Influence of technical parameters on strength and ductility of AlSi9Cu3 alloys in squeeze casting

Bin, Shi-bo; Xing, Shuming; Tian, long-mei; Zhao, Ning; Li, Lan

doi:10.1016/s1003-6326(13)62555-8

Cited by 34 publications

(11 citation statements)

References 9 publications

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“…However, the developed regression equation includes only linear terms and neglected the effects of square and interaction terms, moreover, pouring temperature variations was left out in their analysis. Bin SB et al, (2013) investigated the strength and ductility of squeeze cast AlSi9Cu3 alloys via taguchi tools [14]. The authors failed to develop the model, which could predict the response, in addition pressure duration contributions was left out in their analysis.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Secondary Dendrite Arm Spacing in Squeeze Casting Using Fuzzy Logic Based Approaches

Patel¹,

Krishna²,

Parappagoudar³

2015

Archives of Foundry Engineering

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The quality of the squeeze castings is significantly affected by secondary dendrite arm spacing, which is influenced by squeeze cast input parameters. The relationships of secondary dendrite arm spacing with the input parameters, namely time delay, pressure duration, squeeze pressure, pouring and die temperatures are complex in nature. The present research work focuses on the development of input-output relationships using fuzzy logic approach. In fuzzy logic approach, squeeze cast process variables are expressed as a function of input parameters and secondary dendrite arm spacing is expressed as an output parameter. It is important to note that two fuzzy logic based approaches have been developed for the said problem. The first approach deals with the manually constructed mamdani based fuzzy system and the second approach deals with automatic evolution of the Takagi and Sugeno’s fuzzy system. It is important to note that the performance of the developed models is tested for both linear and non-linear type membership functions. In addition the developed models were compared with the ten test cases which are different from those of training data. The developed fuzzy systems eliminates the need of a number of trials in selection of most influential squeeze cast process parameters. This will reduce time and cost of trial experimentations. The results showed that, all the developed models can be effectively used for making prediction. Further, the present research work will help foundrymen to select parameters in squeeze casting to obtain the desired quality casting without much of time and resource consuming.

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Section: Introductionmentioning

confidence: 99%

“…The last layer is the output layer, since only one output variable is used for the present study only single node is present. The output calculation is performed using the summation of all the received input signals from the 5 th layer shown in Eq [14]…”

mentioning

confidence: 99%

Prediction of Secondary Dendrite Arm Spacing in Squeeze Casting Using Fuzzy Logic Based Approaches

Patel¹,

Krishna²,

Parappagoudar³

2015

Archives of Foundry Engineering

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“…Powder sintered composite method [11] was sintered two types of powder together through the pressure processing. Liquid forging [12] was the process in which the liquid metal was injected into certain metal die cavity. Then the molten or semimolten metal was solidified under the mechanical static pressure.…”

Section: Introductionmentioning

confidence: 99%

Simulation of Bimetallic Bush Hot Rolling Bonding Process

Tian

Huang

2015

Advances in Materials Science and Engineering

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Three-dimensional model of bimetallic bush was established including the drive roller and the core roller. The model adopted the appropriate interface assumptions. Based on the bonding properties of bimetallic bush the hot rolling process was analyzed. The optimum reduction ratio of 28% is obtained by using the finite element simulation software MARC on the assumption of the bonding conditions. The stress-strain distribution of three dimensions was research assumptions to interface deformation of rolling. At the same time, based on the numerical simulation, the minimum reduction ratio 20% is obtained by using a double metal composite bush rolling new technology from the experiment research. The simulation error is not more than 8%.

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“…Moreover, the authors developed multivariable linear response equation by neglecting the effect of pouring temperature and time delay parameters. More recently, authors employed statistical Taguchi tool to study the influencing parameters such as squeeze pressure, filling velocity, die, and pouring temperature on strength and ductility of AlSi9Cu3 alloy [16]. It is to be noted that the authors did not consider the pouring temperature and time delay before pressurization.…”

Section: Introductionmentioning

confidence: 99%

Forward and Reverse Process Models for the Squeeze Casting Process Using Neural Network Based Approaches

Chandrashekarappa

Krishna

Parappagoudar³

2014

Applied Computational Intelligence and Soft Computing

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The present research work is focussed to develop an intelligent system to establish the input-output relationship utilizing forward and reverse mappings of artificial neural networks. Forward mapping aims at predicting the density and secondary dendrite arm spacing (SDAS) from the known set of squeeze cast process parameters such as time delay, pressure duration, squeezes pressure, pouring temperature, and die temperature. An attempt is also made to meet the industrial requirements of developing the reverse model to predict the recommended squeeze cast parameters for the desired density and SDAS. Two different neural network based approaches have been proposed to carry out the said task, namely, back propagation neural network (BPNN) and genetic algorithm neural network (GA-NN). The batch mode of training is employed for both supervised learning networks and requires huge training data. The requirement of huge training data is generated artificially at random using regression equation derived through real experiments carried out earlier by the same authors. The performances of BPNN and GA-NN models are compared among themselves with those of regression for ten test cases. The results show that both models are capable of making better predictions and the models can be effectively used in shop floor in selection of most influential parameters for the desired outputs.

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Influence of technical parameters on strength and ductility of AlSi9Cu3 alloys in squeeze casting

Cited by 34 publications

References 9 publications

Prediction of Secondary Dendrite Arm Spacing in Squeeze Casting Using Fuzzy Logic Based Approaches

Prediction of Secondary Dendrite Arm Spacing in Squeeze Casting Using Fuzzy Logic Based Approaches

Simulation of Bimetallic Bush Hot Rolling Bonding Process

Forward and Reverse Process Models for the Squeeze Casting Process Using Neural Network Based Approaches

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