Development of Strategies for Controlled Additive Manufacturing

Additive Manufacturing (AM), also known as 3D printing, and commonly utilized for fabricating Rapid Prototypes (RP), fixtures, and functional parts, comprises diverse technologies that enable the creation of objects from three-dimensional digital CAD (Computer Aided Design) models. A widely employed AM technique, based on the extrusion process, is the Fused Filament Fabrication (FFF) or Fused Deposition Modeling (FDM), which offers several advantages, among these highly competitive costs. However, this process is complex and involves a chain of activities ranging from digital design to post-processing, where the final properties of the component are dependent on a substantial number of factors. Notwithstanding its popularity, this technology is still considered highly empirical, necessitating calibration and an experimental optimization of printing parameters. Furthermore, the internal structure of FFF parts, formed by deposited filaments layer-by-layer, results in the generation of anisotropy and surface and dimensional defects. It is not simple to consistently obtain the desired characteristics due to the complexity of the physical phenomena controlling the process (temperature, flow mechanics, solidification). This directly affects critical specifications such as surface roughness (a micro-geometric error), and dimensional and geometric tolerances (parallelism, perpendicularity, roundness, etc.).
The proposed project considers the development of strategies for controlled manufacturing in FFF fabrication, particularly focusing on predicting roughness properties, as well as evaluating dimensional and geometric tolerances. Utilizing a systemic and novel approach, the project proposes to investigate the capabilities of FFF technology, planning for specific values of roughness, dimensions, and geometry, addressing metrological aspects and relying on statistical techniques for design of experiments, machine learning, and computational modelling and simulation.
The expected results of the project would permit the definition of clear strategies for product design, facilitating the controlled generation of surface textures (roughness, waviness) and the precise determination of the values for dimensional and geometric tolerances. The initial and high-impact practical application of the project is focused on the development of inclusive didactic materials for the teaching of STEM disciplines, particularly mathematics. The 3D-printed models offer a unique opportunity by being intuitive and easy to comprehend through both sight and touch, which enhances learning opportunities for individuals who are blind, low-vision, or have perceptual disabilities, thereby promoting an inclusive educational environment.

Design strategies to obtain controlled roughness and dimensional properties for the additive manufacturing process.

pendiente