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Development and Formulation of a Machine Learning Model for Analyzing Microstructures in Lattice Structures with Spherical Porosities

Safinia, Pouya | 2023

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 57182 (08)
  4. University: Sharif University of Technology
  5. Department: Mechanical Engineering
  6. Advisor(s): Asghari, Mohsen
  7. Abstract:
  8. lattice structures are part of cellular structures that have cavities or porosities. Because of their unique properties that could be seen in human tissue structures such as bones, despite their low weight, these structures possess desirable mechanical properties and high energy absorption capability. In recent years with the development of additive manufacturing methods that could be used to generate these structures, lattice structures have gained more attention. The analysis and investigation of these structures are usually carried out using various methods to optimize their shape or find their mechanical properties. These methods can be divided into 4 groups: FEM/Analytical methods, homogenization methods, experimental methods, and data-driven methods. Data-driven methods can alleviate the time-consuming nature of homogenization methods and the need for high computational power in finite element methods. However, in this field, there has not been a machine learning model for predicting the mechanical properties of porous materials with irregular geometry. Additionally, while these methods have been developed for composite materials, there is no machine-learning model available for predicting the mechanical properties of composite materials with low computational cost. In this research, after creating a very large set of randomly different foam geometries as RVEs with spherical porosities, the geometries created by a Python code using two algorithms were homogenized using ABAQUS software. A large dataset, in pairs, including foam geometries as features and homogenized elastic tensors as labels was then created. Subsequently, three different machine learning models that do not require high computational power were developed, each with a different presentation of geometry and a different type of machine learning model. Among these three models, the main model, which had the best results among the three, was developed with inspiration from the transformer model and showed excellent performance in predicting the homogenized elastic tensor, achieving an R-squared score of 0.958 on the test data. By successfully predicting the elastic tensor, the results have shown that the computational time and cost of estimating the mechanical properties of the materials under study can be significantly reduced using machine learning models, even if the structure has a complex geometry
  9. Keywords:
  10. Lattice Structures ; Foam Stability ; Homogenization ; Neural Network ; Point Cloud ; Point Transformer ; Microstructure

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