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Three-dimensional printing has experienced steady growth across various industries, but its application in the production of biomedical devices and components is hindered by the lack of materials with suitable mechanical properties and controlled bioactivity. Most polymers have limitations in terms of their strength and bioactivity, necessitating the incorporation of special features to overcome these shortcomings. Among these polymers are polylactic acid (PLA) and polypropylene (PP), which are highly reusable materials. Zeolite, a widely available natural material, has the potential to address these bioactivity and strength limitations. Therefore, this study focuses on the development of composite materials, specifically PLA + zeolite and PP + zeolite, doped with Cu nanoparticles. The goal is to obtain composite materials capable of effective application in the biomedical field through 3D manufacturing, providing an efficient, cost-effective, and non-toxic alternative. The methodology employed involved extrusion manufacturing, followed by the validation of zeolite dispersion in PP and PLA through optical microscopy and the analysis of zeolite morphology using scanning electron microscopy. The mechanical response of the materials was determined through nanoindentation tests and tensile experiments. The results obtained revealed an appropriate dispersion of zeolite in both polymer matrices. Nanoindentation tests highlighted significant anisotropy and an auxetic response in zeolite. However, virtual tests demonstrated a high precision between submicrometric and macrometric scales in the composite materials. Tensile tests indicated a substantial increase in the stiffness of the composite upon zeolite incorporation, with a 21.48% increase in the case of the PLA matrix compared to PLA without zeolite, and a 19.12% increase in the case of PP compared to PP without zeolite. These results hold promise for the fabrication of filaments intended for additive manufacturing processes in the biomedical field, offering an accessible and effective solution. [ABSTRACT FROM AUTHOR]

Additive manufacturing of composite materials is a promising technology. It could solve one of the most critical drawbacks of 3D-printed fiber-reinforced thermoplastics: their low out-of-plane mechanical properties. Due to this factor, it is still unknown how most design and manufacturing parameters affect the out-of-plane properties of composite materials. As a solution, this paper proposes an experimental methodology to characterize out-of-plane printed composite materials. For this purpose, existing standards for traditionally fabricated composites are adapted, investigated, and validated for 3D-printed laminates reinforced with long fibers using the fused filament fabrication technique. Consequently, the methodology is employed to study the impact of stacking sequence and heat treatment conditions on the composites' out-of-plane mechanical properties. The main results showed that increasing the thickness between stacking layers increases the mechanical response due to reducing the number of fiber/matrix interfaces and, consequently, the reduction of porosity. Compared to the initial sample, a heat treatment at 175 °C for 6 h increased the interfacial strength by 101.09% and reduced the porosity in the fiber produced by the additive manufacturing process by 72%. [ABSTRACT FROM AUTHOR]

This work evaluated the phase prediction capability of high entropy alloys using four supervised machine learning models K-Nearest Neighbors (KNN), Multinomial Regression, Extreme Gradient Boosting (XGBoost), and Random Forest. The study addresses the challenge of predicting multicomponent alloys by considering the overlapping of multicategorical stability parameters. Eight prediction classes (FCC, BCC, FCC+BCC, FCC+Im, BCC+Im, FCC+BCC+Im, Im and AM) were used. Finally, the predicted results were compared with those of two new alloys fabricated by induction melting in a controlled atmosphere using X-ray diffraction (XRD). The results indicate that with a robust database, appropriate data treatment, and training, satisfactory and competitive prediction indicators can be obtained with traditional machine learning predictions based on four prediction classes: Solid Solution (SS), Solid Solution with Intermetallic (SS+Im), intermetallic (Im), and amorphous (AM). The best predictive model obtained from the four evaluated models was Random Forest, with an accuracy of 72.8% and ROC AUC of 93.1%. • Machine learning was applied to 2434 experimental data for phase prediction. • The best evaluated model is Random Forest with 72.8% Accuracy and 93.1% ROC AUC. • An adequate prediction was found for two new HEAs obtained experimentally. • The packing factor may be the key to the increase in HEA prediction performance. [ABSTRACT FROM AUTHOR]

3D-printed thermoplastic parts with continuous fiber reinforcement are known to offer mechanical performance that is highly dependent on design variables and printing parameters. In this work, the role of the reinforcement distribution on the mechanical response of glass fiber-reinforced thermoplastics printed using the fused filament fabrication (FFF) technique is evaluated. Laminates with alternating and continuous reinforcement architecture as well as different fiber orientations such as isotropic (0°, 90°, and 45°) and concentric configurations are characterized from monotonic tensile and flexural loads. The resulting superior macromechanical performance in terms of higher stiffness and strength achieved in samples reinforced with alternating fiber plies is correlated with the micromechanical fractography characteristics and interlaminar shear capacity. [ABSTRACT FROM AUTHOR]

The manufacturing processes and design of metal and alloy products can be performed over a wide range of strain rates and temperatures. To design and optimize these processes using computational mechanics tools, the selection and calibration of the constitutive models is critical. In the case of hazardous and explosive impact loads, it is not always possible to test material properties. For this purpose, this paper assesses the efficiency and the accuracy of different architectures of ANNs for the identification of the Johnson–Cook material model parameters. The implemented computational tool of an ANN-based parameter identification strategy provides adequate results in a range of strain rates required for general manufacturing and product design applications. Four ANN architectures are studied to find the most suitable configuration for a reduced amount of experimental data, particularly for cases where high-impact testing is constrained. The different ANN structures are evaluated based on the model's predictive capability, revealing that the perceptron-based network of 66 inputs and one hidden layer of 30 neurons provides the highest prediction accuracy of the effective flow stress–strain behavior of Ti64 alloy and three virtual materials. [ABSTRACT FROM AUTHOR]

The objective of this paper is to determine the impact of density fraction on the mechanical properties of 3D printed thermoplastic materials to maximize the mechanical strength of parts through a combination of topology optimization and tailored density fraction. The results obtained reveal a significant finding of the applicability of the strategy for improving additive manufacturing processes, design automation, and manufacturing of high-strength components. The effect of infill density and print orientation on the mechanical properties of PLA-based 3D-printed parts are quantified by ultimate strength and Young's modulus in two material orientations (in the printing direction and out of the printing plane), employing both experimental and simulated uniaxial tensile tests, a polynomial relationship as a function of density fraction is determined. These data are then used in a series of successive topological optimizations of a part subjected to operational loads that maximize strength with a targeted reduction in the overall mass. The resulting optimized part exhibits a discrete density fraction distribution from the overlapped topologies that allows the total mass of the original part to be maintained with a 258% increase in load-carrying capacity before failure and a mere 23% increase in fabrication time by fused deposition modeling. These findings underscore the promise of leveraging advanced 3D printing methods and topology optimization to enhance the strength and efficiency of manufactured components. [ABSTRACT FROM AUTHOR]

This work addresses the development of compressed earth blocks with high quality using stabilizers and reinforcements of industrial and agroindustrial by-products. A critique of the production of compressed earth blocks using new mixtures, compositions, and characteristics required for adequate performance according to different construction standards is included. Furthermore, the effect of adding stabilizers and reinforcements from industrial and agro-industrial by-products on strength, durability and thermal insulation is evaluated. Fibers improve tensile and crack resistance while stabilizers enhance cohesion of mixtures, originating new compounds with higher compressive strength and lower permeability. Finally, the main prediction tools for engineering design and application of compressed earth blocks in building are discussed. The main findings and research gaps identified provide a baseline for future research projects focused on the transition to a low-carbon future through the production of compressed earth block. [Display omitted] • Latest developments in compressed earth blocks (CEBs). • CEB reinforcements and stabilizers for economical and sustainable construction. • Soil characterization, thermal, mechanical and durability response for quality CEBs. • Applications and prediction models for CEBs and CEB walls under out-of-plane loads. • Need for new waste-based stabilizers and reinforcements to mitigate carbon footprint. [ABSTRACT FROM AUTHOR]