Tensile Strength Measurement for Foundry Sand Brick

  • Manasi V. Magdum
  • Dr. B.T. Salokhe
  • Prof. A.S. Mali
Keywords: Hooke’s Law, Tensile Strength, Bayesian Network

Abstract

Tensile strength is an important concept in engineering, especially in the fields of material science, mechanical engineering and structural Engineering. The tensile strength of a material is the maximum amount of tensile stress that can be applied to it before it ceases to be elastic. If more force is applied the material will become plastic or even break. Passed the elastic limit, the material will not relax to its initial shape after the force is removed. See Hooke's Law and Modulus of elasticity. The tensile strength where the material becomes plastic is called yield tensile strength. This is the point where the deformation (strain) of the material is unrecovered, and the work produced by external forces is not stored as elastic energy but will lead to contraction, cracks and ultimately failure of the construction. Clearly, this is a remarkable point for the engineering properties of the material since here the construction may lose its loading capacity or undergo large deformations. On the stress-strain curve below this point is in between the elastic and the plastic region. The Ultimate Tensile Strength (UTS) of a material is the limit stress at which the material actually breaks, with sudden release of the stored elastic energy. Tensile strength is measured in units of force per unit area. In the SI system, the unit is Newton per square meter (N/m² or Pa - Pascal). The U.S customary unit is pounds per square inch (or PSI).

Downloads

Download data is not yet available.

References

J. Nieves, I. Santos, Y. K. Penya, S. Rojas, M. Salazar, & P.G. Bringas. (2009). Mechanical properties prediction in high-precision foundry production. In Proceedings de la 7th IEEE International Conference on Industrial Informatics (INDIN 09), 31–36.

H. Sarimveis & G. Bafas. (2003). Fuzzy model predictive control of nonlinear processes using genetic algorithms. Fuzzy sets and systems, 139(1), 59–80, 2003.

H. Al-Duwaish & W. Naeem. (2001). Nonlinear model predictive control of hammerstein and wiener models using genetic algorithms. Control Applications, 2001.(CCA’01). In Proceedings of the 2001 IEEE International Conference, 465–469.

M. Tomassini. (1995). A survey of genetic algorithms. Annual Reviews of Computational Physics, 3(2), 87–118.

R. Kohavi. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In International Joint Conference on Artificial Intelligence, 14, 1137–1145.

Gregory F. Cooper & Edward Herskovits. (1991). A bayesian method for constructing Bayesian belief networks from databases. In Proceedings of the 7th Conference on Uncertainty in Artificial Intelligence, 86-94.

H.Q. Peter & J. Wang. (2007). Fault detection using the k-nearest neighbor rule for semiconductor manufacturing processes. IEEE Transactions on Semiconductor Manufacturing, 20(4), 345-354.

R.O. Duda, P.E. Hart, & D.G. Stork. (2001). Pattern classification. (2nd Edition). New York: Wiley.

J. Yang, M. Liu, & C. Wu. (2003). Genetic algorithm based nonlinear model predictive control method. Control and Decision, 18(2), 141–144.

Nuhu A. Ademoh. (2010). Evaluation of the tensile strength of foundry cores made with hybridized binder composed of Neem oil and Nigerian gum Arabic. International Journal of the Physical Sciences, 5(5), 557-563.

Published
2018-08-31
How to Cite
Manasi V. Magdum, Dr. B.T. Salokhe, & Prof. A.S. Mali. (2018). Tensile Strength Measurement for Foundry Sand Brick. International Journal of Engineering and Management Research, 8(4), 40-42. https://doi.org/10.31033/ijemr.8.4.3