Abstract
Castings of Copper–Nickel–Tin alloy were produced by varying the composition of Ni and Sn. The cast specimens were subjected to homogenization and solution treatment. The specimens were characterized for microstructure, hardness and subjected to adhesive wear test. Statistical regression model, artificial neural network model and Sugeno fuzzy model were developed to predict the hardness and wear rate of the alloy based on %Ni, %Sn and ageing time of the specimens. As Sugeno Fuzzy logic model uses adaptive neuro-fuzzy inference system, an integration of neural networks and fuzzy logic principles, the prediction efficiency was higher than statistical regression and artificial neural network model. The interaction effect of %Ni, %Sn and ageing time on the hardness and wear rate of the specimens were analysed using the Sugeno Fuzzy model.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ilangovan S, Sellamuthu R (2016) Measurement of the variation of mechanical properties with aging temperatures for sand cast Cu-5Ni-5Sn alloy. Journal of Engineering Science and Technology 11 (11):1609–1619
Ditchek B, Schwartz LH (1980) Diffraction study of spinodal decomposition in Cu-10 w/o Ni-6 w/o SN. Acta Metall 28 (6):807–822
Schwartz LH, Plewes JT (1974) Spinodal decomposition in Cu-9wt% Ni-6wt% Sn—II. A critical examination of mechanical strength of spinodal alloys. Acta Metallurgica 22 (7):911–921
Baburaj EG, Kulkarni UD, Menon ESK, Krishnan R (1979) Initial stages of decomposition in Cu-9Ni-6Sn. J Appl Crystallogr 12 (5):476–480
Schwartz LH, Mahajan S, Plewes JT (1974) Spinodal decomposition in a Cu-9 wt% Ni-6 wt% Sn alloy. Acta Metallurgica 22 (5):601–609
Kato M, Mori T, Schwartz LH (1980) Hardening by spinodal modulated structure. Acta Metall 28 (3):285–290
Deyong L, Tremblay R, Angers R (1990) Microstructural and mechanical properties of rapidly solidified Cu-Ni-Sn alloys. Mater Sci Eng, A 124 (2):223–231
Singh JB, Cai W, Bellon P (2007) Dry sliding of Cu–15 wt%Ni–8 wt%Sn bronze: Wear behaviour and microstructures. Wear 263 (1–6):830–841
Russell SJ, Norvig P, Canny JF, Malik JM, Edwards DD (2003) Artificial intelligence: a modern approach, vol 2. Prentice hall Upper Saddle River
Zurada JM (1992) Introduction to artificial neural systems, vol 8. West St. Paul
Yegnanarayana B (2009) Artificial neural networks. PHI Learning Pvt. Ltd.
Sivanandam SN, Sumathi S, Deepa SN (2006) Introduction to Fuzzy Logic using MATLAB. Springer Berlin Heidelberg
Nguyen HT, Prasad NR (1999) Fuzzy Modeling and Control: Selected Works of Sugeno. Taylor & Francis
Schwartz LH, Plewes JT (1974) Spinodal decomposition in Cu-9wt% Ni-6wt% Sn—II. A critical examination of mechanical strength of spinodal alloys. Acta Metall 22 (7):911–921
Schwartz LH, Mahajan S, Plewes JT (1974) Spinodal decomposition in a Cu-9 wt% Ni-6 wt% Sn alloy. Acta Metall 22 (5):601–609
Ilangovan S, Sellamuthu R (2012) An investigation of the effect of Ni content and hardness on the wear behaviour of sand cast Cu–Ni–Sn alloys. Int J Microstruct Mater Prop 7 (4):316–328
Zhao DM, Dong QM, Liu P, Kang BX, Huang JL, Jin ZH (2003) Structure and strength of the age hardened Cu–Ni–Si alloy. Mater Chem Phys 79 (1):81–86
Vignesh RV, Padmanaban R, Arivarasu M, Karthick KP, Sundar AA, Gokulachandran J (2016) Analysing the strength of friction stir spot welded joints of aluminium alloy by fuzzy logic. IOP Conference Series: Materials Science and Engineering 149 (1)
Ramalingam VV, Ramasamy P (2017) Modelling Corrosion Behavior of Friction Stir Processed Aluminium Alloy 5083 Using Polynomial: Radial Basis Function. Transactions of the Indian Institute of Metals 70 (10):2575–2589
Zhang S-Z, Jiang B-H, Ding W-J (2008) Wear of Cu–15Ni–8Sn spinodal alloy. Wear 264 (3–4):199–203
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ilangovan, S., Vaira Vignesh, R., Padmanaban, R., Gokulachandran, J. (2018). Comparison of Statistical and Soft Computing Models for Predicting Hardness and Wear Rate of Cu-Ni-Sn Alloy. In: Pattnaik, P., Rautaray, S., Das, H., Nayak, J. (eds) Progress in Computing, Analytics and Networking. Advances in Intelligent Systems and Computing, vol 710. Springer, Singapore. https://doi.org/10.1007/978-981-10-7871-2_54
Download citation
DOI: https://doi.org/10.1007/978-981-10-7871-2_54
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-7870-5
Online ISBN: 978-981-10-7871-2
eBook Packages: EngineeringEngineering (R0)