karaca, C. and Karacan, H., 2016. Çoklu Regresyon Metoduyla Elektrik Tüketim Talebini Etkileyen Faktörlerin İncelenmesi, Selçuk Üniversitesi Mühendislik Fakültesi Bilim ve Teknoloji Dergisi. 4, 183-194.
  • Kelleher, J. D., Mac Namee, B., & D'Arcy, A., 2015. Fundamentals of machine learning for predictive data analytics: algorithms. Worked examples, and case studies.
  • Kitouni, S. and Harabi, A., 2011. Sintering and mechanical properties of porcelains prepared from algerian raw materials, Cerâmica, 57, 453-460.
  • Kong, Y., Yang, Z., Zhang, G., & Yuan, Q., 1998. Friction and wear characteristics of mullite, ZTM and TZP ceramics. Wear, 218(2), 159-166.
  • Kubat, M., 1999. Neural networks: a comprehensive foundation by Simon Haykin, Macmillan, 1994, ISBN 0-02-352781-7., Knowl. Eng. Rev., 13, 409-412.
  • Lopez, S. Y. R., Rodriguez, J. S., & Sueyoshi, S. S., 2011. Determination of the activation energy for densification of porcelain stoneware. Journal of Ceramic Processing Research, 12(3), 228-232.
  • Luo, H. H., Zhang, F. C., & Roberts, S. G., 2008. Wear resistance of reaction sintered alumina/mullite composites. Materials Science and Engineering: A, 478(1-2), 270-275.
  • Madhiarasan, M. and Louzazni M., 2022. Analysis of Artificial Neural Network: Architecture, Types, and Forecasting Applications, Journal of Electrical and Computer Engineering, V.2022, ID 5416722.
  • Martín‐Márquez, J., De la Torre, A. G., Aranda, M. A., Rincón, J. M., & Romero, M., 2009. Evolution with temperature of crystalline and amorphous phases in porcelain stoneware. Journal of the American Ceramic Society, 92(1), 229-234.
  • Martín-Márquez, J., Rincón, J. M., & Romero, M., 2008. Effect of firing temperature on sintering of porcelain stoneware tiles. Ceramics International, 34(8), 1867-1873.
  • Özkan, G., 2016. Düzenleyici Reformların Türkiye Enerji Piyasası Üzerindeki Ekonomik Etkileri, Kırıkkale Üniversitesi, Yüksek Lisans Tezi, Kırıkkale.
  • Öztürk, Ç., Akpınar, S., & Tığ, M., 2022. Effect of calcined colemanite addition on properties of porcelain tile. Journal of the Australian Ceramic Society, 58(1), 321-331.
  • Pramod, R., Kumar, G. V., Gouda, P. S., & Mathew, A. T., 2018. A study on the Al2O3 reinforced Al7075 metal matrix composites wear behavior using artificial neural networks. Materials Today: Proceedings, 5(5), 11376-11385.
  • Ramanathan, A., Pullum, L. L., Hussain, F., Chakrabarty, D., & Jha, S. K., 2016. Integrating symbolic and statistical methods for testing intelligent systems: Applications to machine learning and computer vision. In 2016 Design, Automation & Test in Europe Conference & Exhibition (DATE) (pp. 786-791). IEEE.
  • Sacli, M., Onen, U. M. U. T., & Boyraz, T. A. H. S. İ. N., 2015. Microstructural characterization and thermal properties of aluminium titanate/porcelain ceramic matrix composites. Acta Physica Polonica A, 127(4).
  • Serragdj, I., Harabi, A., Kasrani, S., Foughali, L., & Karboua, N., 2019. Effect of ZrO 2 additions on densification and mechanical properties of modified resistant porcelains using economic raw materials. Journal of the Australian Ceramic Society, 55, 489-499.
  • Shekhawat, D., Agarwal, P., Singh, A., & Patnaik, A., 2022. Prediction of thermal and thermo‐mechanical behavior of nano‐zirconia reinforced aluminium matrix composites. Materialwissenschaft und Werkstofftechnik, 53(10), 1216-1228.
  • Sykes A. O., 1993. An Introduction to Regression Analysis, Coase-Sandor Institute for Law & Economics Working Paper No: 20, 1 – 33.
  • Taktak, S. and Baspinar, M.S., 2005. Wear and friction behaviour of alumina/mullite composite by sol–gel infiltration technique, Materials and Design, 26, 459–464.
  • Turkmen, O., Kucuk, A., & Akpinar, S., 2015. Effect of wollastonite addition on sintering of hard porcelain. Ceramics International, 41(4), 5505-5512.
  • Yu, H. Y., Cai, Z. B., Ren, P. D., Zhu, M. H., & Zhou, Z. R., 2006. Friction and wear behavior of dental feldspathic porcelain. Wear, 261(5-6), 611-621.
  • Yuksek, A. G., Tuzemen, E. S., & Elagoz, S., 2015. Modeling of reflectance properties of ZnO film using artificial neural networks. Journal of Optoelectronics and Advanced Materials, 17(11-12), 1615-1628.
  • Yumurtacı, Z. and Asmaz, E., 2004. Electric Energy Demand of Turkey for the Year 2050, Energy Sources, XXVI, 12, 1157-1164.
  • Yüzük, F., 2019. Çoklu Regresyon Analizi Ve Yapay Sinir Ağlari İle Türkiye Enerji Talep Tahmini, YL tezi, Sivas Cumhuriyet Üniversitesi, Sosyal Bilimler Enstitüsü, İşletme Ana Bilim Dalı Başkanlığı
Toplam 48 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yazılım Mühendisliği (Diğer), Malzeme Mühendisliği (Diğer)
Bölüm Araştırma Makaleleri
Yazarlar

Ahmet Gürkan Yüksek 0000-0001-7709-6360

Tahsin Boyraz 0000-0003-4404-6388

Ahmet Akkuş 0000-0002-6881-9333

Erken Görünüm Tarihi 19 Şubat 2024
Yayımlanma Tarihi 14 Şubat 2024
Gönderilme Tarihi 19 Aralık 2023
Kabul Tarihi 4 Ocak 2024
Yayımlandığı Sayı Yıl 2023Cilt: 2 Sayı: 2

Kaynak Göster

APA Yüksek, A. G., Boyraz, T., & Akkuş, A. (2024). Regression Modelling to Study Wear Properties of Experimental Produced Porcelain Ceramics. Teknik Meslek Yüksekokulları Akademik Araştırma Dergisi, 2(2), 25-31.