Determination of temperature difference in squeeze casting hot work tool steel

Wang, Rong Ji; Zeng, Junliang; Zhou, Dianwu

doi:10.1007/s12289-011-1061-8

Cited by 13 publications

(5 citation statements)

References 11 publications

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“…It is important to note that the objective function includes only main effect parameters and the paramount importance of square and interaction parameters in identifying the non-linear effects are neglected in their research work. Wang RJ et al, (2012) [18] used artificial neural networks to predict the temperature difference of the squeeze cast part. It was observed that, ANNs finds better prediction and reduces the need of costly simulation software, and need of experts to interpret the results.…”

Section: Introductionmentioning

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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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%

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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“…These reasons made lots of researchers to search for alternate method to optimize the process. In recent years, some authors [46,47] used procast simulation software to predict temperature difference and the solidification time of the squeeze cast components. Since optimizing the process requires huge number of input-output data and is considered to be impractical either through experimental or simulation software alone.…”

Section: Introductionmentioning

confidence: 99%

Modelling in Squeeze Casting Process-Present State and Future Perspectives

Patel¹,

MB²

2015

Adv Automob Eng

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The growing demand in today's competitive manufacturing environment has encouraged the researchers to develop and apply modelling tools. The development and application of modelling tools help the casting industries to considerably increase productivity and casting quality. Till date there is no universal standard available to model and optimize any of the manufacturing processes. However the present work discusses the advantages and limitations of some conventional and non-conventional modelling tools applied for various casting processes. In addition the research effort made by various authors till date in modelling and optimization of the squeeze casting process has been reported. Furthermore the necessary steps for prediction and optimization are high lightened by identifying the trends in the literature. Ultimately this research paper explores the scope for future research in online control of the process by automatically adjusting the squeeze cast process parameters through reverse prediction by utilizing the soft computing tools namely, Neural Network, Genetic Algorithms, Fuzzy-logic Controllers and their different combinations. The present work also proposed a detailed methodology, starting from the selection of process variables till the best process variable combinations for extreme values of the outputs responsible for better product quality using experimental, prediction and optimization methodology. Citation: Manjunath Patel GC, Krishna P, Parappagoudar MB (2015) Modelling in Squeeze Casting Process-Present State and Future Perspectives. Modelling in Squeeze Casting

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“…The BPANN is superior to other methods, such as a statistical method, in simulating and analyzing nonlinear complex systems, especially in solving inexact and fuzzy information process problems. [10,11] This paper proposes a BPANN model, on the basis of a series of CAE modeling experiments, which can relate the process parameters to warpage. Utilizing the obtained BPANN prediction model, it is possible to evaluate the influence of process parameters on warpage, to know the process parameters that contribute to warpage, and to determine the optimum settings for the process parameters to reduce the warpage.…”

Section: Introductionmentioning

confidence: 99%

Modeling the Effect of Injection Molding Process Parameters on Warpage Using Neural Network Theory

et al. 2015

Journal of Macromolecular Science, Part B

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A back propagation artificial neural network (BPANN) prediction model for warpage of injection-molded polypropylene was developed based on an orthogonal design method. The BPANN model was trained by the input and output data obtained from the moldflow software platform simulations. It is proved that the BPANN model can predict the warpage with reasonable accuracy. Utilizing the BPANN model, the effects of the process parameters, packing pressure (Pp), melt temperature (T me ), mold temperature (T mo ), packing time (t p ), cooling time (t c ), and fill pressure (p f ), on the warpage were investigated. The most important process parameter affecting the warpage was Pp, and the second most important was T me . The rest of the process parameters, T mo , t p , t c , and p f , were found to be relatively less influential. Warpage increased with elevating T mo . In contrast, an increase in Pp and T me caused the warpage to decrease.

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Determination of temperature difference in squeeze casting hot work tool steel

Cited by 13 publications

References 11 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

Modelling in Squeeze Casting Process-Present State and Future Perspectives

Modeling the Effect of Injection Molding Process Parameters on Warpage Using Neural Network Theory

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