Search results

This papers aims to study lattice structures in terms of geometric variables, manufacturing variables and material-based variants and their correlation with compressive behaviour for their application in a methodology for the design and development of personalized elastic therapeutic products.

Lattice samples were designed and manufactured using extrusion-based additive manufacturing technologies. Mechanical tests were carried out on lattice samples for elasticity characterization purposes. The relationships between sample stiffness and key geometric and manufacturing variables were subsequently used in the case study on the design of a pressure cushion model for validation purposes. Differentiated areas were established according to patient’s pressure map to subsequently make a correlation between the patient’s pressure needs and lattice samples stiffness.

A substantial and wide variation in lattice compressive behaviour was found depending on the key study variables. The proposed methodology made it possible to efficiently identify and adjust the pressure of the different areas of the product to adapt them to the elastic needs of the patient. In this sense, the characterization lattice samples turned out to provide an effective and flexible response to the pressure requirements.

This study provides a generalized foundation of lattice structural design and adjustable stiffness in application of pressure cushions, which can be equally applied to other designs with similar purposes. The relevance and contribution of this work lie in the proposed methodology for the design of personalized therapeutic products based on the use of individual lattice structures that function as independent customizable cells.

This research aims to provide a innovative class of shape-memory-responsive cellulosic composites (RCC) for 4D printing, enabling self-activated, reversible shape morphing. By integrating experimental, theoretical, and computational modeling, the study optimizes material behavior, offering precise curvature predictions for advanced biomedical and pharmaceutical applications.

This study presents an innovative class of shape–memory–responsive cellulosic composites (RCC), with a unique combination of starch and Affnisol^TM. RCC-mediated filaments were used to print single-layer strips using fused deposition modeling 3D printing technology. The printed single-layer strip exhibited reversible, contactless and self-activated shape morphing in response to swelling and heat. The programming stage involves the swelling and heating of the composite strip and subsequent shape recovery through heating. The shape deformation during the self-activated programming stage was both estimated and predicted using simple experimental, theoretical and computational tools. The study was conducted at different thicknesses (1.5, 2.0 and 2.5 mm) and temperatures (25°C and 37°C) to validate the performance of the developed model in predicting bending curvature.

The developed model showed less than a 13.96 % difference in curvature predicted using theoretical and experimental modeling at studied temperatures. At lower thicknesses, the model can predict the bending curvature with less than a 2.0 % difference in curvature. These RCC materials exhibited potential reversible 4D printing capacity and satisfied the adopted approaches and modeling to forecast the bending curvature for reversible 4D printing.

This study introduces a new class of composite materials for potential 4D applications and provides simple predictive models to forecast bending curvature in reversible 4D printing.

The purpose of this study is to investigate the bending behaviour of three-dimensional (3D) thermoplastic polyurethane (TPU) structures printed onto the fabric.

TPU parts with varying infill patterns and raster angles were 3D-printed onto both woven and knitted fabrics. The resulting hybrid structures’ bending behaviours were evaluated using three test methods: cantilever bending, three-point bending and circular compression. Besides, both sides of the hybrid structures were tested to capture the influence of test direction.

The fabric structure is effective on adhesion force and greater values were observed for woven fabrics. The infill structures, raster angle and test directions were observed effective on the bending behaviour of the hybrid structures. The 45° raster angle resulted in greater bending resistance in three test methods. For knitted fabric structures, gyroid infill generally exhibits superior bending resistance. A case of fabricating a personal elbow brace for cubital tunnel syndrome was also introduced.

This study provides experimental information about the effects of 3D printing parameters on the bending behaviour of the hybrid structures and supports the development of special-purpose designs with tailored functionalities for various applications.

This paper aims to investigate the potential for 4D deformation of the smallest building blocks of the material extrusion additive manufacturing (MEAM) process: single extrudates produced with a single material. In contrast to previous 4D printing approaches where property-variations are realised across multiple layers or with complex composites, this study hypothesises that residual strain varies from top-to-bottom within a single printed extrudate and that this offers an opportunity to achieve controllable 4D printing with the smallest possible resolution (single lines in a single layer).

The influences of bed temperature, printing temperature, printing speed, extrusion width, extrusion thickness and activation temperature are quantified in terms of residual strain and 4D curvature.

An almost fourfold variation in curvature was achieved, printing speed and layer thickness greatly affected 4D deformation: the maximum curvature was increased by >600% compared to the minimum curvature when varying printing speed. In addition to rigorous parametric characterisation, a case study demonstrates the 4D deformation of a flat single-layer lattice into a 3D self-formed stent structure comprised of intricate single-extrudate struts. A separate case study demonstrates the resilience of the method by showing results to translate to alternative materials, with alternative printing hardware and with a different 4D activation procedure.

This study successfully proves a new way to achieve intricate 3D structures with the MEAM process, which would be impossible without 4D deformation due to their intricacy and the need for support material. The findings are also relevant to research into undesired warping due to the quantification of residual strain.

Predicting dimensional and geometrical errors in 3D printing parts during the design stage can significantly enhance the product’s quality. This study aims to predict the form deviation and process capability in additive manufacturing (AM) specimens considering layer thickness, laser power and scan speed parameters in the laser powder bed fusion (LPBF) method. Various machine learning (ML) techniques are implemented to estimate the form deviation and process capability with the highest accuracy in 3D-printed cylindrical parts as a case study.

The workflow started by simulating the LPBF AM process using a finite element modeling approach. Then, different ML algorithms like artificial neural networks are used to predict the form deviation. The process capability value is forecasted using some classification ML models and process capability indices (PCIs) for cylindrical parts. Finally, concentricity tolerance classification is performed for cylindrical parts, which can ensure quality control issues in the production stage.

Results present an accuracy of about 93% for predicting form deviations and 95% accuracy for predicting PCI C_pm in PCI classification based on random forest model as an ML algorithm.

The noteworthy point of the research is accessing the form deviation due to AM and process capability evaluation in the AM process before the production stage, which has not been studied before based on the author’s knowledge. So that the product quality is evaluated based on the shape deviation and its tolerances in the AM process digital chain.

This paper aims to present an innovative approach to fabricating an electrically responsive shape memory polymer (SMP).

Polymers that change shape over time when a stimulus is applied are known as SMPs. It uses polylactic acid (PLA) as the base material and carbon nanotubes (CNTs) as conductive additives. Instead of blending CNT with PLA, they were coated on the surface of the samples. The coating consisted of a mixture of CNT/ polycaprolactone. The samples were made using fused deposition modeling, an additive manufacturing method and the shape memory properties of the samples were analyzed under various parameters, including infill angle, coating layers and applied voltage. The voltage generates the Joule heating effect and results in the recovery of SMP to the original shape.

The highest conductivity of samples belongs to three coating layers, whose conductivity is equal to 0.51 S/cm. Under different parameter settings, the highest recorded shape recovery ratio reached 71.47% at voltage 60, infill angle 0 / 90 and two coating layers. This value emphasizes the remarkable ability of the developed material to return to its original shape. Furthermore, the maximum shape recovery speed observed was 0.3593 degree/s, providing valuable information about shape recovery speed under optimal conditions.

This paper presents the surface coating method and the effects of process parameters for activating shape memory using electric current. Compared to previous techniques, this method offers higher speed and requires less material, making it suitable for use in various industries.

The purpose of this paper is to develop a reliable model that would predict the compressive strength of slurry infiltrated fiber concrete (SIFCON) modified with various supplementary cementitious materials (SCMs) using artificial intelligence approach.

This study engaged the artificial intelligence to predict the compressive strength of SIFCON through deep neural networks (DNN), artificial neural networks, linear regression, regression trees, support vector machine, ensemble trees, Gaussian process regression and neural networks (NN). A thorough data set of 387 samples was gathered from relevant studies. Eleven variables (cement, silica fume, fly ash, metakaolin, steel slag, fine aggregates, steel fiber fraction, steel fiber aspect ratio, superplasticizer, water to binder ratio and curing ages) were taken as input to predict the output (compressive strength). The accuracy and reliability of the developed models were assessed using a variety of performance metrics.

The results showed that the DNN (11-20-20-20-1) predicted the compressive strength of SIFCON better than the other algorithms with R² and mean square error yielding 95.89% and 8.07. The sensitivity analysis revealed that steel fiber, cement, silica fume, steel fiber aspect ratio and superplasticizer are the most vital variables in estimating the compressive strength of SIFCON. Steel fiber contributed the highest value to the SIFCON’s compressive strength with 16.90% impact.

This is a novel technique in predicting the compressive strength of SIFCON optimized with different SCMs using supervised learning algorithms, improving its quality and performance.