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created from the known pure nickel data. Initially, the property dataset measured by laser flash was used as a starting point. An insulating wool dataset was obtained from thermo- physical data available in the product datasheet. Te heat transfer coef- ficient (HTC) assumed between the casting and shell was 3,500 W/m2K (HTC1) and between the shell and insulating wool was 1,000 W/m2K (HTC2). Te inverse method’s goal was to


match the computationally simulated curves with the experimentally mea-


3


Results and Conclusions During the project, the


research team used the laser flash method to reduce discrepancies due to open


sured temperature curves. Te initial simulation setup was the baseline for the curve to be compared with the temperature curves obtained from the experimental castings. Figure 2 shows an example of the good match between calculated and experimental temperature curves after hundreds of simulations.


Te specific heat capacities, thermal


conductivity of the shell and insulat- ing material, and the external heat transfer coefficient (HTC3) were the main parameters that influenced the temperature curves of the casting and


shell. Preliminary modeling showed solidification time and the coordinates of the point where the shell reached the highest temperature were mainly influenced by the specific heat capacity and thermal conductivity of the shell. Density and Porosity To evaluate the shell density and


porosity, pieces of the shell were examined. Te overall bulk density and open porosity were measured. In addition, a shell specimen was crushed to 100 mesh to obtain the theoretical density. Te total porosity and closed porosity then were calculated.


Fig. 3 The graph compares theoretical val- ues and inverse method results of thermal conductivity.


porosity by determining the effec- tive thickness of the sample with the help of a 3-D optical profiler. The inverse method was used to generate a thermal properties database for investment casting shells. Using a combination of laser flash and the inverse methods, the researchers could accurately deter- mine the thermal properties for the seven industrial shell systems. Table 2 shows the densi- ties and porosities of the seven industrial shells after prefiring at 1,562F (850C) for an hour. The silica-based shells (1 and 3) are less dense compared to the alumi- nosilicate-based shells (4 and 6). The alumina-based shells (5) had the highest density. Total porosity mostly depended on the shell- building process (particle sizes, slurry viscosity, etc.), but shell 7, made by a rapid shelling process, was nearly 40% porous. Termal Properties from Inverse


Method Figure 3 shows the specific heat


Table 2. Densities and Porosities of Industrial Shells Used In This Study Bulk Density, g/cm3


Shell #1 Shell #2 Shell #3 Shell #4 Shell #5 Shell #6 Shell #7


1.64 1.53 1.63 1.93 2.24 1.98 1.96


Theoretical density, g/cm3 2.41 -


2.42 2.90 3.30 3.18 3.26


Open porosity, % 21.7 25.7 23.0 23.8 21.0 26.1 26.7


capacity and thermal conductiv- ity data estimated by the inverse method. Temperature-dependent specific heat capacities in all shells


Fig. 4. The graphs show (a) specific heat capacity and (b) thermal conductivity val- ues determined by the laser flash method.


Closed porosity, % 10.0 -


9.9 9.7


11.1 11.6 13.1


Total porosity, % 31.7 -


32.9 33.5 32.1 37.7 39.8


January 2015 MODERN CASTING | 41


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