Computational Intelligence for Analysing the Mechanical Properties of AA 2219 - (B4C + h-BN) Hybrid Nano Composites Processed by Ultrasound Assisted Casting

• Autorzy: Radha P. , Selvakumar N. , Harichandran R.

Identyfikatory

DOI

10.24425/amm.2019.129509

• Języki publikacji: EN

Abstrakty

EN

The computational intelligence tool has major contribution to analyse the properties of materials without much experimentation. The B₄ C particles are used to improve the quality of the strength of materials. With respect to the percentage of these particles used in the micro and nano, composites may fix the mechanical properties. The different combinations of input parameters determine the characteristics of raw materials. The load, content of B₄ C particles with 0%, 2%, 4%, 6%, 8% and 10% will determine the wear behaviour like CoF, wear rate etc. The properties of materials like stress, strain, % of elongation and impact energy are studied. The temperature based CoF and wear rate is analysed. The temperature may vary between 30°C, 100°C and 200°C. In addition, the CoF and wear rate of materials are predicted with respect to load, weight % of B₄ C and nano hexagonal boron nitride %. The intelligent tools like Neural Networks (BPNN, RBNN, FL and Decision tree) are applied to analyse these characteristics of micro/nano composites with the inclusion of B₄ C particles and nano hBN % without physically conducting the experiments in the Lab. The material properties will be classified with respect to the range of input parameters using the computational model.

Słowa kluczowe

EN

powder metallurgy; soft computing; ANN; fuzzy logic; decision tree

• Wydawca: Institute of Metallurgy and Materials Science of Polish Academy of Sciences ; Committee of Materials Engineering and Metallurgy of Polish Academy of Sciences
• Czasopismo: Archives of Metallurgy and Materials
• Rocznik: 2019
• Tom: Vol. 64, iss. 3
• Strony: 1163--1173
• Opis fizyczny: Bibliogr. 13 poz., rys., tab., wzory

Twórcy

autor

Radha P.

• Mepco Schlenk Engineering College, Department of Computer Applications, Sivakasi, Pin.: 626 005, Tamilnadu, India

autor

Selvakumar N.

• Centre for Nano Science and Technology, Department of Mechanical Engineering, Mepco Schlenk Engineering College, Sivakasi, Pin.: 626 005, Tamilnadu, India

autor

Harichandran R.

• National Engineering College, Department of Mechanical Engineering, Kovilpatti, Pin.: 628 503, Tamilnadu, India

Bibliografia

• [1] S. Alalhessabi, S. A. Manafi; E. Borhani, The structural and mechanical properties of Al-2.5%wt. B4C metal matrix nano-composite fabricated by the mechanical alloying, The Mechanics of Advanced Composite Structures 2 (1), 39-44 (2015).
• [2] M. R. Morovvati, A. Lalehpour, A. Esmaeilzare, Effect of nano/micro B4C and SiC particles on fracture properties of aluminium 7075 particulate composites under chevron-notch plane strain fracture toughness test, Materials Research Express 3 (12), (2016).
• [3] S. Gopalakannan, T. Senthilvelan, Synthesis and characterisation of Al 7075 reinforced with SiC and B4C nano particles fabricated by ultrasonic cavitation method, Journal of Scientific and Industrial Research 74, 281-285 (2015).
• [4] V. Sukesha, Rajeev Ranjan, G. Nagesh, K. Sekar, Fabrication and study on mechanical and tribological properties of nano Al2O3and micro b4c particles reinforced A356 hybrid composites, 5th International & 26th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2014) 12-14th December 2014, IIT, Guwahati, Assam, India.
• [5] P. Radha, G. Chandrasekaran, N. Selvakumar, Simplifying the powder metallurgy manufacturing process using soft computing tools, Applied Soft Computing 27 (2), 191-204 (2015).
• [6] A. Canakci, T. Varol, S. Ozsahin, S. Ozkayal, Artificial neural network approach to predict the abrasive wear of AA2024-B4C composites, Universal Journal of Materials Science 2 (6), 111-118 (2014).
• [7] R. Soundararajan, A. Ramesh, S. Sivasankaran, M. Vignesh, Modelling and analysis of mechanical properties of aluminium alloy (A413) reinforced with boron carbide (B4C) processed through squeeze casting process using artificial neural network model and statistical technique, Materials Today Proceedings 4 (2), Part A, 2008-2030 (2017).
• [8] T. Varol, S. Ozsahin, Artificial neural network analysis of the effect of matrix size and milling time on the properties of flake Al-Cu-Mg alloy particles synthesized by ball milling, An International Journal Particulate Science and Technology, 2018.
• [9] Mohsen Ostadshabani, AliMazahery, Prediction performance of various numerical model training algorithms in solidification process of A356 matrix composites, International Journal of Engineering Material Sciences 12, 129-134 (2012).
• [10] Rashmi Amardeep, K. Thippe Swamy, Training feed forward neural network with back propogation algorithm, International Journal of Engineering and Computer Science 6 (1), 19860-19866 (2017).
• [11] P. Radha, Balancing the complexity of architecture and generalization of soft-computing model in predicting the properties of composite preforms, GRD publishers 5 (3), 89-101 (2019).
• [12] Kasim M. Daws, Zouhair I. AL-Dawood, Sadiq H. AL-Kabi, Fuzzy logic approach for metal casting selection process, Jordan Journal of Mechanical and Industrial Engineering 3 (3), 162-167 (2009).
• [13] Bhaskar N. Patel, Satish G. Prajapati, Kamaljit I. Lakhtaria, Efficient classification of data using decision, Bonfring International Journal of Data Mining 2 (1), 6-12 (2012).

Uwagi

EN

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).