A Neural Network Model for the Compressive Strength of a Hybrid LM6 Aluminium Alloy Composite
Sathyabalan P1, Srimath R2
1Sathyabalan P, Department of Mechanical Engineering, Kumaraguru College of Technology, Coimbatore (Tamil Nadu), India.
2Srimath R, Department of Mechanical Engineering, Sri Ramakrishna Engineering College, Coimbatore (Tamil Nadu), India.
Manuscript received on 23 August 2019 | Revised Manuscript received on 11 September 2019 | Manuscript Published on 17 September 2019 | PP: 1652-1654 | Volume-8 Issue-2S8 August 2019 | Retrieval Number: B11230882S819/2019©BEIESP | DOI: 10.35940/ijrte.B1123.0882S819
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Adding more than one reinforcement increases the flexibility in composites. The objective of the work is to develop a model to predict the compressive strength in an LM6 aluminium alloy reinforced with SiC and flyash particles. Central composite rotatable design had been employed to carry out the experiments with size and composition of the reinforcements as the parameters. ANN model developed has good prediction accuracy with error being less than 5%.
Keywords: LM6 Aluminium alloy, SiC, Flyash, Compressive Strength, ANN.
Scope of the Article: Composite Materials