Document Type
Extended Abstract
Abstract
A major challenge of 3-D printable concrete lies in the design of fresh material properties. Ideally, a fresh concrete needs to be fluid-like for pumping and extrusion but solid-like during placement to support the printed structure. In practice, achieving this balance is challenging. This research addresses these challenges by leveraging machine learning and numerical simulation to model the fresh behavior of cement-based materials for 3-D printing. It begins with rheological characterization to understand how mixture design factors influence rheological properties, laying the foundation for subsequent studies. A multilayer perception (MLP) model is developed to predict the yield stress of cement-based materials, achieving a desired overall accuracy while demonstrating performance variations on some subsets of data. Additionally, both Discrete Element Method (DEM) and Smoothed Particle Hydrodynamics (SPH) are utilized to model the flow behavior of cement-based materials in common flow scenarios. These 3-D printing simulations help us understand real-life issues such as jamming and plastic collapse. Vibration of the material in the printer nozzle was shown to be effective for improving extrusion and consistency during deposition. Machine learning and numerical simulation provided insights into design of 3-D printing materials and construction processes.
Keywords
3D printing concrete, fresh properties, fluid behavior, machine learning, numerical modeling
DOI
10.5703/1288284318054
Modeling the fresh properties of cement-based materials for 3D concrete printing
A major challenge of 3-D printable concrete lies in the design of fresh material properties. Ideally, a fresh concrete needs to be fluid-like for pumping and extrusion but solid-like during placement to support the printed structure. In practice, achieving this balance is challenging. This research addresses these challenges by leveraging machine learning and numerical simulation to model the fresh behavior of cement-based materials for 3-D printing. It begins with rheological characterization to understand how mixture design factors influence rheological properties, laying the foundation for subsequent studies. A multilayer perception (MLP) model is developed to predict the yield stress of cement-based materials, achieving a desired overall accuracy while demonstrating performance variations on some subsets of data. Additionally, both Discrete Element Method (DEM) and Smoothed Particle Hydrodynamics (SPH) are utilized to model the flow behavior of cement-based materials in common flow scenarios. These 3-D printing simulations help us understand real-life issues such as jamming and plastic collapse. Vibration of the material in the printer nozzle was shown to be effective for improving extrusion and consistency during deposition. Machine learning and numerical simulation provided insights into design of 3-D printing materials and construction processes.