Effect of the gap distance on the cooling behavior and the microstructure of indirect squeeze cast and gravity die cast 5083 wrought Al alloy

Lee, Sang-Kwon; Kim, H.S.; Won, C.W.; Cantor, B.

doi:10.1016/s0921-5093(02)00054-0

Cited by 71 publications

(29 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Rapid cooling results in a reduced solidification time of the casting, and thus in a finer structure of the alloy. The pressure also affects the structure of castings, where it causes a smaller grain size in the primary, structure, changes the morphology of eutectic and intermetallic particles that act less harmfully [6]. Affecting the structure results in changed mechanical properties.…”

Section: Introductionmentioning

confidence: 99%

Effect of Pressure on the Crystallisation of AlSi7Mg Alloy

Pastirčák

Ščury

2017

Archives of Metallurgy and Materials

View full text Add to dashboard Cite

This paper deals with influencing the crystallisation of Al alloys by a direct squeeze casting method. The effects of changed cooling rates of the casting is evaluated using a heat transfer coefficient at different casting conditions. The experimental results obtained by temperature measurement of the casting and the mould were used to predict the casting and mould surface temperatures using regression curves. The measured temperatures in the sub-surface layers were used to determine the amount of heat transferred from the casting to the mould. The amount of transferred heat increased 20-fold due to the effect of pressure. We also evaluated the effect of the acting pressure on the mechanical properties and microstructure of the alloy used. The process parameters were varied in the experiment.

show abstract

Section: Introductionmentioning

confidence: 99%

Effect of Pressure on the Crystallisation of AlSi7Mg Alloy

Pastirčák

Ščury

2017

Archives of Metallurgy and Materials

View full text Add to dashboard Cite

show abstract

“…The analytical methods such as gracia's virtual and steady state heat flow model had been utilized by solving the governing equations to study the effects of solidification time on the density and other mechanical properties of aluminium (Al) and zinc (Zn) based alloys [6]. The effects of gap distance on the cooling rate and secondary dendrite arm spacing were studied by using the numerical and experimental approaches [7]. The effects of pouring temperatures and squeeze pressures on the cast structure and tensile strengths of wrought aluminium alloy were investigated [8].…”

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

View full text Add to dashboard Cite

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.

show abstract

“…They inserted thermocouples through the die, located at the centre of each casting. However a numerical model was used to calculate the temperature profile and was preferred for the cooling rate extraction due to the intrinsic dispersion of the measured cooling curves (Lee et al, 2002). In fact, too many factors affect the temperature profile during casting, from melt turbulence during injection to mould transient phase.…”

Section: Introductionmentioning

confidence: 99%