Parametric Analyses on Compressive Strength of Furan No Bake Mould System Using ANN

• Autorzy: Acharya S. G. , Vadher J. A.
• Języki publikacji: EN

Abstrakty

EN

Casting is the most widely used manufacturing technique. Furan No-bake mould system is very widely accepted in competitive foundry industries due to its excellent characteristics of producing heavy and extremely difficult castings. These castings have excellent surface finish and high dimensional stability. Self setting and high dimensional stability are the key characteristics of FNB mould system which leads to reduce production cycle time for foundry industries which will ultimately save machining cost, labour cost and energy. Compressive strength is the main aspect of furan no bake mould, which can be improved by analyzing the effect of various parameters on it. ANN is a useful technique for determining the relation of various parameters like Grain Fineness Number, Loss on Ignition, pH, % resin and temperature of sand with compressive strength of the FNB mould. Matlab version: R2015a version 8.3 software with ANN tool box can be used to gain output of relation. This paper deals with the representation of relationship of various parameters affecting on the compressive strength of FNB mould.

Słowa kluczowe

EN

ANN method; compressive strength; FNB mould; Matlab version; R2015a version 8.3

PL

metoda ANN; wytrzymałość na ściskanie; forma FNB; Matlab

• Wydawca: Komisja Odlewnictwa Polskiej Akademii Nauk Oddział w Katowicach
• Czasopismo: Archives of Foundry Engineering
• Rocznik: 2016
• Tom: Vol. 16, iss. 4
• Strony: 5--10
• Opis fizyczny: Bibliogr. 12 poz., rys., tab., wykr.

Twórcy

autor

Acharya S. G.

• School of Engineering, R.K.University, Rajkot, INDIA; Mechanical Engineering Department, SVIT, Vasad, INDIA

autor

Vadher J. A.

• Mechanical Engineering Department, Government Engineering College, Palanpur, INDIA

Bibliografia

• [1] Acharya, S.G., Vadher, J.A. (2015). Experimental investigations on modern furan no bake system to obtain quality casting, Researchgate, International Conference on Advances in Materials and Product Science, January.
• [2] Patil, G.G. & Inamdar, K.H. (2014). Prediction of casting defects through Artificial Neural Network. International Journal of Science, Engineering And Technology. 2(5), 245-253.
• [3] Surekha, B., Hanumanth Rao, D,. Krishnamohan Rao, G. & P.R Vundavilli, (2013). Application of Response Surface Methodology for modeling the properties of Chromite-based resin bonded sand cores. International Journal of Mechanics. 7(4), 443-458.
• [4] Zych, J. (2013). Bench Life of Moulding and core sands with chemical binders- A new Ultrasonic Investigation Method. Archives of Foundry Engineering. 13(4), 117-122.
• [5] Qing, J., Lekakh, S. & Richards, V. (2013). No-bake S-containing Mold- DI metal interactions: Consequences and Potential application. American Foundry Society. 13(1320).
• [6] Ghosh, D. (2013). Modern furan for modern castings, Indian Foundry Congress.
• [7] LaFay, V. (2012). Application of No-Bake sodium silicate binder systems. American Foundry Society. International Journal of Metalcasting. 2012(Summer), 19-26.
• [8] Saikaew, C. Weingwiset, S. (2012). Optimization of molding sand composition for quality improvement of iron castings. Applied Clay Science, Elsevier.
• [9] Gresovnik, I., Kodelja, T., Vertnik, R., Sarler, B. (2012). Application of artificial neural networks to improve steel production process. Proceedings of the International Conference Artificial Intelligence and Soft Computing (pp.249–255).
• [10] Hosadyna, M., Dobosz, St.M. & Major-Gabrys, K. (2011). Influence of the hardener type on the sulphur diffusion from the moulding sand to the casting surface. Archives of Foundry Engineering. 11(2), 47-50.
• [11] Htofuji, H. Tamura, M. & Ito, H. (2010). Production of the 7-Ton Nonmagnetic Ductile iron Casting for largest class Power generator. Material transactions. 51, 103-109.
• [12] Basheera, I.A. & Hajmeer, M. (2000). Artificial neural networks: fundamentals computing, design, and application. Journal of Microbiological Methods. 43, 3-31.

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.