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Year 2024, Volume: 8 Issue: 4, 640 - 646, 31.10.2024
https://doi.org/10.31127/tuje.1454237

Abstract

References

  • Ramani, J., Dandge, S., & Chakraborty, S. (2020, November). Machinability study of plain carbon steels using data mining technique. In AIP Conference Proceedings (Vol. 2273, No. 1). AIP Publishing. https://doi.org/10.1063/5.0024334
  • Uzorh, A. C. (2013). Corrosion properties of plain carbon steels. The International Journal of Engineering and Science, 2(11), 18-24.
  • Couper, M. J., Neeson, A. E., & Griffiths, J. R. (1990). Casting defects and the fatigue behaviour of an aluminium casting alloy. Fatigue & Fracture of Engineering Materials & Structures, 13(3), 213-227. https://doi.org/10.1111/j.1460-695.1990.tb00594.x
  • Wang, Q. G., Apelian, D., & Lados, D. A. (2001). Fatigue behavior of A356-T6 aluminum cast alloys. Part I. Effect of casting defects. Journal of Light Metals, 1(1), 73-84. https://doi.org/10.1016/S1471-5317(00)00008-0
  • Hardin, R. A., & Beckermann, C. (2013). Effect of porosity on deformation, damage, and fracture of cast steel. Metallurgical and Materials Transactions A, 44, 5316-5332. https://doi.org/10.1007/s11661-013-1669-z
  • Xu, Z., Wang, X., & Jiang, M. (2017). Investigation on improvement of center porosity with heavy reduction in continuously cast thick slabs. Steel Research International, 88(2), 1600061. https://doi.org/10.1002/srin.201600061
  • Yao, R. Q., & Tang, H. Q. (2011). The numerical simulation and optimization of squeeze casting process for producing magnesium wheels. Advanced Materials Research, 299, 955-961. https://doi.org/10.4028/www.scientific.net/AMR.299-300.955
  • Kuang, W., Wang, H., Li, X., Zhang, J., Zhou, Q., & Zhao, Y. (2018). Application of the thermodynamic extremal principle to diffusion-controlled phase transformations in Fe-CX alloys: Modeling and applications. Acta Materialia, 159, 16-30. https://doi.org/10.1016/j.actamat.2018.08.008
  • Chen, L., Zhao, Y., Yan, F., & Hou, H. (2017). Statistical investigations of serpentine channel pouring process parameters on semi-solid ZL101 aluminum alloy slurry using response surface methodology. Journal of Alloys and Compounds, 725, 673-683. https://doi.org/10.1016/j.jallcom.2017.07.169
  • Zhao, P., Dong, Z., Zhang, J., Zhang, Y., Cao, M., Zhu, Z., ... & Fu, J. (2020). Optimization of injection-molding process parameters for weight control: converting optimization problem to classification problem. Advances in Polymer Technology, 2020, 1-9. https://doi.org/10.1155/2020/7654249
  • Cao, L., Liao, D., Sun, F., Chen, T., Teng, Z., & Tang, Y. (2018). Prediction of gas entrapment defects during zinc alloy high-pressure die casting based on gas-liquid multiphase flow model. The International Journal of Advanced Manufacturing Technology, 94, 807-815. https://doi.org/10.1007/s00170-017-0926-5
  • Shafyei, A., Anijdan, S. M., & Bahrami, A. (2006). Prediction of porosity percent in Al–Si casting alloys using ANN. Materials Science and Engineering: A, 431(1-2), 206-210. https://doi.org/10.1016/j.msea.2006.05.150
  • Kumruoglu, L. C., & Özer, A. (2008). Investigation of critical liquid fraction factor in nodular iron castings by computer simulation. Journal of Materials Processing Technology, 197(1-3), 182-188. https://doi.org/10.1016/j.jmatprotec.2007.06.008
  • Rathod, H., Dhulia, J. K., & Maniar, N. P. (2017, August). Prediction of shrinkage porosity defect in sand casting process of LM25. In IOP Conference Series: Materials Science and Engineering (Vol. 225, No. 1, p. 012237). IOP Publishing. https://doi.org/10.1088/1757-899X/225/1/012237
  • Chen, Z., Li, Y., Zhao, F., Li, S., & Zhang, J. (2022). Progress in numerical simulation of casting process. Measurement and Control, 55(5-6), 257-264. https://doi.org/10.1177/00202940221102656
  • Cemernek, D., Cemernek, S., Gursch, H., Pandeshwar, A., Leitner, T., Berger, M., ... & Kern, R. (2021). Machine learning in continuous casting of steel: A state-of-the-art survey. Journal of Intelligent Manufacturing, 1-19. https://doi.org/10.1007/s10845-021-01754-7
  • Kubo, K., & Pehlke, R. D. (1985). Mathematical modeling of porosity formation in solidification. Metallurgical Transactions B, 16, 359-366. https://doi.org/10.1007/BF02679728
  • Saleh, S. M., & İhsan, A. A. (2021). Strength and behaviour assessment of axially loaded concrete filled steel tubular stub columns. Turkish Journal of Engineering, 5(4), 154-164. https://doi.org/10.31127/tuje.686246
  • Chen, Y. H., & Im, Y. T. (1990). Analysis of solidification in sand and permanent mold castings and shrinkage prediction. International Journal of Machine Tools and Manufacture, 30(2), 175-189. https://doi.org/10.1016/0890-6955(90)90128-6
  • Inegbedion, F., & James, O. R. J. I. (2023). Determination of the critical drop height and critical flow velocity of aluminum alloy (AL-91% Mg-8% Fe-0.4% Zn-0.2%) in gravity sand casting. Turkish Journal of Engineering, 7(2), 149-156. https://doi.org/10.31127/tuje.1077467
  • Asan, Y. E., & Çolak, M. (2022). Modeling the effect of pour height, casting temperature and die preheating temperature on the fluidity of different section thicknesses in permanent mold casting of Al12Si alloys. Erzincan University Journal of Science and Technology, 15(Special Issue I), 14-27. https://doi.org/10.18185/erzifbed.1199648
  • Sabau, A. S., & Viswanathan, S. (2002). Microporosity prediction in aluminum alloy castings. Metallurgical and Materials Transactions B, 33, 243-255. https://doi.org/10.1007/s11663-002-0009-2
  • Pequet, C., Rappaz, M., & Gremaud, M. (2002). Modeling of microporosity, macroporosity, and pipe-shrinkage formation during the solidification of alloys using a mushy-zone refinement method: Applications to aluminum alloys. Metallurgical and Materials Transactions A, 33, 2095-2106. https://doi.org/10.1007/s11661-002-0041-5
  • Sun, D., & Garimella, S. V. (2007). Numerical and experimental investigation of solidification shrinkage. Numerical Heat Transfer, Part A: Applications, 52(2), 145-162. https://doi.org/10.1080/10407780601115079
  • de Obaldia, E. E., & Felicelli, S. D. (2007). Quantitative prediction of microporosity in aluminum alloys. Journal of Materials Processing Technology, 191(1-3), 265-269. https://doi.org/10.1016/j.jmatprotec.2007.03.072
  • Reis, A., Houbaert, Y., Xu, Z., Van Tol, R., Santos, A. D., Duarte, J. F., & Magalhaes, A. B. (2008). Modeling of shrinkage defects during solidification of long and short freezing materials. Journal of Materials Processing Technology, 202(1-3), 428-434. https://doi.org/10.1016/j.jmatprotec.2007.10.030
  • Tavakoli, R. (2014). On the prediction of shrinkage defects by thermal criterion functions. The International Journal of Advanced Manufacturing Technology, 74, 569-579. https://doi.org/10.1007/s00170-014-5995-0
  • Sutaria, M. (2013). Casting solidification feed-paths: Modeling, computation and applications (Doctoral thesis). Indian Institute of Technology Bombay.
  • ASM. (1962). Casting design handbook. American Society of Metals.
  • Noda, N. A., Egawa, S., Tashiro, Y., & Takenouchi, K. (2009). Predicting locations of defects in the solidification process for large-scale cast steel. Journal of Computational Science and Technology, 3(1), 242-251. https://doi.org/10.1299/JCST.3.242
  • Joshi, D., & Ravi, B. (2009). Classification and simulation-based design of 3D junctions in castings. AFS Transactions, 117, 7-22.
  • Kabnure, B. B., Shinde, V. D., & Patil, D. C. (2020). Quality and yield improvement of ductile iron casting by simulation technique. Materials Today: Proceedings, 27, 111-116. https://doi.org/10.1016/j.matpr.2019.09.022

Development of geometry-driven quantitative prediction for shrinkage porosity in T-junction of steel sand castings

Year 2024, Volume: 8 Issue: 4, 640 - 646, 31.10.2024
https://doi.org/10.31127/tuje.1454237

Abstract

Shrinkage porosity poses a significant challenge in metal casting processes, impacting both productivity and energy efficiency, especially when dealing with components that are not accepted or reprocessed. Addressing this issue requires proactive measures, and predictive techniques play a crucial role in minimizing its occurrence. Among these methods, the Criterion Function stands out as a valuable empirical model extensively explored in the literature. By intricately linking solidification processes to the development of shrinkage porosity, the Criterion Function leverages key process parameters, including thermal gradient, molten metal velocity during solidification, and cooling rate, to offer predictive insights into the location and presence of porosity. However, a criterion function is needed that also considers the effect of geometric variations as well as the size of the defect (shrinkage porosity). In this study, a casting with three T-joints was taken as a benchmark shape to develop a geometry-based quantitative prediction model for plain carbon steel castings. Real experimental results were combined with solidification simulation results to produce reliable data, which were then used to extrapolate the results. The developed quantitative prediction model, which includes the effect of geometric changes, has been validated and proven effective in predicting shrinkage porosity.

References

  • Ramani, J., Dandge, S., & Chakraborty, S. (2020, November). Machinability study of plain carbon steels using data mining technique. In AIP Conference Proceedings (Vol. 2273, No. 1). AIP Publishing. https://doi.org/10.1063/5.0024334
  • Uzorh, A. C. (2013). Corrosion properties of plain carbon steels. The International Journal of Engineering and Science, 2(11), 18-24.
  • Couper, M. J., Neeson, A. E., & Griffiths, J. R. (1990). Casting defects and the fatigue behaviour of an aluminium casting alloy. Fatigue & Fracture of Engineering Materials & Structures, 13(3), 213-227. https://doi.org/10.1111/j.1460-695.1990.tb00594.x
  • Wang, Q. G., Apelian, D., & Lados, D. A. (2001). Fatigue behavior of A356-T6 aluminum cast alloys. Part I. Effect of casting defects. Journal of Light Metals, 1(1), 73-84. https://doi.org/10.1016/S1471-5317(00)00008-0
  • Hardin, R. A., & Beckermann, C. (2013). Effect of porosity on deformation, damage, and fracture of cast steel. Metallurgical and Materials Transactions A, 44, 5316-5332. https://doi.org/10.1007/s11661-013-1669-z
  • Xu, Z., Wang, X., & Jiang, M. (2017). Investigation on improvement of center porosity with heavy reduction in continuously cast thick slabs. Steel Research International, 88(2), 1600061. https://doi.org/10.1002/srin.201600061
  • Yao, R. Q., & Tang, H. Q. (2011). The numerical simulation and optimization of squeeze casting process for producing magnesium wheels. Advanced Materials Research, 299, 955-961. https://doi.org/10.4028/www.scientific.net/AMR.299-300.955
  • Kuang, W., Wang, H., Li, X., Zhang, J., Zhou, Q., & Zhao, Y. (2018). Application of the thermodynamic extremal principle to diffusion-controlled phase transformations in Fe-CX alloys: Modeling and applications. Acta Materialia, 159, 16-30. https://doi.org/10.1016/j.actamat.2018.08.008
  • Chen, L., Zhao, Y., Yan, F., & Hou, H. (2017). Statistical investigations of serpentine channel pouring process parameters on semi-solid ZL101 aluminum alloy slurry using response surface methodology. Journal of Alloys and Compounds, 725, 673-683. https://doi.org/10.1016/j.jallcom.2017.07.169
  • Zhao, P., Dong, Z., Zhang, J., Zhang, Y., Cao, M., Zhu, Z., ... & Fu, J. (2020). Optimization of injection-molding process parameters for weight control: converting optimization problem to classification problem. Advances in Polymer Technology, 2020, 1-9. https://doi.org/10.1155/2020/7654249
  • Cao, L., Liao, D., Sun, F., Chen, T., Teng, Z., & Tang, Y. (2018). Prediction of gas entrapment defects during zinc alloy high-pressure die casting based on gas-liquid multiphase flow model. The International Journal of Advanced Manufacturing Technology, 94, 807-815. https://doi.org/10.1007/s00170-017-0926-5
  • Shafyei, A., Anijdan, S. M., & Bahrami, A. (2006). Prediction of porosity percent in Al–Si casting alloys using ANN. Materials Science and Engineering: A, 431(1-2), 206-210. https://doi.org/10.1016/j.msea.2006.05.150
  • Kumruoglu, L. C., & Özer, A. (2008). Investigation of critical liquid fraction factor in nodular iron castings by computer simulation. Journal of Materials Processing Technology, 197(1-3), 182-188. https://doi.org/10.1016/j.jmatprotec.2007.06.008
  • Rathod, H., Dhulia, J. K., & Maniar, N. P. (2017, August). Prediction of shrinkage porosity defect in sand casting process of LM25. In IOP Conference Series: Materials Science and Engineering (Vol. 225, No. 1, p. 012237). IOP Publishing. https://doi.org/10.1088/1757-899X/225/1/012237
  • Chen, Z., Li, Y., Zhao, F., Li, S., & Zhang, J. (2022). Progress in numerical simulation of casting process. Measurement and Control, 55(5-6), 257-264. https://doi.org/10.1177/00202940221102656
  • Cemernek, D., Cemernek, S., Gursch, H., Pandeshwar, A., Leitner, T., Berger, M., ... & Kern, R. (2021). Machine learning in continuous casting of steel: A state-of-the-art survey. Journal of Intelligent Manufacturing, 1-19. https://doi.org/10.1007/s10845-021-01754-7
  • Kubo, K., & Pehlke, R. D. (1985). Mathematical modeling of porosity formation in solidification. Metallurgical Transactions B, 16, 359-366. https://doi.org/10.1007/BF02679728
  • Saleh, S. M., & İhsan, A. A. (2021). Strength and behaviour assessment of axially loaded concrete filled steel tubular stub columns. Turkish Journal of Engineering, 5(4), 154-164. https://doi.org/10.31127/tuje.686246
  • Chen, Y. H., & Im, Y. T. (1990). Analysis of solidification in sand and permanent mold castings and shrinkage prediction. International Journal of Machine Tools and Manufacture, 30(2), 175-189. https://doi.org/10.1016/0890-6955(90)90128-6
  • Inegbedion, F., & James, O. R. J. I. (2023). Determination of the critical drop height and critical flow velocity of aluminum alloy (AL-91% Mg-8% Fe-0.4% Zn-0.2%) in gravity sand casting. Turkish Journal of Engineering, 7(2), 149-156. https://doi.org/10.31127/tuje.1077467
  • Asan, Y. E., & Çolak, M. (2022). Modeling the effect of pour height, casting temperature and die preheating temperature on the fluidity of different section thicknesses in permanent mold casting of Al12Si alloys. Erzincan University Journal of Science and Technology, 15(Special Issue I), 14-27. https://doi.org/10.18185/erzifbed.1199648
  • Sabau, A. S., & Viswanathan, S. (2002). Microporosity prediction in aluminum alloy castings. Metallurgical and Materials Transactions B, 33, 243-255. https://doi.org/10.1007/s11663-002-0009-2
  • Pequet, C., Rappaz, M., & Gremaud, M. (2002). Modeling of microporosity, macroporosity, and pipe-shrinkage formation during the solidification of alloys using a mushy-zone refinement method: Applications to aluminum alloys. Metallurgical and Materials Transactions A, 33, 2095-2106. https://doi.org/10.1007/s11661-002-0041-5
  • Sun, D., & Garimella, S. V. (2007). Numerical and experimental investigation of solidification shrinkage. Numerical Heat Transfer, Part A: Applications, 52(2), 145-162. https://doi.org/10.1080/10407780601115079
  • de Obaldia, E. E., & Felicelli, S. D. (2007). Quantitative prediction of microporosity in aluminum alloys. Journal of Materials Processing Technology, 191(1-3), 265-269. https://doi.org/10.1016/j.jmatprotec.2007.03.072
  • Reis, A., Houbaert, Y., Xu, Z., Van Tol, R., Santos, A. D., Duarte, J. F., & Magalhaes, A. B. (2008). Modeling of shrinkage defects during solidification of long and short freezing materials. Journal of Materials Processing Technology, 202(1-3), 428-434. https://doi.org/10.1016/j.jmatprotec.2007.10.030
  • Tavakoli, R. (2014). On the prediction of shrinkage defects by thermal criterion functions. The International Journal of Advanced Manufacturing Technology, 74, 569-579. https://doi.org/10.1007/s00170-014-5995-0
  • Sutaria, M. (2013). Casting solidification feed-paths: Modeling, computation and applications (Doctoral thesis). Indian Institute of Technology Bombay.
  • ASM. (1962). Casting design handbook. American Society of Metals.
  • Noda, N. A., Egawa, S., Tashiro, Y., & Takenouchi, K. (2009). Predicting locations of defects in the solidification process for large-scale cast steel. Journal of Computational Science and Technology, 3(1), 242-251. https://doi.org/10.1299/JCST.3.242
  • Joshi, D., & Ravi, B. (2009). Classification and simulation-based design of 3D junctions in castings. AFS Transactions, 117, 7-22.
  • Kabnure, B. B., Shinde, V. D., & Patil, D. C. (2020). Quality and yield improvement of ductile iron casting by simulation technique. Materials Today: Proceedings, 27, 111-116. https://doi.org/10.1016/j.matpr.2019.09.022
There are 32 citations in total.

Details

Primary Language English
Subjects Experimental Methods in Fluid Flow, Heat and Mass Transfer, Computational Methods in Fluid Flow, Heat and Mass Transfer (Incl. Computational Fluid Dynamics)
Journal Section Articles
Authors

Kamar Mazloum 0000-0002-3974-1696

Amit Sata 0000-0002-0945-3095

Early Pub Date October 28, 2024
Publication Date October 31, 2024
Submission Date March 17, 2024
Acceptance Date April 17, 2024
Published in Issue Year 2024 Volume: 8 Issue: 4

Cite

APA Mazloum, K., & Sata, A. (2024). Development of geometry-driven quantitative prediction for shrinkage porosity in T-junction of steel sand castings. Turkish Journal of Engineering, 8(4), 640-646. https://doi.org/10.31127/tuje.1454237
AMA Mazloum K, Sata A. Development of geometry-driven quantitative prediction for shrinkage porosity in T-junction of steel sand castings. TUJE. October 2024;8(4):640-646. doi:10.31127/tuje.1454237
Chicago Mazloum, Kamar, and Amit Sata. “Development of Geometry-Driven Quantitative Prediction for Shrinkage Porosity in T-Junction of Steel Sand Castings”. Turkish Journal of Engineering 8, no. 4 (October 2024): 640-46. https://doi.org/10.31127/tuje.1454237.
EndNote Mazloum K, Sata A (October 1, 2024) Development of geometry-driven quantitative prediction for shrinkage porosity in T-junction of steel sand castings. Turkish Journal of Engineering 8 4 640–646.
IEEE K. Mazloum and A. Sata, “Development of geometry-driven quantitative prediction for shrinkage porosity in T-junction of steel sand castings”, TUJE, vol. 8, no. 4, pp. 640–646, 2024, doi: 10.31127/tuje.1454237.
ISNAD Mazloum, Kamar - Sata, Amit. “Development of Geometry-Driven Quantitative Prediction for Shrinkage Porosity in T-Junction of Steel Sand Castings”. Turkish Journal of Engineering 8/4 (October 2024), 640-646. https://doi.org/10.31127/tuje.1454237.
JAMA Mazloum K, Sata A. Development of geometry-driven quantitative prediction for shrinkage porosity in T-junction of steel sand castings. TUJE. 2024;8:640–646.
MLA Mazloum, Kamar and Amit Sata. “Development of Geometry-Driven Quantitative Prediction for Shrinkage Porosity in T-Junction of Steel Sand Castings”. Turkish Journal of Engineering, vol. 8, no. 4, 2024, pp. 640-6, doi:10.31127/tuje.1454237.
Vancouver Mazloum K, Sata A. Development of geometry-driven quantitative prediction for shrinkage porosity in T-junction of steel sand castings. TUJE. 2024;8(4):640-6.
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