Influence of injection process parameters on dimensional stability of wax patterns made by the lost wax process using Taguchi approach

Pattnaik, Sarojrani; Karunakar, D. Benny; Jha, Pradeep Kumar

doi:10.1177/1464420712451807

Cited by 11 publications

(10 citation statements)

References 9 publications

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“…3 produced by mixing 25% of paraffin wax, 5 % of carnauba wax, 35% of microcrystalline wax, 12% of polyethylene wax and 23% of starch showed least shrinkage and remarkable surface finish. 19 It has also been found that the aforementioned wax blend possessed low-ash content and viscosity followed by sufficient hardness. Hence, further experiments were carried out using blend no.…”

Section: Methodsmentioning

confidence: 95%

A prediction model for the lost wax process through fuzzy-based artificial neural network

Pattnaik

Karunakar

Jha

2013

Proceedings of the Institution of Mechanical Engineers, Part C:

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The application of investment casting process is rapidly increasing, specifically for near net shape manufacturing of complex and small engineering components. The process begins with making of wax patterns, thereafter employing a precision mould, dewaxing, pouring molten alloy and knocking the shell, followed by minor finishing operations. This study is about predicting the quality of responses of the wax patterns namely linear shrinkage and surface roughness using fuzzy-based artificial neural network. The process parameters considered are injection temperature, injection pressure and holding time, and experiments have been performed as per Taguchi’s L18 orthogonal array. As optimum parameter levels were different for both the responses, fuzzy logic reasoning has been used to combine all the objectives and transform the experimental results into single performance index known as multi-response performance index. Later, modelling of the process has been done using artificial neural network with experimental process parameters as inputs and multi-response performance index as output obtained from fuzzy modelling. Further, experiments have been conducted at random combination of parameter levels to validate the developed model, and it has been found that the actual results agreed well with that of the predicted value on the basis of mean absolute percentage error and correlation plots.

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Section: Methodsmentioning

confidence: 95%

A prediction model for the lost wax process through fuzzy-based artificial neural network

Pattnaik

Karunakar

Jha

2013

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…According to equation (7), if f kÁj (u j (k)) is derivable at the point u t j (k), the Taylor series expansion of equation ( 9) can be shown…”

Section: State Space Equationmentioning

confidence: 99%

“…The effect of wax injection processing parameters on the final dimensions and dimensional stability of the wax part was indicated by some experimental investigations. 5,6 Pattnaik et al 7 found the optimum levels of the process parameters on shrinkage of wax parts via the signal-to-noise and the analysis of variance method. Furthermore, Chen et al 8,9 used the mathematical model to describe the relationship between process parameters and dimension shrinkage variation of injection molded wax part with thin shell feature via response surface methodology.…”

Section: Introductionmentioning

confidence: 99%

Dimensional variation stream modeling of investment casting process based on state space method

Liu

Jin

Lai

et al. 2014

Proceedings of the Institution of Mechanical Engineers, Part B:

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Investment casting process produces high-precision castings, but there is a constant demand for improving the process capabilities, including dimensional accuracy and consistency. In this article, a state space modeling approach of investment casting process for dimensional variation is developed. This research focuses on the linear dimensional change (expansion or shrinkage) in the investment casting process. The generation, propagation and accumulation of the dimensional variation in the investment casting process are interpreted. In order to develop a mathematical model to describe the procedure above, a notion, the dimensional variation stream, is employed, and several key concepts, such as dimensional change rate, state vector and process parameter variation vector, are defined. The inherent relationships among these components are uncovered and finally bring about a State Space Model that describes the dimensional variation stream in the whole investment casting process. In the end, the usages of the developed model are illustrated and summarized via studying a case.

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“…Yarlagadda and Hock [8] found the correctness of wax patterns manufactured by hard and soft tooling and further optimized the injection process parameters to achieve good dimensional accurateness of the patterns. Further, Pattnaik et al [9][10][11] determined the best wax blend composition made from different types of waxes and fillers and also optimized the injection process parameters so as to reduce pattern shrinkage at the same time improving surface finish and hardness.…”

Section: Introductionmentioning

confidence: 99%

Investigation on controlling the process parameters for improving the quality of investment cast parts

Pattnaik

2018

J Braz. Soc. Mech. Sci. Eng.

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Investment casting is a highly flexible process which was previously perceived as an expensive process. However, when the process is compared to other optional processes which may require machining or welding, this casting can produce metallic components at highly competitive costs. There are many process variables which affect the process such as die temperature, wax temperature, injection pressure, shell firing temperature and time, cooling rate. In this study, important shell parameters such as preheat temperature, firing temperature and firing time, and melt pouring temperature have been chosen as process variables influencing the quality of the hypoeutectic aluminium-silicon alloy investment casting. The optimal input parametric condition for reduction of linear and volumetric shrinkages and increment of tensile strength of Al-Si 7%-Mg investment casting has been identified as shell preheat temperature of 200°C, firing temperature of 900°C, firing time of 7 h and pouring temperature of 600°C. At this optimal setting, it was found that linear and volumetric shrinkages decreased from 0.65 and 1.89% to 0.381 and 1.546%. The tensile strength of the casting increased from 96 to 121 MPa with regard to the nine experimental runs performed. Microstructural observation revealed that higher shell preheat and pouring temperatures led to augmented porosity, increased secondary dendrite arm spacing (35.53 ± 2.4 lm), larger detrimental iron-rich intermetallics (40.49 ± 25.15 lm) followed by reduced tensile properties of the casting (96 MPa).

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Influence of injection process parameters on dimensional stability of wax patterns made by the lost wax process using Taguchi approach

Cited by 11 publications

References 9 publications

A prediction model for the lost wax process through fuzzy-based artificial neural network

A prediction model for the lost wax process through fuzzy-based artificial neural network

Dimensional variation stream modeling of investment casting process based on state space method

Investigation on controlling the process parameters for improving the quality of investment cast parts

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