Analysis of workability behavior of Al–SiC P/M composites using backpropagation neural network model and statistical technique

Sivasankaran, S.; Narayanasamy, R.; Ramesh, T.; Prabhakar, M.

doi:10.1016/j.commatsci.2009.06.013

Cited by 32 publications

(14 citation statements)

References 31 publications

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“…However, the outputs so calculated are compared with the actual/desired outputs by the network and the error is transmitted to the initial layer, which results in correction of the weights. The training iteration process may be terminated either by attaining a convergence limit or simply by limiting the total number of iterations [37][38][39].…”

Section: Modeling Of Process Variablesmentioning

confidence: 99%

“…The number of hidden neurons must be sufficiently large to realize a certain function. Several structures have to be considered with different numbers of hidden neurons to determine the best configuration [37][38][39][40].…”

Section: Modeling Of Process Variablesmentioning

confidence: 99%

“…Advances in Materials Science and Engineering Similarly the performance of other various architectures was checked in the same manner as shown in Table 5 [37][38][39][40][41].…”

Section: Designing Training and Testing Of Neural Networkmentioning

confidence: 99%

“…The process variables that lead to the cast component with the desired properties can be interpolated with the help of the optimal model. This is well suited for aluminum metal casting industries where the real-time experimental runs can be reduced to a greater extent which leads to time and cost saving [38].…”

Section: Performance Measurement Of Neural Network For Experimental Datamentioning

confidence: 99%

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Modeling and Analysis of Mechanical Properties of Aluminium Alloy (A413) Processed through Squeeze Casting Route Using Artificial Neural Network Model and Statistical Technique

Soundararajan¹,

Ramesh²,

Sivasankaran

et al. 2015

Advances in Materials Science and Engineering

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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.

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Section: Modeling Of Process Variablesmentioning

confidence: 99%

Section: Modeling Of Process Variablesmentioning

confidence: 99%

“…Advances in Materials Science and Engineering Similarly the performance of other various architectures was checked in the same manner as shown in Table 5 [37][38][39][40][41].…”

Section: Designing Training and Testing Of Neural Networkmentioning

confidence: 99%

Section: Performance Measurement Of Neural Network For Experimental Datamentioning

confidence: 99%

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Modeling and Analysis of Mechanical Properties of Aluminium Alloy (A413) Processed through Squeeze Casting Route Using Artificial Neural Network Model and Statistical Technique

Soundararajan¹,

Ramesh²,

Sivasankaran

et al. 2015

Advances in Materials Science and Engineering

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“…Ferrous powder metallurgy preforms containing 0%, 0.5%, 1.0% and 1.5% of molybdenum were completely investigated experimentally to study workability behaviour. Sivasankaran et al 11 analysed the workability behaviour of Al–SiC P/M composites using back propagation neural network model and statistical technique. An artificial neural network (ANN) model for predicting and analysing the workability behaviour during cold upsetting of sintered Al–SiC powder metallurgy (P/M) metal matrix composites (MMCs) was proposed.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimization of upsetting parameters of Al–TiC metal matrix composites: A grey Taguchi approach

Sahu

Valarmathi

Baskaran

et al. 2014

Proceedings of the Institution of Mechanical Engineers, Part B:

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This work is a novel attempt to identify the optimum process parameters in cold upsetting of Al–TiC metal matrix composites with multiple responses using grey Taguchi approach. The formation of barrel and workability are the important attributes that greatly influence the process of upsetting. Three independently controllable process variables, namely, aspect ratio, friction factor and load, each at four levels are considered to find their optimum levels which yields maximum barrel radius and workability simultaneously. Grey Taguchi analysis has been performed to optimize the levels of input parameters. Both the quality characteristics are improved significantly at the optimal process condition as verified by the confirmation test. The effect of individual process variables on the responses is determined using analysis of variance.

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Data‐Driven Materials Innovation and Applications

et al. 2022

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Analysis of workability behavior of Al–SiC P/M composites using backpropagation neural network model and statistical technique

Cited by 32 publications

References 31 publications

Modeling and Analysis of Mechanical Properties of Aluminium Alloy (A413) Processed through Squeeze Casting Route Using Artificial Neural Network Model and Statistical Technique

Modeling and Analysis of Mechanical Properties of Aluminium Alloy (A413) Processed through Squeeze Casting Route Using Artificial Neural Network Model and Statistical Technique

Multi-objective optimization of upsetting parameters of Al–TiC metal matrix composites: A grey Taguchi approach

Data‐Driven Materials Innovation and Applications

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