Development of Wax Blend Pattern and Optimization of Injection Process Parameters by Grey-Fuzzy Logic in Investment Casting Process

Karunakar, D. Benny

doi:10.1007/s40962-021-00655-y

Cited by 7 publications

(2 citation statements)

References 16 publications

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“…Investment pattern waxes are blends of many compounds comprising natural or synthetic wax, natural or synthetic resin, solid organic fillers, and even water [5]. These additions are combined to achieve the best proportion of cohesiveness, surface finish, fluidity, strength, and dimensional accuracy [13,14]. As for wax fluidity, the addition of soybeans, gum arabic, silicone, etc., can increase it or, in the case of pectin, agar, polyvinylpyrrolidone-40 (PVP-40), etc., decrease it, or do not influence it [12,15,16].…”

Section: Introductionmentioning

confidence: 99%

The Influence of Injection Temperature and Pressure on Pattern Wax Fluidity

Bazhenov,

Sannikov,

Kovyshkina

et al. 2023

JMMP

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In the investment casting process, the pattern made of wax is obtained in a die for further formation of a shell mold. The problem of die-filling by pattern wax is significant because it influences the quality of the final casting. This work investigates three commercial pattern waxes’ fluidity with a newly developed injection fluidity test. It was shown that the fluidity of waxes increased with increasing injection temperature and pressure, and the simultaneous increase in temperature and pressure gives a much more significant enhancement of fluidity than an increase in temperature or pressure separately. The rheological behavior of the waxes was also investigated at different temperatures using a rotational viscosimeter, and temperature dependences of waxes’ dynamic viscosity were determined. It was shown that wax viscosity is increased more than ten times with decreasing temperature from 90 to 60 °C. A good correlation between wax fluidity and its viscosity is observed, which is different from metallic alloys, where the solidification behavior is more critical. The difference in wax flow behavior in comparison with metallic melts is associated with the difference in dynamic viscosity, which for investigated waxes and metallic melts is 3000–27,000 mPa·s and 0.5–6.5 mPa·s, respectively. The difference in investigated filled waxes’ fluidity is observed, which can be associated with the type and amount of filler. The twice-increasing fraction of cross-linked polystyrene decreases fluidity twice. At the same time, terephthalic acid has a minor influence on wax fluidity.

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Section: Introductionmentioning

confidence: 99%

The Influence of Injection Temperature and Pressure on Pattern Wax Fluidity

Bazhenov,

Sannikov,

Kovyshkina

et al. 2023

JMMP

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“…Many researchers usually adopt the Taguchi approach or design of experiments (DOE) method to investigate the effects of process parameters on dimensional accuracy [4][5][6]. The response indicators are usually defined as dimensional shrinkage, discrete point deviations, or other performance indexes.…”

Section: Introductionmentioning

confidence: 99%

Experimental Study and GRNN Modeling of Shrinkage Characteristics for Wax Patterns of Gas Turbine Blades Considering the Influence of Complex Structures

et al. 2023

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With the continuous increase in power demand in aerospace, shipping, electricity, and other industries, a series of manufacturing requirements such as high precision, complex structure, and thin wall have been put forward for gas turbines. Gas turbine blades are the key parts of the gas turbine. Their manufacturing accuracy directly affects the fuel economy of the gas turbine. Thus, how to improve the manufacturing accuracy of gas turbine blades has always been a hot research topic. In this study, we perform a quantitative study on the correlation between process parameters and the overall wax pattern shrinkage of gas turbine blades in the wax injection process. A prediction model based on a generalized regression neural network (GRNN) is developed with the newly defined cross-sectional features consisting of area, area ratio, and some discrete point deviations. In the qualitative analysis of the cross-sectional features, it is concluded that the highest accuracy of the wax pattern is obtained for the fourth group of experiments, which corresponds to a holding pressure of 18 bars, a holding time of 180 s, and an injection temperature of 62 °C. The prediction model is trained and tested based on small experimental data, resulting in an average RE of 1.5% for the area, an average RE of 0.58% for the area ratio, and a maximum MSE of less than 0.06 mm2 for discrete point deviations. Experiments show that the GRNN prediction model constructed in this study is relatively accurate, which means that the shrinkage of the remaining major investment casting procedures can also be modeled and controlled separately to obtain turbine blades with higher accuracy.

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