Аналіз програмного забезпечення для моделювання технологічного процесу лиття
The paper presents an extended analysis of modern software tools intended for computer simulation of technological processes of metal and alloy casting. The theoretical and mathematical foundations of numerical modeling used to describe mold filling hydrodynamics, heat transfer, solidification, phas...
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| Date: | 2025 |
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| Main Authors: | , , |
| Format: | Article |
| Language: | Ukrainian |
| Published: |
Kamianets-Podilskyi National Ivan Ohiienko University
2025
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| Online Access: | http://mcm-tech.kpnu.edu.ua/article/view/342534 |
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| Journal Title: | Mathematical and computer modelling. Series: Technical sciences |
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Mathematical and computer modelling. Series: Technical sciences| Summary: | The paper presents an extended analysis of modern software tools intended for computer simulation of technological processes of metal and alloy casting. The theoretical and mathematical foundations of numerical modeling used to describe mold filling hydrodynamics, heat transfer, solidification, phase transformations, and the formation of macro- and microstructures of castings are considered. The capabilities of the finite element method, finite volume method, and multiphysics approaches implemented in modern simulation systems to improve prediction accuracy are analyzed.
A comparative analysis of widely used commercial and research software products, including ProCAST, MAGMASoft, FLOW-3D Cast, ANSYS, SolidCast, NovaFlow&Solid, and QuikCAST, is performed. Their functional features, advantages, limitations, and areas of practical application depending on the casting type, product geometry complexity, and defect prediction accuracy requirements are identified. Special attention is paid to the integration of simulation software into comprehensive CAD/CAE/CAM systems, enabling the development of digital twins of technological processes within the Industry 4.0 concept and improving manufacturing preparation efficiency.
Current trends in the application of artificial intelligence and machine learning methods in foundry production are also discussed, particularly for optimizing technological parameters, accelerating numerical calculations, and predicting casting defects. It is shown that the use of computer modeling reduces experimental costs, improves casting quality, and shortens the development time of new products |
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