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Casting defects can be predicted in advance of practice and countermeasures can be taken to improve casting quality and increase productivity. Applying the casting filters is a method of improvement methods for defects caused by unsuitable molten metal flow. Casting filters have the effect of removing inclusions in molten metal and rectifying the flow. However, specific conditions such as the type, pore size, and setting position of the casting filter are not clear. Casting filter conditions are determined by conventional empirical rules that are not theoretical. In the other view, the use of casting CAE is essential to realize front-loading for the process design process, in which casting defects are predicted in advance of practice and countermeasures are taken. In the previous study, K. Taki et al. performed direct observation of mold filling and flow simulations passing through the casting filter. The particle-based COMINA CAE software was used for the flow simulation of molten metal in complex interior geometries in the filter. The calculations used a model of the filter that was reproduced on an X-ray CT system. To inspect the filter performance it was necessary to make a small and simplified filter model, which is called the 1/4 model of filter. In the present study, the flow dynamics through the filter are investigated using various 1/4 models. The 1/4 model maintains permeability on the surface and porosity of volume while halving the dimensions. As a result, we succeed in reproducing the flow behaviour of molten metal when it passed through the filter by setting the particle size of molten metal to 1/16 of the filter’s pore diameter. Further, we try to evaluate the performance of the filter by extending the calculation target from only the area around the filter to the entire mold. If mold filling behavior for the mold with filter could be simulated, it wouldbe used effectively in casting geometry design and defect countermeasure. | Casting defects can be predicted in advance of practice and countermeasures can be taken to improve casting quality and increase productivity. Applying the casting filters is a method of improvement methods for defects caused by unsuitable molten metal flow. Casting filters have the effect of removing inclusions in molten metal and rectifying the flow. However, specific conditions such as the type, pore size, and setting position of the casting filter are not clear. Casting filter conditions are determined by conventional empirical rules that are not theoretical. In the other view, the use of casting CAE is essential to realize front-loading for the process design process, in which casting defects are predicted in advance of practice and countermeasures are taken. In the previous study, K. Taki et al. performed direct observation of mold filling and flow simulations passing through the casting filter. The particle-based COMINA CAE software was used for the flow simulation of molten metal in complex interior geometries in the filter. The calculations used a model of the filter that was reproduced on an X-ray CT system. To inspect the filter performance it was necessary to make a small and simplified filter model, which is called the 1/4 model of filter. In the present study, the flow dynamics through the filter are investigated using various 1/4 models. The 1/4 model maintains permeability on the surface and porosity of volume while halving the dimensions. As a result, we succeed in reproducing the flow behaviour of molten metal when it passed through the filter by setting the particle size of molten metal to 1/16 of the filter’s pore diameter. Further, we try to evaluate the performance of the filter by extending the calculation target from only the area around the filter to the entire mold. If mold filling behavior for the mold with filter could be simulated, it wouldbe used effectively in casting geometry design and defect countermeasure. | ||
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+ | == Full Paper == | ||
+ | <pdf>Media:Draft_Sanchez Pinedo_101791653pap_33.pdf</pdf> |
Casting defects can be predicted in advance of practice and countermeasures can be taken to improve casting quality and increase productivity. Applying the casting filters is a method of improvement methods for defects caused by unsuitable molten metal flow. Casting filters have the effect of removing inclusions in molten metal and rectifying the flow. However, specific conditions such as the type, pore size, and setting position of the casting filter are not clear. Casting filter conditions are determined by conventional empirical rules that are not theoretical. In the other view, the use of casting CAE is essential to realize front-loading for the process design process, in which casting defects are predicted in advance of practice and countermeasures are taken. In the previous study, K. Taki et al. performed direct observation of mold filling and flow simulations passing through the casting filter. The particle-based COMINA CAE software was used for the flow simulation of molten metal in complex interior geometries in the filter. The calculations used a model of the filter that was reproduced on an X-ray CT system. To inspect the filter performance it was necessary to make a small and simplified filter model, which is called the 1/4 model of filter. In the present study, the flow dynamics through the filter are investigated using various 1/4 models. The 1/4 model maintains permeability on the surface and porosity of volume while halving the dimensions. As a result, we succeed in reproducing the flow behaviour of molten metal when it passed through the filter by setting the particle size of molten metal to 1/16 of the filter’s pore diameter. Further, we try to evaluate the performance of the filter by extending the calculation target from only the area around the filter to the entire mold. If mold filling behavior for the mold with filter could be simulated, it wouldbe used effectively in casting geometry design and defect countermeasure.
Published on 23/11/23
Submitted on 23/11/23
Volume Computational Modeling of Manufacturing Processes Using Particle and Meshless Methods, 2023
DOI: 10.23967/c.particles.2023.005
Licence: CC BY-NC-SA license
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