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Title: Optimisation studies and modelling of material flow in 3D cementitious material printing
Authors: Liu, Zhixin
Keywords: Engineering::Mechanical engineering
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Liu, Z. (2021). Optimisation studies and modelling of material flow in 3D cementitious material printing. Doctoral thesis, Nanyang Technological University, Singapore.
Abstract: Three-dimensional (3D) cementitious material printing (3DCMP), which produces structures layer by layer without collapse, is gaining popularity due to its various merits such as fabricating complex and highly customised structures, significantly labour-savings, and increasing worker safety. Numerous investigations have been conducted on developing new materials for 3DCMP and examining the impacts of the printing process on filament quality experimentally. However, the 3D cementitious materials have not been tailored in a systematic method, and the material flow behaviour has not been studied thoroughly under various conditions. Hence, this thesis deals with the following four topics: (1) Material multi-objective optimisation based on material rheological properties (dynamic yield stress, plastic viscosity); (2) Modelling of material flow behaviour during the extrusion and deposition process, and investigating the combined effects of material rheological properties and printing process parameters on filament deformation for straight-line printing; (3) Developing numerical models to investigate material flow behaviour by considering the combined effects of material rheological properties and printing process parameters at corners; (4) Identifying printing process windows based on filament cross-section ratio and developing a novel nozzle configuration to improve filament homogeneity at corners. Firstly, the Mixture Design Approach is adopted in this thesis to formulate the correlation between the cementitious material components and material rheological properties (such as static yield stress and dynamic yield stress), and then identify the optimal components for 3DCMP. Additionally, the content of each component is extended to a wider range rather than a specific point by using the Desirability function. Finally, the Mixture Design Approach is proven to be an effective method of optimising the cementitious materials used in 3DCMP applications. Secondly, a numerical model is developed to investigate the material flow behaviour during the extrusion and deposition process in straight-line printing. Additionally, a Support Vector Machine (SVM) approach is proposed to investigate the combined effects of material rheological properties and printing process parameters on deformation of the printed filament. The SVM model results show that the deformation of the printed filament is independent of plastic viscosity, whereas material yield stress and relative nozzle travel speed significantly affect the deformation of the printed filament. Thereafter, an empirical parametric associative model is proposed to predict the filament deformation based on material yield stress and relative nozzle travel speed. Finally, the numerical model is extended to study the formation of voids within printed multi-layer structures. The results show that products porosity decreases with relative nozzle travel speed ζ for both the circular nozzle and rectangular nozzle. The rectangular nozzle performs better in reducing product porosity. Thirdly, tearing or skewing may occur at the filament surface due to uneven mass distribution of the filament produced with a rotational rectangular nozzle at corner. To remove the undesired phenomenon, the one-dimensional (1D) and three-dimensional (3D) models are developed to study the flow behaviour during the extrusion and deposition process, and hence the filament mass distribution at corners. Both the 1D and 3D models give reasonable predictions as the tool path radius R is larger than 60 mm. However, only the 3D model gives better prediction as the tool path radius is smaller than 60 mm. The 3D numerical results show that material flows in three dimensional domains and some material spills from the overfilled zone during the deposition process. Additionally, the results indicate that the rheological properties have little effect on the cross-section ratio, while the printing process parameters affect filament cross-section ratio significantly. A higher relative nozzle travel speed, larger tool path radius, and lower nozzle aspect ratio are promising routes in obtaining a uniform material distribution of the filament. Since the material flow behaviour at corners is sensitive to printing process parameters, it is necessary for the printing process windows to be identified to ensure homogeneous filament mass distribution and good mechanical properties of the printed filament at corners. The support vector machine (SVM) method is used to predict the acceptable printing process window, and the prediction accuracy is improved with the transfer learning method. The experimental results show the feasibility and effectiveness of the machine learning methods in printing process windows determination under various conditions. Generally, the machine learning method (data-driven method) provides more systematic, reliable, and efficient results than the conventional variable control method in printing process windows determination. However, a homogeneity of the filament still cannot be achieved by changing printing process parameters, hence, a novel nozzle configuration (the Gaussian shape) is proposed to address the issue due to its various merits: (a) low-pressure loss, (b) curved nozzle wall, (c) adjustable aspect ratio. The experimental results show that the Gaussian shape performs better in improving the filament homogeneity as compared to rectangular nozzle and trapezoidal nozzle at corners under small tool path radius (R ≤ 30).
DOI: 10.32657/10356/147630
Schools: School of Mechanical and Aerospace Engineering 
Rights: This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
Fulltext Permission: embargo_20260407
Fulltext Availability: With Fulltext
Appears in Collections:MAE Theses

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