Modeling and Analysis of Mechanical Properties of Aluminium Alloy (A413) Processed through Squeeze Casting Route Using Artificial Neural Network Model and Statistical Technique
Artificial Neural Network (ANN) approach was used for predicting and analyzing the mechanical properties of A413 aluminum alloy produced by squeeze casting route. The experiments are carried out with different controlled input variables such as squeeze pressure, die preheating temperature, and melt...
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | Advances in Materials Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2015/714762 |
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| author | R. Soundararajan A. Ramesh S. Sivasankaran A. Sathishkumar |
| author_facet | R. Soundararajan A. Ramesh S. Sivasankaran A. Sathishkumar |
| author_sort | R. Soundararajan |
| collection | DOAJ |
| description | Artificial Neural Network (ANN) approach was used for predicting and analyzing the mechanical properties of A413 aluminum alloy produced by squeeze casting route. The experiments are carried out with different controlled input variables such as squeeze pressure, die preheating temperature, and melt temperature as per Full Factorial Design (FFD). The accounted absolute process variables produce a casting with pore-free and ideal fine grain dendritic structure resulting in good mechanical properties such as hardness, ultimate tensile strength, and yield strength. As a primary objective, a feed forward back propagation ANN model has been developed with different architectures for ensuring the definiteness of the values. The developed model along with its predicted data was in good agreement with the experimental data, inferring the valuable performance of the optimal model. From the work it was ascertained that, for castings produced by squeeze casting route, the ANN is an alternative method for predicting the mechanical properties and appropriate results can be estimated rather than measured, thereby reducing the testing time and cost. As a secondary objective, quantitative and statistical analysis was performed in order to evaluate the effect of process parameters on the mechanical properties of the castings. |
| format | Article |
| id | doaj-art-23e9bfe029b749dcafe06a9471b56a3c |
| institution | Kabale University |
| issn | 1687-8434 1687-8442 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Materials Science and Engineering |
| spelling | doaj-art-23e9bfe029b749dcafe06a9471b56a3c2025-02-03T06:04:58ZengWileyAdvances in Materials Science and Engineering1687-84341687-84422015-01-01201510.1155/2015/714762714762Modeling and Analysis of Mechanical Properties of Aluminium Alloy (A413) Processed through Squeeze Casting Route Using Artificial Neural Network Model and Statistical TechniqueR. Soundararajan0A. Ramesh1S. Sivasankaran2A. Sathishkumar3Department of Mechanical Engineering, Sri Krishna College of Engineering & Technology, Coimbatore 640008, IndiaDepartment of Mechanical Engineering, Sri Krishna College of Technology, Coimbatore 641042, IndiaSchool of Mechanical and Electromechanical Engineering, Institute of Technology, Hawassa University, 1530 Awassa, EthiopiaDepartment of Mechanical Engineering, PPG Institute of Technology, Coimbatore 641035, IndiaArtificial Neural Network (ANN) approach was used for predicting and analyzing the mechanical properties of A413 aluminum alloy produced by squeeze casting route. The experiments are carried out with different controlled input variables such as squeeze pressure, die preheating temperature, and melt temperature as per Full Factorial Design (FFD). The accounted absolute process variables produce a casting with pore-free and ideal fine grain dendritic structure resulting in good mechanical properties such as hardness, ultimate tensile strength, and yield strength. As a primary objective, a feed forward back propagation ANN model has been developed with different architectures for ensuring the definiteness of the values. The developed model along with its predicted data was in good agreement with the experimental data, inferring the valuable performance of the optimal model. From the work it was ascertained that, for castings produced by squeeze casting route, the ANN is an alternative method for predicting the mechanical properties and appropriate results can be estimated rather than measured, thereby reducing the testing time and cost. As a secondary objective, quantitative and statistical analysis was performed in order to evaluate the effect of process parameters on the mechanical properties of the castings.http://dx.doi.org/10.1155/2015/714762 |
| spellingShingle | R. Soundararajan A. Ramesh S. Sivasankaran A. Sathishkumar Modeling and Analysis of Mechanical Properties of Aluminium Alloy (A413) Processed through Squeeze Casting Route Using Artificial Neural Network Model and Statistical Technique Advances in Materials Science and Engineering |
| title | Modeling and Analysis of Mechanical Properties of Aluminium Alloy (A413) Processed through Squeeze Casting Route Using Artificial Neural Network Model and Statistical Technique |
| title_full | Modeling and Analysis of Mechanical Properties of Aluminium Alloy (A413) Processed through Squeeze Casting Route Using Artificial Neural Network Model and Statistical Technique |
| title_fullStr | Modeling and Analysis of Mechanical Properties of Aluminium Alloy (A413) Processed through Squeeze Casting Route Using Artificial Neural Network Model and Statistical Technique |
| title_full_unstemmed | Modeling and Analysis of Mechanical Properties of Aluminium Alloy (A413) Processed through Squeeze Casting Route Using Artificial Neural Network Model and Statistical Technique |
| title_short | Modeling and Analysis of Mechanical Properties of Aluminium Alloy (A413) Processed through Squeeze Casting Route Using Artificial Neural Network Model and Statistical Technique |
| title_sort | modeling and analysis of mechanical properties of aluminium alloy a413 processed through squeeze casting route using artificial neural network model and statistical technique |
| url | http://dx.doi.org/10.1155/2015/714762 |
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