RESUMO
The interest in research and development for additive manufacturing (AM) processes has grown significantly over the last years and attracts both industry and academia alike. Among the available AM technologies, stereolithography (SLA) is one of the most discussed, researched, and employed. On the other hand, being based on thermoset resins, all the limitations of this typology of materials still apply, limiting the range of applications of this highly versatile process. To overcome these limitations, especially brittleness, this research analyzes the effects of Tungsten (W) micro-size (average size 1 µm) particles reinforcement on a methacrylate base material. First, the manufacturing process for creating the W-reinforced methacrylate material is presented and investigated to define the effect of pre- and post-processing operations on the quality of the pre-cured solution considering 4% and 10% wt. W particles concentrations. Afterward, tensile, compressive, and impact specimens were manufactured with both concentrations and compared with the experimental results from clear (unfilled) resin-based specimens used as the benchmark. The addition of tungsten particles showed a strong improvement in the impact strength of the methacrylate base material, quantified in 28% for the 4% and 55% for the 10% wt., respectively, although at the expense of a slight reduction in elastic and yield properties on average -12%. Furthermore, using Scanning Electron Microscope (SEM) analyses, the particle-matrix interaction was investigated, showing the interaction between the polymer matrix and the reinforcement and the mechanism by which the impact resistance is enhanced.
RESUMO
Fiber reinforcement orientation in thermoplastic injection-molded components is both a strength as well as a weak point of this largely employed manufacturing process. Optimizing the fiber orientation distribution (FOD) considering the shape of the part and the applied loading conditions allows for enhancing the mechanical performances of the produced parts. Henceforth, this research proposes an algorithm to identify the best injection gate (IG) location/s starting from a 3D model and a user-defined load case. The procedure is composed of a first Visual Basic Architecture (VBA) code that automatically sets and runs Finite Volume Method (FVM) simulations to find the correlation between the fiber orientation tensor (FOT) and the IG locations considering single and multiple gates combinations up to three points. A second VBA code elaborates the results and builds a dataset considering the user-defined loading and constraint conditions, allowing the assignment of a score to each IG solution. Three geometrical components of increasing complexity were considered for a total of 1080 FVM simulations and a total computational time of ~390 h. The search for the best IG location has been further expanded by training a Machine Learning (ML) model based on the Gradient Boosting (GB) algorithm. The training database (DB) is based on FVM simulations and was expanded until a satisfactory prediction accuracy higher than 90% was achieved. The enhancement of the local FOD on the critical regions of three components was verified and showed an average improvement of 26.9% in the stiffness granted by a high directionality of the fibers along the load path. Finite element method (FEM) simulations and laboratory experiments on an industrial pump housing, injection-molded with a polyamide-66 reinforced with 30% of short glass fibers (PA66-30GF) material were also carried out to validate the FVM-FEM simulation frame and showed a 16.4% local stiffness improvement in comparison to the currently employed IG solution.