Search results

The purpose of this study is to evaluate the 3D printability of a multimaterial, fully self-supporting auxetic structure. This will contribute to expanding the application of additive manufacturing (AM) to new products, such as automotive suspensions.

An experimental approach for sample fabrication on a multiextruder 3D printer and characterization by compression testing was conducted along with numerical simulations, which were used to support the design of different auxetic configurations for the jounce bumper.

The effect of stacking different auxetic cell modules was discussed, and the findings demonstrated that a one-piece printed structure has a better performance than one composed of multiple single modules stacked on top of each other.

The quality of the 3D printing process affected the performance of the final components and reproducibility of the results. Therefore, researchers are encouraged to further study component fabrication optimization to achieve a more reliable process.

This research work can help improve the manufacturing and functionality of a critical element of automotive suspension systems, such as the jounce bumper, which can efficiently reduce noise, vibration and harshness by absorbing impact energy.

In previous research, auxetic structures for the application of jounce bumpers have already been suggested. However, to the best of the authors’ knowledge, in this work, an AM approach was used for the first time to fabricate multimaterial auxetic structures, not only by co-printing a flexible thermoplastic polymer with a stiffer one but also by continuously extruding multilevel structures of auxetic cell modules.

The purpose of this study is to explore the synthesis of novel conductive photo-resins to produce flexible conducting composites for use in additive manufacturing. By using direct ink writing (DIW) additive manufacturing, this study aims to explore the fabrication of multimaterial devices with conductive and insulating components. Using digital light processing (DLP) additive manufacturing, this study aims to fabricate detailed objects with higher resolution than material extrusion 3D printing systems.

In this paper, several photocurable conducting resins were prepared for DIW and DLP additive manufacturing. These resins were then cured using 405 nm near UV light to create intrinsically conductive polymer (ICP) composites. The electrochemical properties of these composites were analysed, and the effect of co-monomer choice and crosslinking density was determined. These results determined a suitable resin for subsequent additive manufacture using DIW and DLP. These 3D printing techniques were used to develop flexible conducting devices of submillimetre resolution that were fabricated with unmodified, commercially available 3D printers.

Cyclic voltammetry and volume conductivity analysis of the conducting resins determined the most conductive resin formula for 3D printing. Conductive devices were fabricated using the two 3D printing techniques. A multimaterial soft conducting device was fabricated using DIW, and each conducting component was insulated from its neighbours. DLP was used to fabricate a soft conducting device with good XY resolution with a minimum feature size of 0.2 mm. All devices were prepared in unmodified commercially available 3D printers.

These findings have value in the development of soft robotics, artificial muscles and wearable sensors. In addition, this work highlights techniques for DIW and DLP additive manufacturing.

Several original conducting resin formulae were developed for use in two 3D printing systems. The resulting 3D-printed composites are soft and flexible while maintaining their conductive properties. These findings are of value to both polymer chemists and to the field of additive manufacturing.

Topology optimization of structures under self-weight loading is a challenging problem which has received increasing attention in the past years. The use of standard formulations based on compliance minimization under volume constraint suffers from numerous difficulties for self-weight dominant scenarios, such as non-monotonic behaviour of the compliance, possible unconstrained character of the optimum and parasitic effects for low densities in density-based approaches. This paper aims to propose an alternative approach for dealing with topology design optimization of structures into three spatial dimensions subject to self-weight loading.

In order to overcome the above first two issues, a regularized formulation of the classical compliance minimization problem under volume constraint is adopted, which enjoys two important features: (a) it allows for imposing any feasible volume constraint and (b) the standard (original) formulation is recovered once the regularizing parameter vanishes. The resulting topology optimization problem is solved with the help of the topological derivative method, which naturally overcomes the above last issue since no intermediate densities (grey-scale) approach is necessary.

A novel and simple approach for dealing with topology design optimization of structures into three spatial dimensions subject to self-weight loading is proposed. A set of benchmark examples is presented, showing not only the effectiveness of the proposed approach but also highlighting the role of the self-weight loading in the final design, which are: (1) a bridge structure is subject to pure self-weight loading; (2) a truss-like structure is submitted to an external horizontal force (free of self-weight loading) and also to the combination of self-weight and the external horizontal loading; and (3) a tower structure is under dominant self-weight loading.

An alternative regularized formulation of the compliance minimization problem that naturally overcomes the difficulties of dealing with self-weight dominant scenarios; a rigorous derivation of the associated topological derivative; computational aspects of a simple FreeFEM implementation; and three-dimensional numerical benchmarks of bridge, truss-like and tower structures.

The purpose of this paper is to design and manufacture an intelligent 3D printed sensor to monitor the re-occurrence of diaphragmatic hernia (DH; after surgery) in bovines as an Internet of Things (IOT)-based solution.

The approach used in this study is based on a bibliographic analysis for the re-occurrence of DH in the bovine after surgery. Using SolidWorks and ANSYS, the computer-aided design model of the implant was 3D printed based on literature and discussions on surgical techniques with a veterinarian. To ensure the error-proof design, load test and strain–stress rate analyses with boundary distortion have been carried out for the implant sub-assembly.

An innovative IOT-based additive manufacturing solution has been presented for the construction of a mesh-type sensor (for the health monitoring of bovine after surgery).

An innovative mesh-type sensor has been fabricated by integration of metal and polymer 3D printing (comprising 17–4 precipitate hardened stainless steel and polyvinylidene fluoride-hydroxyapatite-chitosan) without sacrificing strength and specific absorption ratio value.

There are few reports that evolutional topology optimization methods are applied to the conductor geometry design problems. This paper aims to propose an evolutional topology optimization method is applied to the conductor design problems of an on-chip inductor model.

This paper presents a topology optimization method for conductor shape designs. This method is based on the normalized Gaussian network-based evolutional on/off topology optimization method and the covariance matrix adaptation evolution strategy. As a target device, an on-chip planer inductor is used, and single- and multi-objective optimization problems are defined. These optimization problems are solved by the proposed method.

Through the single- and multi-objective optimizations of the on-chip inductor, it is shown that the conductor shapes of the inductor can be optimized based on the proposed methods.

The proposed topology optimization method is applicable to the conductor design problems in that the connectivity of the shapes is strongly required.

The purpose of this paper is to investigate the process of fused filament fabrication (FFF) of a compliant gripper (CG) using thermoplastic polyurethane (TPU) material. The paper studies the applicability of different CG designs and the efficiency of some design parameters.

After reviewing a number of different papers, two designs were selected for a number of exploratory experiments. Using design of experiments (DOE) techniques to identify important design parameters. Finally, the efficiency of the parts was investigated.

The research finds that a simpler design sacrifices some effectiveness in exchange for a remarkable decrease in production cost. Decreasing infill percentage of previous designs and 3D printing them, out of TPU, experimenting with different parameters yields functional products. Moreover, the paper identified some key parameters for further optimization attempts of such prototypes.

The cost of conducting FFF experiments for TPU increases dramatically with product size, number of parameters studied and the number of experiments. Therefore, all three of these factors had to be kept at a minimum. Further confirmatory experiments encouraged.

This paper addresses an identified need to investigate applications of FFF and TPU in manufacturing functional efficient flexible mechanisms, grippers specifically. While most research focused on designing for increased performance, some research lacks discussion on design philosophy, as well as manufacturing issues. As the needs for flexible grippers vary from high-performance grippers to lower performance grippers created for specific functions/conditions, some effectiveness can be sacrificed to reduce cost, reduce complexity and improve applicability in different robotic assemblies and environments.

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.

In recent years, additive manufacturing (AM) has started to be used for manufacturing real functional parts and assemblies for critical applications in aerospace, automotive, and machinery industries. Most complex or assembled parts require internal features (IF) such as holes, channels, slots, or guides for locational and mating requirements. Therefore, it is critical to understand and compare the structural and mechanical properties of additively manufactured and conventionally machined IFs.

In this study, mechanical and microstructural properties of Inconel 718 (Inc718) alloy internal features, manufactured either as-built with AM or machining of additively manufactured (AMed) part thereafter were investigated.

The results showed that the average ultimate tensile strength (UTS) of additively manufactured center internal feature (AM-IF) is almost analogous to the machined internal feature (M-IF). However, the yield strength of M-IF is greater than that of AM-IF due the greater surface roughness of the internal feature in AM-IF, which is deemed to surpass the effect of microstructure on the mechanical performance. The results of digital image correlation (DIC) analysis suggest that AM-IF and M-IF conditions have similar strain values under the same stress levels but the specimens with as built IF have a more locally ductile region around their IF, which is confirmed by hardness test results. But this does not change global elongation behavior. The microstructural evolution starting from as-built (AB) and heat-treated (HT) samples to specimens with IF are examined. The microstructure of HT specimens has bimodal grain structure with d phase while the AB specimens display a very fine dendritic microstructure with the presence of carbides. Although they both have close values, machined specimens have a higher frequency of finer grains based on SEM images.

It was shown that the concurrent creation of the IF during AM can provide a final part with a preserved ultimate tensile strength and elongation but a decreased yield strength. The variation in UTS of AM-IF increases due to the surface roughness near the internal feature as compared to smooth internal surfaces in M-IF. Hence, the outcomes of this study are believed to be valuable for the industry in terms of determining the appropriate production strategy of parts with IF using AM and postprocessing processes.

Porosity is a commonly analyzed defect in the laser-based additive manufacturing processes owing to the enormous thermal gradient caused by repeated melting and solidification. Currently, the porosity estimation is limited to powder bed fusion. The porosity estimation needs to be explored in the laser melting deposition (LMD) process, particularly analytical models that provide cost- and time-effective solutions compared to finite element analysis. For this purpose, this study aims to formulate two mathematical models for deposited layer dimensions and corresponding porosity in the LMD process.

In this study, analytical models have been proposed. Initially, deposited layer dimensions, including layer height, width and depth, were calculated based on the operating parameters. These outputs were introduced in the second model to estimate the part porosity. The models were validated with experimental data for Ti6Al4V depositions on Ti6Al4V substrate. A calibration curve (CC) was also developed for Ti6Al4V material and characterized using X-ray computed tomography. The models were also validated with the experimental results adopted from literature. The validated models were linked with the deep neural network (DNN) for its training and testing using a total of 6,703 computations with 1,500 iterations. Here, laser power, laser scanning speed and powder feeding rate were selected inputs, whereas porosity was set as an output.

The computations indicate that owing to the simultaneous inclusion of powder particulates, the powder elements use a substantial percentage of the laser beam energy for their melting, resulting in laser beam energy attenuation and reducing thermal value at the substrate. The primary operating parameters are directly correlated with the number of layers and total height in CC. Through X-ray computed tomography analyses, the number of layers showed a straightforward correlation with mean sphericity, while a converse relation was identified with the number, mean volume and mean diameter of pores. DNN and analytical models showed 2%–3% and 7%–9% mean absolute deviations, respectively, compared to the experimental results.

This research provides a unique solution for LMD porosity estimation by linking the developed analytical computational models with artificial neural networking. The presented framework predicts the porosity in the LMD-ed parts efficiently.

This study aims to develop a four-dimensional (4D) textile composite that self-forms upon thermal stimulation while eliminating thermomechanical programming steps by using fused deposition modeling (FDM) 3D printing technology, and tries to refine the product development path for this composite.

Polylactic acid (PLA) printing filaments were deposited on prestretched Lycra-knitted fabric using desktop-level FDM 3D printing technology to construct a three-layer structure of thermally responsive 4D textiles. Subsequently, the effects of different PLA thicknesses and Lycra knit fabric relative elongation on the permanent shape of thermally responsive 4D textiles were studied. Finally, a simulation program was written, and a case in this study demonstrates the usage of thermally responsive 4D textiles and the simulation program to design a wrist support product.

The constructed three-layer structure of PLA and Lycra knitted fabric can self-form under thermal stimulation. The material can also achieve reversible transformation between a permanent shape and multiple temporary shapes. Thinner PLA deposition and higher relative elongation of the Lycra-knitted fabric result in the greater curvature of the permanent shape of the thermally responsive 4D textile. The simulation program accurately predicted the permanent form of multiple basic shapes.

The proposed method enables 4D textiles to directly self-form upon thermal, which helps to improve the manufacturing efficiency of 4D textiles. The thermal responsiveness of the composite also contributes to building an intelligent human–material–environment interaction system.