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Variable Offset Computation Space for Automatic Cooling Dimensioning

The injection mold is one of the most important elements for the part precision of this important mass production process. The thermal mold design is realized by cooling channels around the cavity and poses as a decisive factor for the part quality. Thus, the objective but specific design of the coo...

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Detalles Bibliográficos
Autores principales: Hopmann, Christian, Fritsche, Daniel Colin, Hohlweck, Tobias, Nehring-Wirxel, Julius
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8876967/
https://www.ncbi.nlm.nih.gov/pubmed/35215675
http://dx.doi.org/10.3390/polym14040762
Descripción
Sumario:The injection mold is one of the most important elements for the part precision of this important mass production process. The thermal mold design is realized by cooling channels around the cavity and poses as a decisive factor for the part quality. Thus, the objective but specific design of the cooling channel layout is crucial for a reproducible part with high-dimensional accuracy in production. Consequently, knowledge-based and automated methods are used to create the optimal heat management in the mold. One of these methods is the inverse thermal mold design, which uses a specific calculation space. The geometric boundary conditions of the optimization algorithm influence the resulting thermal balance within the mold. As the calculation area in the form of an offset around the molded part is one of these boundary conditions, its influence on the optimization result is determined. The thermal optimizations show a dependency on different offset shapes due to the offset thickness and coalescence of concave geometries. An algorithm is developed to generate an offset for this thermal mold design methodology considering the identified influences. Hence, a reproducible and adaptive offset is generated automatically for a complex geometry, and the quality function result improves by 43% in this example.