journal article·2013-08

Predicting Charpy impact energy of Al6061/SiC p laminated nanocomposites in crack divider and crack arrester forms

Hesam Pouraliakbar, Ali Nazari, Pouriya Fataei, Akbar Karimi Livary, Mohammad Jandaghi

Ceramics International· Volume 39· Issue 6
Publisher page

Abstract

Charpy impact energy of the produced Al6061–SiCp laminated nanocomposites by mechanical alloying was modeled by adaptive neuro-fuzzy interfacial systems (ANFIS) in both crack divider and crack arrester configurations. The model was constructed by training, validating and testing of 171 gathered input–target data. The thickness of layers, the number of layers, the adhesive type, the crack tip configuration and the content of SiC nanoparticles were five independent input parameters utilized for modeling. The output parameter was Charpy impact energy of the nanocomposites. The performance of the proposed models was evaluated by absolute fraction of variance, the absolute percentage error and the root mean square error and the best values of 0.9945, 3.521 and 8.224, respectively acquired for them. The results introduced ANFIS as an influential tool for predicting the Charpy impact energy of the considered Al6061–SiCp laminate nanocomposites. & 2013 Elsevier Ltd and Techna Group S.r.l. All rights reserved.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Advanced machining processes and optimization · Mechanical Behavior of Composites · Advanced Machining and Optimization Techniques

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (23)

Show source-provided references
1. Novel nanoparticle-reinforced metal matrix composites with enhanced mechanical properties10.1002/adem.200700106 ↗
2. Effect of reinforce- ment volume fraction on mechanical alloying of Al–SiC nanocomposite10.1179/174329007X189621 ↗
3. Effect of extrusion temperature on the microstructure and porosity of A356–SiCp composites10.1016/j.jmatprotec.2006.11.077 ↗
4. Aluminium–lithium/SiCp composites produced by mechanically milled powders10.1016/S0924-0136(96)02809-9 ↗
5. SiC-reinforced aluminium composite made by resistance sintering of mechanically alloyed powders10.1016/0921-5093(89)90534-0 ↗
6. Structural and morphological evaluation of Al–5vol%SiC nanocomposite powder produced by mechanical milling10.1016/j.jallcom.2010.10.188 ↗
7. Mechanical solid state mixing for synthesizing of SiCp/Al nanocomposites10.1016/S0925-8388(98)00658-6 ↗
8. Fabrication of Al–7wt%Si–0.4wt%Mg/SiC nanocomposite powders and bulk nanocomposites by high energy ball milling and powder metallurgy10.1016/j.cap.2003.11.002 ↗
9. Enhanced mechanical properties of an Al based metal matrix composite prepared using mechanical alloying10.1016/S0921-5093(98)00676-5 ↗
10. Artificial neural networks for prediction Charpy impact energy of Al6061/SiCp laminated nanocomposites
11. Modeling Charpy impact behavior of Al6061/SiCp laminated nanocomposites by genetic programming10.1016/j.ceramint.2012.08.051 ↗
12. ANFIS: adaptive-network-based fuzzy inference system10.1109/21.256541 ↗
13. Modeling impact resistance of aluminum–epoxy-laminated composites by artificial neural networks10.1177/0021998311421222 ↗
14. Artificial neural network modeling of mechanical alloying process for synthesizing of metal matrix nanocomposite powders10.1016/j.msea.2007.02.075 ↗
15. ASTM E23, Standard test methods noteched bar impact testing of me6tallic materials, ANFISual book of ASTM standards, ASTM, Philadelphia, PA, 2001.
16. Prediction of mechanical properties of recycled aggregate concretes containing silica fume using artificial neural networks and fuzzy logic10.1016/j.commatsci.2007.06.011 ↗
17. Nuro-fuzzy modeling and control10.1109/5.364486 ↗
18. Prediction of web crippling strength of cold-formed steel sheetings using neural networks10.1016/j.jcsr.2006.01.008 ↗
19. Prediction of rotation capacity of wide flange beams using neural networks10.1016/j.jcsr.2006.01.003 ↗
20. Neural network modeling of strength enhancement for Cfrp confined concrete cylinders10.1016/j.buildenv.2007.01.036 ↗
21. Computing based approach for the prediction of ultimate strength of metal plates in compression10.1016/j.engstruct.2006.05.005 ↗
22. Application of network based neuro-fuzzy system for prediction of the strength of high strength concrete
23. Prediction of compressive strength of concrete containing fly ash using artificial neural network and fuzzy logic10.1016/j.commatsci.2007.04.009 ↗