Title:
Numerical Modelling and Data-Driven Approaches as Valuable Tools for the Development of New 3D Printable Inks
Author(s):
Giacomo Rizzieri
Publication:
Web Session
Volume:
ws_F23_GiacomoRizzieri.pdf
Issue:
Appears on pages(s):
Keywords:
DOI:
Date:
10/29/2023
Abstract:
Additive manufacturing (AM) techniques are increasingly being adopted in the construction industry to create optimized and aesthetically pleasing geometries, reduce material waste, and accelerate the construction process. Extrusion-based AM technologies, such as 3D Concrete Printing (3DCP), have been applied to a multitude of small-to-medium scale projects and even more relevant applications are expected in the years to come. However, 3DCP is a complex process and its applicability is still limited by the fact that a lot of experimentation and trial-and-error procedures are often necessary, in the practice, to obtain high-quality results. The reason is that many variables can affect the outcome of the print, such as the material properties (which vary in time), the printing parameters (nozzle shape, printing velocity, extrusion velocity, toolpath etc.), the object design and the environmental conditions (temperature and humidity). This contribution explores the use of numerical models and data-driven approaches to assist the designer in the fundamental tasks of the development of new 3D printable cementitious inks. An artificial neural network, trained on a continuously updated database, is exploited to get a first estimate of the expected rheological parameters for a given mix design. Successively, a fluid FEM numerical model is employed to further calibrate the early-age rheological response of the material and optimize the printing parameters with respect to the desired filament geometry. The proposed methodology is not intended to substitute experimental tests, but it should rather be used in combination with practical approaches to speed up design times and gain a deeper knowledge of the printing process.