Advanced multi-material modelling for toolpath generation and graded component fabrication in additive manufacturing
This project aims to advance multi-material additive manufacturing (MMAM) by developing a novel adaptive voxelization and parameter assignment technique. MMAM offers a flexible way to produce complex and functionally graded materials (FGMs). FGMs allow precise control over the material distributions...
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2024
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sg-ntu-dr.10356-1769252024-05-25T16:49:58Z Advanced multi-material modelling for toolpath generation and graded component fabrication in additive manufacturing Xie, Yuxuan Moon Seung Ki School of Mechanical and Aerospace Engineering A*STAR SIMTech skmoon@ntu.edu.sg Engineering Computer aided design Computer aided manufacturing Multi material additive manufacturing Adaptive voxelization This project aims to advance multi-material additive manufacturing (MMAM) by developing a novel adaptive voxelization and parameter assignment technique. MMAM offers a flexible way to produce complex and functionally graded materials (FGMs). FGMs allow precise control over the material distributions, which results in varying mechanical, electrical or chemical properties, leading to more sophisticated and cost-effective part designs and fabrications. However, the lack of material-aware printing capabilities in existing Computer Aided Design (CAD) and Computer Aided Manufacturing (CAM) software poses a significant challenge. MMAM with existing CAD/CAM software can only fabricate multi-material parts with sharp multi-material interfacial transitions. To address this challenge, we propose a novel adaptive voxelization and parameter assignment technique to achieve material-dependent variable printing toolpath generation. The proposed method enables adaptive process parameter assignment at variable resolutions. To meet the multi-material design specifications, the proposed method can generate G-code commands with required process parameters. The key novelty and contribution of this work is the mapping between the solid CAD design to the final printing toolpath with parametric transitions in the multi-material interfaces. Various transition functions, including radial distribution and linear transitions, can be defined to achieve graded material distributions. Graded voxel size is also achievable with finer resolution at multi-material interface regions to enable precise control of process parameters and material distributions, whilst coarse voxels are used for deposition in single-material regions. The proposed method sets the foundation for producing complex multi-material components by Additive Manufacturing (AM). Bachelor's degree 2024-05-21T01:25:04Z 2024-05-21T01:25:04Z 2024 Final Year Project (FYP) Xie, Y. (2024). Advanced multi-material modelling for toolpath generation and graded component fabrication in additive manufacturing. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/176925 https://hdl.handle.net/10356/176925 en RIE2025 MTC IAF-PP Grant M22K5a0045 MAE22105 URECA Project MAE23006 URECA-FYP Project application/pdf Nanyang Technological University |
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Engineering Computer aided design Computer aided manufacturing Multi material additive manufacturing Adaptive voxelization Xie, Yuxuan Advanced multi-material modelling for toolpath generation and graded component fabrication in additive manufacturing |
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This project aims to advance multi-material additive manufacturing (MMAM) by developing a novel adaptive voxelization and parameter assignment technique. MMAM offers a flexible way to produce complex and functionally graded materials (FGMs). FGMs allow precise control over the material distributions, which results in varying mechanical, electrical or chemical properties, leading to more sophisticated and cost-effective part designs and fabrications. However, the lack of material-aware printing capabilities in existing Computer Aided Design (CAD) and Computer Aided Manufacturing (CAM) software poses a significant challenge. MMAM with existing CAD/CAM software can only fabricate multi-material parts with sharp multi-material interfacial transitions. To address this challenge, we propose a novel adaptive voxelization and parameter assignment technique to achieve material-dependent variable printing toolpath generation. The proposed method enables adaptive process parameter assignment at variable resolutions. To meet the multi-material design specifications, the proposed method can generate G-code commands with required process parameters. The key novelty and contribution of this work is the mapping between the solid CAD design to the final printing toolpath with parametric transitions in the multi-material interfaces. Various transition functions, including radial distribution and linear transitions, can be defined to achieve graded material distributions. Graded voxel size is also achievable with finer resolution at multi-material interface regions to enable precise control of process parameters and material distributions, whilst coarse voxels are used for deposition in single-material regions. The proposed method sets the foundation for producing complex multi-material components by Additive Manufacturing (AM). |
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Moon Seung Ki |
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Moon Seung Ki Xie, Yuxuan |
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Final Year Project |
author |
Xie, Yuxuan |
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Xie, Yuxuan |
title |
Advanced multi-material modelling for toolpath generation and graded component fabrication in additive manufacturing |
title_short |
Advanced multi-material modelling for toolpath generation and graded component fabrication in additive manufacturing |
title_full |
Advanced multi-material modelling for toolpath generation and graded component fabrication in additive manufacturing |
title_fullStr |
Advanced multi-material modelling for toolpath generation and graded component fabrication in additive manufacturing |
title_full_unstemmed |
Advanced multi-material modelling for toolpath generation and graded component fabrication in additive manufacturing |
title_sort |
advanced multi-material modelling for toolpath generation and graded component fabrication in additive manufacturing |
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Nanyang Technological University |
publishDate |
2024 |
url |
https://hdl.handle.net/10356/176925 |
_version_ |
1800916310571352064 |