Prediction of Secondary Dendrite Arm Spacing in Squeeze Casting Using Fuzzy Logic Based Approaches

• Autorzy: Patel M. G. C. , Krishna P. , Parappagoudar M. B.

Identyfikatory

DOI

10.1515/afe-2015-0011

• Języki publikacji: EN

Abstrakty

EN

The quality of the squeeze castings is significantly affected by secondary dendrite arm spacing, which is influenced by squeeze cast input parameters. The relationships of secondary dendrite arm spacing with the input parameters, namely time delay, pressure duration, squeeze pressure, pouring and die temperatures are complex in nature. The present research work focuses on the development of input-output relationships using fuzzy logic approach. In fuzzy logic approach, squeeze cast process variables are expressed as a function of input parameters and secondary dendrite arm spacing is expressed as an output parameter. It is important to note that two fuzzy logic based approaches have been developed for the said problem. The first approach deals with the manually constructed mamdani based fuzzy system and the second approach deals with automatic evolution of the Takagi and Sugeno’s fuzzy system. It is important to note that the performance of the developed models is tested for both linear and non-linear type membership functions. In addition the developed models were compared with the ten test cases which are different from those of training data. The developed fuzzy systems eliminates the need of a number of trials in selection of most influential squeeze cast process parameters. This will reduce time and cost of trial experimentations. The results showed that, all the developed models can be effectively used for making prediction. Further, the present research work will help foundrymen to select parameters in squeeze casting to obtain the desired quality casting without much of time and resource consuming.

Słowa kluczowe

EN

squeeze casting process; secondary dendrite arm spacing; fuzzy logic; adaptive network based fuzzy; interface system

PL

prasowanie stopu; odległość ramion dendrytów; logika rozmyta; system interfejsowy

• Wydawca: Komisja Odlewnictwa Polskiej Akademii Nauk Oddział w Katowicach
• Czasopismo: Archives of Foundry Engineering
• Rocznik: 2015
• Tom: Vol. 15, iss. 1
• Strony: 51--68
• Opis fizyczny: Bibliogr. 38 poz., rys., tab., wykr.

Twórcy

autor

Patel M. G. C.

• Department of Mechanical Engineering, National Institute of Technology Karnataka-Surathkal-575025, India

autor

Krishna P.

• Department of Mechanical Engineering, National Institute of Technology Karnataka-Surathkal-575025, India

autor

Parappagoudar M. B.

• Department of Mechanical Engineering, Chhatrapati Shivaji Institute of Technology, Durg (C.G) 491001, India

Bibliografia

• [1] Miller, W.S., Zhuang, L., Bottema, J. & Wittebrood, A. J. (2000). Recent development in aluminium alloys for the automotive industry. Materials Science and Engineering, A. 280(1). 37-49.
• [2] Hegde, S. & Prabhu, K. N. (2008). Modification of eutectic silicon in Al–Si alloys. Journal of materials science. 43(9). 3009-3027.
• [3] Maleki, A. Shafyei, A. & Niroumand, B. (2009). Effects of squeeze casting parameters on the microstructure of LM13 alloy. Journal of Materials Processing Technology. 209.8, 3790-3797.
• [4] Hosseini, V. A. Shabestari, S. G. & Gholizadeh, R. (2013). Study on the effect of cooling rate on the solidification parameters, microstructure, and mechanical properties of LM13 alloy using cooling curve thermal analysis technique. Materials & Design. 50, 7-14.
• [5] Britnell, D.J. & Neailey, K. (2003). Macrosegregation in thin walled castings produced via the direct squeeze casting process. Journal of materials processing technology. 138(1), 306-310.
• [6] Lee, J. H., Kim, H.S., Won, C.W. & Cantor, B. (2002). Effect of the gap distance on the cooling behavior and the microstructure of indirect squeeze cast and gravity die cast 5083 wrought Al alloy. Materials Science and Engineering: A. 338(1), 182-190.
• [7] Yang, L. J. (2007). The effect of solidification time in squeeze casting of aluminium and zinc alloys. Journal of materials processing technology. 192, 114-120.
• [8] Yue, T. M. (1997). Squeeze casting of high-strength aluminium wrought alloy AA7010. Journal of materials processing technology. 66(1), 179-185.
• [9] Zhang, M. Zhang, W. W., Zhao, H. D., Zhang, D. T. & Li, Y. (2007). Effect of pressure on microstructures and mechanical properties of Al-Cu-based alloy prepared by squeeze casting. Transactions of Nonferrous Metals Society of China. 17(3), 496-501.
• [10] Hajjari, E. & Divandari, M. (2008). An investigation on the microstructure and tensile properties of direct squeeze cast and gravity die cast 2024 wrought Al alloy. Materials & Design. 29(9), 1685-1689.
• [11] Hong, C.P., Lee, S.M. & Shen, H.F. (2000). Prevention of macrodefects in squeeze casting of an Al-7 wt pct Si alloy. Metallurgical and Materials Transactions B. 31(2), 297-305.
• [12] Maleki, A., Shafyei, A. & Niroumand, B. (2009). Effects of squeeze casting parameters on the microstructure of LM13 alloy. Journal of materials processing technology. 209(8), 3790-3797.
• [13] Vijian, P. & Arunachalam, V. P. (2007). Modelling and multi objective optimization of LM24 aluminium alloy squeeze cast process parameters using genetic algorithm. Journal of materials processing technology. 186(1), 82-86.
• [14] Bin, S. B., Xing, S. M., Zhao, N. & Li, L. (2013). Influence of technical parameters on strength and ductility of AlSi9Cu3 alloys in squeeze casting. Transactions of Nonferrous Metals Society of China. 23(4), 977-982.
• [15] Senthil, P. & Amirthagadeswaran, K.S. (2012). Optimization of squeeze casting parameters for non symmetrical AC2A aluminium alloy castings through Taguchi method. Journal of mechanical science and technology. 26(4), 1141-1147.
• [16] Senthil, P. & Amirthagadeswaran, K .S. (2014). Experimental Study and Squeeze Casting Process Optimization for High Quality AC2A Aluminium Alloy Castings. Arabian Journal for Science and Engineering. 39(3), 2215-2225.
• [17] Parappagoudar, M. B. & Vundavilli, P. R. (2012). Application of modeling tools in manufacturing to improve quality and productivity with case study. Proceedings in Manufacturing Systems. 7(4), 193-198.
• [18] Wang, R. J., Zeng, J. & Zhou, D. W. (2012). Determination of temperature difference in squeeze casting hot work tool steel. International journal of material forming. 5(4), 317-324.
• [19] Parappagoudar M. B., Pratihar, D. K. & Datta, G. L. (2008). Forward and reverse mapping's in green sand mould system using neural networks. Applied Soft Computing. 8(1), 239-260.
• [20] Parappagoudar, M. B., Pratihar, D. K. & Datta, G. L. (2007). Modelling of input–output relationships in cement bonded moulding sand system using neural networks. International Journal of Cast Metals Research. 20(5), 265-274.
• [21] Kittur, J. K. & Parappagoudar, M. B. (2012). Forward and reverse mappings in die casting process by neural network based approaches. J. Manuf. Sci. Prod. 12(1), 65-80.
• [22] Parappagoudar, M. B., Pratihar, D. K. & Datta, G. L. (2008). Neural network-based approaches for forward and reverse mappings of sodium silicate-bonded, carbon dioxide gas hardened moulding sand system. Materials and Manufacturing Processes. 24(1), 59-67.
• [23] Benguluri, S., Vundavilli, P. R., Bhat, R. P. & Parappagoudar, M. B. (2011). Forward and reverse mappings in metal casting—A step towards quality casting and automation, (11-009). AFS Transactions-American Foundry Society. 119(19), 1-15.
• [24] Surekha, B., Vundavilli, P. R., Parappagoudar, M. B. & Srinath, A. (2011). Design of genetic fuzzy system for forward and reverse mapping of green sand mould system. International Journal of Cast Metals Research. 24(1), 53-64.
• [25] Surekha, B., Vundavilli, P.R. & Parappagoudar, M.B. (2012). Forward and reverse mappings of the cement-bonded sand mould system using fuzzy logic, The International Journal of Advanced Manufacturing Technology. 61(9-12), 843-854.
• [26] Vundavilli, P. R., Parappagoudar, M. B., Kodali, S. P. & Benguluri, S. (2012). Fuzzy logic-based expert system for prediction of depth of cut in abrasive water jet machining process. Knowledge-Based Systems. 27, 456-464.
• [27] Surender, Y. & Pratihar, D. K. (2013). Fuzzy Logic-Based Techniques for Modeling the Correlation between the Weld Bead Dimension and the Process Parameters in MIG Welding. International Journal of Manufacturing Engineering. http://dx.doi.org/10.1155/2013/230463.
• [28] Patel, G. C. M., Mathew, R., Krishna, P. & Parappagoudar, M. B. (2014). Investigation of squeeze cast process parameters effects on secondary dendrite arm spacing using statistical regression and artificial neural network models, (Accepted for publication in Procedia Technology Elsevier Journal).
• [29] Pratihar, D. K. (2008). Soft computing, Narosa publishing house pvt. Ltd, India.
• [30] Azar, A. T. (2010). Adaptive neuro-fuzzy systems, Fuzzy systems, 85-110, Feburaury 2010, INTECH, Croatia, ISBN 978-953-7619-92-3.
• [31] Haykin, S. (1994). Neural networks: a comprehensive foundation. Prentice Hall PTR.
• [32] Rajasekaran, S. & Pai, G.V. (2003). Neural networks, fuzzy logic and genetic algorithm: synthesis and applications, PHI Learning Pvt. Ltd.
• [33] Wong, B. K. & Lai, V. S. (2011). A survey of the application of fuzzy set theory in production and operations management: 1998–2009. International Journal of Production Economics. 129(1), 157-168.
• [34] Yurdusev, M. A. & Firat, M. (2009). Adaptive neuro fuzzy inference system approach for municipal water consumption modeling: An application to Izmir, Turkey. Journal of hydrology. 365(3), 225-234.
• [35] Daoming, G. & Jie, C. (2006). ANFIS for high-pressure waterjet cleaning prediction. Surface and Coatings Technology. 201(3), 1629-1634.
• [36] Lo, S. P. (2003). An adaptive-network based fuzzy inference system for prediction of workpiece surface roughness in end milling. Journal of Materials Processing Technology. 142(3), 665-675.
• [37] Maji, K. & Pratihar, D. K. (2010). Forward and reverse mappings of electrical discharge machining process using adaptive network-based fuzzy inference system. Expert Systems with Applications. 37(12), 8566-8574.
• [38] Zeren, M. (2005). Effect of copper and silicon content on mechanical properties in Al–Cu–Si–Mg alloys. Journal of Materials Processing Technology. 169(2), 292-298.