ss

Hot Keywords
Dielectric elastomer actuators

Top
Soft Sci 2022;2:2. 10.20517/ss.2021.22 © The Author(s) 2022.
Open Access Review Article

A brief review of mechanical designs for additive manufactured soft materials

1State Key Laboratory of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, Jiangsu, China.

2Beijing Key Laboratory of Lightweight Multi-functional Composite Materials and Structures, Beijing Institute of Technology, Beijing 100081, China.

3State Key Laboratory of Explosion Science and Technology, Beijing Institute of Technology, Beijing 100081, China.

*Correspondence to: Prof. Qiang Zhang, State Key Laboratory of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics, No.29 Yudao Street, Nanjing 210016, Jiangsu, China.E-mail: qiang.zhang@nuaa.edu.cn ; Prof. Zeang Zhao, Beijing Key Laboratory of Lightweight Multi-functional Composite Materials and Structures, Beijing Institute of Technology, No. 5, South Street, Zhongguancun, Haidian District, Beijing 100081, China. E-mail: zza@pku.edu.cn

    Views:1005 | Downloads:273 | Cited:0 | Comments:0 | :3
    Academic Editors: Zhifeng Ren, Cunjiang Yu | Copy Editor: Xi-Jun Chen | Production Editor: Xi-Jun Chen

    © The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, sharing, adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

    Abstract

    Additive manufacturing is an arising technology for soft materials and structures with improved complexity and functionality and has gradually become widespread in various fields, including soft robotics, flexible electronics and biomedical devices. Along with the development of material systems and fabrication techniques, mechanical design principles for additive manufactured soft materials have been greatly developed and evolved in recent years and some unique issues that are distinct from conventional manufacturing techniques have emerged. In this short review, we mainly focus on additive manufactured soft materials that are in significant need of mechanical models/simulations to provide design guidelines; therefore, topics such as soft robotics and electronics are not considered here. We first discuss the mechanical design methods for controlling shape distortions and interfacial strength, as they are directly related to the quality and reliability of additive manufactured soft materials. Design principles and manufacturing strategies for bioinspired composites, which represent a large part of current research on additive manufactured soft materials, are then summarized integrally with regards to three aspects. In addition, basic mechanical considerations for additive manufactured four-dimensional shape-changing structures are explained, together with a review of the recent theories and numerical approaches. Finally, suggestions and perspectives are given for future developments in soft material additive manufacturing.

    INTRODUCTION

    Recent advances in additive manufacturing [or three-dimensional (3D) printing] has greatly broadened the design space and application fields of soft materials[1,2]. Traditional soft structures and devices are generally fabricated by molding[3] or cutting[4], which are only suitable for simple geometries. Other common subtractive manufacturing approaches for stiff materials can easily cause damage when applied to ultrasoft material systems[5]. In additive manufacturing, soft materials are step-by-step piled up with relatively small contact or deflective force, thereby enabling the non-destructive construction of complex soft structures. Benefitting from the discrete manufacturing process, it is also possible to spatially control the distribution of heterogeneous materials without involving tedious steps. Such advantages make additive manufacturing a promising technique for the direct fabrication of soft robotics[6], flexible electronics[7] and biomedical devices[8]. For example, turtle-inspired robotics consisting of fluidic circuits and interconnects, soft actuators and outer castings were integrally manufactured by multi-material Polyjet printing, and could be actuated by an external pressure source[9]. 3D electronic metamaterials with programmable large-strain piezoresistive and piezoelectric effects were fabricated by combining additive manufacturing with selective metal deposition[10]. Active biomedical devices that can perceive deformation and process signals were additive manufactured using hydrogel inks and bacterial cells[11]. Other typical additive manufactured soft materials and structures include mechanical metamaterials[12], optical devices[13] and microfluidic devices[14].

    Traditional mechanical analysis and characterization of soft materials are generally decoupled from the manufacturing process[15,16]. Before manufacturing, an optimal structure is designed considering the target functionality, structural reliability and manufacturability, which are established by carrying out theoretical analysis or numerical simulations. After the soft structure is manufactured, mechanical experiments and analysis are conducted to examine the structural stiffness, vibration behavior, interfacial strength and so on. In additive manufacturing, where the fabrication process is highly flexible and programmable, the steps of mechanical design have significantly changed[17,18]. Firstly, with the development of multi-material printing techniques, many additive manufactured soft structures can be considered as ready-to-use devices, instead of raw materials or components[19]. The as-printed soft structure has its own functionality, either multifield response or programmed deformation, and does not need to be further assembled with other accessories. Key issues arise in mechanical designs for these additive manufactured soft devices, including, but not limited to, the design of material distribution, microarchitectures, reaction kinetics in manufacturing and the pre-definition of deformation patterns. Secondly, additive manufacturing is a process strongly related to a chemical reaction or phase change, and thus the mechanics during fabrication become more important compared with traditional subtractive manufacturing[20]. Here, the most significant issues in mechanical design include how to control manufacturing-induced shape distortion and ensure high geometric accuracy and how to program and utilize the intrinsic anisotropy of additive manufactured soft materials [Figure 1][21,22].

    Figure 1. Schematic of mechanical design of additive manufactured soft materials based on the four most important aspects [distortion design: how to control the geometric accuracy of additive manufactured soft materials; interface design: how to ensure the interfacial strength and to program the anisotropy of additive manufactured soft materials; composites design: how to manufacture multi-material soft composites; four-dimensional (4D) printing design: how to manufacture 4D shape-changing soft structures].

    Although there are already several review articles on additive manufactured soft materials in the literature, most of them are focused on novel material systems, advanced print strategies and the application of additive manufacturing in different fields[1,2,6,7,12,22-26]. As discussed above, the significance of mechanical design arises concerning the accuracy, and reliability of additive manufactured soft materials. In this article, we briefly review recent progress in mechanical theories and design methods for additive manufactured soft materials. The mechanical designs related to the manufacturing process are reviewed, where we mainly concentrate on how to control geometric accuracy (“distortion design” in Figure 1), how to program material anisotropy and how to improve the interfacial strength (“interface design” in Figure 1) of printed structures. The mechanical design principles for multi-material soft structures that can be treated as structural or functional composites (“composites design” in Figure 1) are summarized. Mechanical design methods of 4D shape-changing soft structures (“4D printing design” in Figure 1) are also discussed. Finally, the perspectives and conclusions are given.

    MECHANICAL DESIGNS FOR SHAPE DISTORTION

    Light-based 3D printing technologies [e.g., stereolithoraphy (SLA)[27], selective laser sintering[28], digital light processing (DLP)[29,30] and two-photon polymerization[31]] and ink-based 3D printing technologies [e.g., inkjet printing[32], fused deposition modelling[33,34] and direct ink writing (DIW)[35,36]] allow for the rapid design and assembly of various soft materials into structures with tunable mechanical, electrical, optical and other functional properties at a wide spectrum of length scales (from ~100 nm to ~300 cm)[26,37,38]. So far, various 3D-printed prototypes have been conceptualized and validated. However, it is often not feasible to directly utilize 3D-printed prototypes to replace their conventionally-manufactured counterparts, especially for industrial products[39]. One of the issues that hinder the practical application of 3D printing lies in the inaccurate geometric shape in the 3D-printed parts due to shape distortion, which is induced by the development of complex internal stress during the layer-by-layer material adding process of printing. There are many process mechanism-dependent sources that can cause internal stress, with the two most common being volume shrinkage and thermal deformation. To efficiently reduce shape distortion and improve shape accuracy in 3D printing, the development of quantitative methods to predict the evolution of internal stress is urgently required.

    In a light-based 3D printing process that solidifies materials through photopolymerization, the internal stress mainly builds with volume shrinking deformation in materials experiencing a transition from liquid to solid[40]. Previous studies have reported that during photopolymerization, some acrylate-based monomers exhibit a ratio of volume shrinkage exceeding 20%[41]. Such a large shrinking deformation can cause severe shape distortion in the printed parts, making them unacceptable for applications where a high-resolution geometric shape is required.

    Shape distortion in the printing method of SLA has been studied since the 1990s. Starting from the pioneering work of Jacobs[42], many researchers have investigated the printing process of SLA[43,44]. Bugeda numerically analyzed the SLA processes using a self-developed finite element code and concluded that the dependence of the curl distortion with respect to the magnitude of the volumetric shrinkage is linear only for short parts and becomes non-linear for longer parts[45]. Wiedemann et al.[46] also performed a numerical analysis on the SLA printing process and revealed a common rule where half the layer thickness doubles the curl. By relating the material modulus and cure shrinkage to the combinations of the averaged energy dose and curing time through several empirical equations, Huang et al.[47] developed a dynamic finite element method to simulate the curl distortion of SLA-printed structures and found that short raster scanning produces less distortion than long raster scanning [Figure 2A]. The dynamic finite element method was later adopted by the same authors in another study in order to optimize the scanning parameters[48].

    Figure 2. Mechanical designs for the shape distortion of additive manufactured soft materials. (A) Schematic of the rezoning technique in the dynamic element method simulation of SLA-printed parts[47]. (B) Schematic and experiment result of the deformation of a two-layer resin laminate[40]. (C) Comparison of the simulated geometry with the printed samples and the target geometry with the printed samples for a T3-shape[49]. (D) Coarse-grained molecular dynamics simulation of continuous liquid interface production and dependence of the shape and radius of the object on the printing speed[52]. (E) Part distortions occurring during the material adding process[57]. (F) Warpage in the FDM (Fused Deposition Modeling) process and warpage evaluation[55].

    In addition to finite element method-based numerical approaches, simplified theoretical models have also been developed for studying shrinkage strains in a SLA-printed part. For example, Karalekas et al.[40] applied elastic lamination theory to determine the magnitude of the resulting shrinkage strains in a laser solidified acrylic-based photopolymer [Figure 2B] and made the conclusions that test specimens post-cured under intense ultraviolet light exhibited a uniform shrinkage, while thermal post-curing resulted in higher shrinkage strains.

    The aforementioned works successfully analyzed the shape distortion in SLA-printed parts and some design rules for optimizing printing parameters were proposed to increase shape accuracy. However, most of these studies only consider shape distortion at the structural element scale, such as a long strip or a thin layer, and ignore the complex features of the dynamics of photopolymerization. A recent study conducted by Westbeek et al.[49] overcame these limitations by developing a multi-physical modeling framework to predict the deformed geometry of SLA-printed parts on a full-component scale. Multiple samples were printed for comparison with the simulation and the results showed that the distorted geometry depends on the irradiation profile and the polymerization and solidification characteristics [Figure 2C][49]. In another study[50], the same authors investigated the SLA printing process where particles are filled in resins by using a multiscale process simulation framework. In this work, the influence of the filler particles on the light scattering, conversion characteristics and resulting effective thermal and mechanical properties can be determined.

    DLP is a printing technique that also uses photopolymerization to solidify materials and the internal stress therefore mainly comes from volume shrinking as well. Different from SLA that patterns one volume element at a time through point-source exposure, the printing method of DLP relies on the dynamic mask patterning technology to illuminate one layer of the liquid resin at a time[29,30]. This greatly speeds up printing but also separates the printing process from the SLA and requires new analytical approaches, as well as new design guidelines to reduce shape distortion. Wu developed a theoretical model where modulus and shrinkage are related to cure to understand the curing process for DLP. A finite element analysis that incorporates the model was then performed to calculate the dynamic evolution of stresses during the layer-by-layer fabrication[51].

    Wang et al.[52] conducted a coarse-grained molecular dynamics simulation to model the continuous liquid interface production (one form of DLP) 3D printing technique [Figure 2D] and simulations showed that the quality of the shape of the 3D-printed objects is determined by a fine interplay between the elastic, capillary and friction forces. The authors identified the source of the object shape deformations and developed a set of rules for calibrating the parameters to meet the accuracy requirements. Very recently, Zhang et al.[53] investigated the volume shrinkage-induced distortion of DLP-printed parts by conducting experiments, theoretical modeling and finite element analysis simulations. Design rules that include adjusting the layer printing time, the layer thickness, as well as the content of photoabsorbers and photoinitiators, were given to reduce shape distortion of DLP-printed structures. For shape distortion induced by the post-curing process, Wu et al.[54] theoretically investigated the shape distortion of DLP 3D-printed structures during ultraviolet post-curing by combining photopolymerization reaction kinetics and the Euler-Bernoulli beam theory. The results showed that the evolution of the mechanical behavior of the printed samples during the post-printing process was dependent on the printing parameters, which provides fundamental understanding for optimizing the final shape of 3D-printed structures[54].

    Another common source of internal stress is thermal deformation, which can occur in 3D printing technologies that require heat to process materials. FDM is one such printing technology and thermal deformation-induced shape distortion is a difficult problem for FDM. FDM printing relies on melting thermoplastic materials by an extruder and then depositing the material onto the printing plate for cooling and solidifying. During a FDM printing process, the material can experience multiple thermal cycles [Figure 2F][55]. After printing (with the part peeled from the building plate), these residual strain drive the printed part to curl at the corners. Warpage in FDM-printed parts can depend on many factors related to material properties, part geometry and process parameters. Towards estimating the warpage built with different process settings, many modeling approaches have been proposed in either one-dimensional (1D), two-dimensional (2D) or 3D frameworks and validated by experimental tests. According to some basic hypotheses and simplifications, Wang et al.[56] proposed a 1D model of the prototype warp deformation and quantitatively analyzed many variables, including the deposition layer number, the stacking section length, the chamber temperature and the material linear shrinkage rate. Moreover, improved methods for reducing the warp deformation were proposed.

    A similar mathematical model was proposed by Zhu et al.[57] to identify the influential parameters that contribute to part distortions and the parameters were used in their experimental designs [Figure 2E]. For 2D models, Liu et al.[58] adopted the theory of elastic thin plates in thermoelasticity to describe the deflection field in the x-y plane and the warpage estimated by the model was verified on 81 test specimens with a portable 3D laser scanner. For more complex problems, Zhang et al.[59,60] conducted a 3D finite element analysis to simulate the mechanical and thermal phenomena in FDM. The method was further used for simulations of residual stress and part distortion. From the simulations, the short-raster tool path results in higher residual stresses and thus possibly larger distortions than the long-raster and alternate-raster patterns. The same authors also conducted a follow-up study to evaluate the effects of the deposition parameters on residual stresses and part distortions, where the parameters include three factors and three levels. Shape distortion has also been analyzed for other polymer-based AM processes by considering specific shrinkage effects, as well as further process-dependent mechanisms. Based on models and tests, design rules are proposed for selective laser sintering[61,62] and binder jetting[63].

    Based on the above discussion, strategies for reducing the degree of shape distortion are highly dependent on the specific process mechanisms. The general goal is to reduce the residual stress during the manufacturing process, which can usually be realized by adjusting the printing conditions, including the light intensity, layer thickness for photopolymerization-based printing, printing speed, printing temperature and printing path for thermal-based printing. The useful strategies for reducing distortion in some soft material-based additive manufacturing techniques are summarized in Figure 3.

    Figure 3. Strategies for reducing shape distortion in various soft material-based additive manufacturing techniques.

    MECHANICAL DESIGNS FOR INTERFACIAL STRENGTH AND ANISOTROPY

    In most additive manufacturing techniques, soft materials are layer-by-layer deposited or part-by-part piled up. This process creates many internal interfaces inside a soft structure, for both single- and multi-material printing. Before printing, mechanical designs should be carried out to eliminate the influence of these interfaces on the final mechanical performance. The existence of internal interfaces also results in macroscopic anisotropy, for example, layer-by-layer printed structures perform similarly to composite laminates. Mechanical designs of the printing path and processing conditions are needed to effectively control this type of anisotropy.

    Weak interfaces in additive manufactured single materials come from inconsistencies in the reaction history or processing sequence. In the FDM printing of polymers or soft composites, thermoplastic precursors are sequentially heated up and deposited onto the structure, during which materials on two sides of a certain interface experience different and periodic thermal histories[64,65]. The strength of such an interface is dependent on molecular diffusion and chain entanglement near the interfacial region[66], which can be quantitatively correlated with the equivalent thermal history of the interface. Thermal viscoelastic models incorporating the Williams-Landel-Ferry equation[67], as well as multiscale thermal mechanical models considering the evolution of polymer chains[68], can be utilized to predict interfacial strength and to optimize the macroscopic thermal history[69]. In DLP-based additive manufacturing, interfaces are created when liquid resin is layer-by-layer cured. Due to the inhomogeneity of the photopolymerization reaction, materials near an internal interface have weaker mechanical properties and sometimes might be as low as half those of the base material[70]. Based on delicate mechanical models considering details of the reaction kinetics, researchers have found that the heterogeneity near the interfacial region can be quantitatively controlled by the printing parameters[71,72] [Figure 4E]. It should be noted that recently developed continuous[73] and volumetric DLP printing techniques[74] have significantly reduced the amount of internal interfaces, but there is still intrinsic heterogeneity inside the soft material. The internal interface in DIW is a combination of the aforementioned two types of interfaces because many DIW systems utilize partially cured precursors that will continue to react after been deposited on the structure[26,75].

    Figure 4. Mechanical designs for the interfacial strength and anisotropy of additive manufactured soft materials. (A) Improving the interfacial strength between chemically dissimilar materials by adding initiators to trigger interfacial reactions[81]. (B) Improving the interfacial strength by selective wavelength irradiation to create interfacial penetration[83]. (C) Improving the interfacial strength with a water-soluble initiator[82]. (D) Improving the interfacial strength by material mixing[79,80]. (E) A weak interface and the origin of macroscopic anisotropy in DLP-printed materials revealed by indentation[71]. (F) Anisotropic failure stress and failure strain of additive manufactured polymers determined from experiment and simulation[94].

    Another important category of interface is that between different materials, which is common in multi-material additive manufactured soft structures. Early multi-material additive manufacturing mainly utilized chemically similar materials, for example, the acrylate resin used in Polyjet printing[76] and DLP printing[77]. Desirable interfacial strength could be obtained, although interlocking structures are sometimes designed to further improve the interface under large deformation[78]. For chemically dissimilar materials, either chemical treatments or physical designs become necessary. The group of Lewis designed a microfluidic nozzle to homogeneously mix different materials and to create gradient interphase regions with both chemical and physical connections [Figure 4D][79,80]. Yang et al.[81] supplied additional initiators into the precursor, which promoted the formation of interfacial covalent bonds during printing [Figure 4A]. Ge et al.[82] used water soluble 2,4,6-trimethylbenzoyl diphenylphosphine oxide to increase the interfacial strength between polymers and hydrogels [Figure 4C]. Dolinski et al.[83] utilized selective wavelength irradiation to create interpenetrating networks within the interphase region, which could be used in solution mask liquid lithography [Figure 4B]. These chemical approaches have the intrinsic drawback that they are only suitable to special material systems.

    For general chemically dissimilar materials, a combination of the chemical and physical approaches would be more helpful. Multi-material interfaces in nature generally have structured geometries and specific geometric parameters exist when the interfacial strength is optimized[84,85]. Roach et al.[86] and Wu et al.[87] employed blunt and buzzsaw interfaces to improve the interfacial strength between elastomers and thermosets that could not be chemically bonded together. Rivera et al.[88] further incorporated laminate designs near the structured interface, by which the interfacial strength of the additive manufactured connection was almost doubled.

    The existence of internal interfaces results in macroscopic anisotropy, even for single-material additive manufactured soft structures[89]. In the DLP printing of polymers, Monzon et al.[90] found that the elastic modulus of vertically-printed materials could be 300 MPa higher than that of horizontally-printed materials. In path-dependent FDM printing, the anisotropy in modulus and failure strength became more apparent[91] but could be intentionally regulated by sequentially aligning the printing path[92]. Many additive manufactured soft structures adopt lightweight porous infills and isotropic lattice designs would be beneficial for such structures[93]. To quantitatively predict the anisotropy of additive manufactured materials, Zhang et al.[94] developed a phenomenological hyperelastic-viscoplastic model that could precisely capture the anisotropic failure stress and failure strain [Figure 4F]. Chen et al.[95] considered the anisotropy arising from porosity evolution to develop a viscoelastic-viscoplastic model for Multi Jet Fusion-printed polymers. Liu et al.[96] incorporated material anisotropy into the topological optimization of self-support structures. For future developments, details of the origin of the internal interface can be included in these theoretical approaches to quantitatively correlate the mechanical behavior with the manufacturing process.

    MECHANICAL DESIGNS FOR BIOINSPIRED COMPOSITES

    Natural materials, such as bone, nacre, wood, silk and hair, have outstanding mechanical performance in resisting impacts and tolerating damage because of their unique microstructure architectures across multiple length scales[97-100]. The building blocks that constitute these materials are rather simple and most of them do not have remarkable mechanical properties. It is instead the complex architectures that endow the materials with superior performance. This has inspired scientists and engineers to emulate designs in natural materials and create novel materials with bioinspired structural features. However, due to the complexity of these architectures, it is difficult for conventional manufacturing techniques (e.g., casting) to reproduce the design patterns observed in natural materials. In contrast, state-of-the-art 3D printing techniques allow digital materials to be placed into almost arbitrary positions in a 3D space at a “voxel” level[39] and have thus become suitable platforms for designing, modeling and manufacturing various bioinspired materials. Currently, 3D printing-enabled bioinspired materials can be roughly classified into three categories, namely, composites reinforced by complex-oriented fibers or particles, composites composed of two material components arranged in a brick block-like layout and composites consisting of gradient material constituents.

    Balsa wood is a cellular natural composite and achieves its remarkable properties from the hierarchical structure of biological polymers. Inspired by balsa wood, Compton et al.[101] created a cellular, lightweight composite with controlled alignment of multiscale, high aspect ratio SiC fiber reinforcement through DIW [Figure 5A]. In DIW, because fiber alignment occurs in the direction of the print path, the mechanical properties, including stiffness and toughness, of balsa wood-inspired composites can be designed and optimized by reasonably planning the print path. By additionally adding a rotary union into the standard DIW platform and using the so-called rotational direct-ink writing technique, the orientation of the extruded fiber reinforcement can be decoupled from the prescribed print path, such that each voxel of the printed material can itself be spatially varied for locally controlling fiber orientation, which allows the mechanical response of the resulting material to be site-specifically optimized[102]. The inspiration of this work comes from natural composites that use helical fiber arrangements to enhance damage tolerance, such as the hammer-like stomatopod dactyl club and wood cell walls.

    Figure 5. Mechanical designs for bioinspired soft composites. (A) Design and printing of various honeycomb structures using SiC-filled epoxy materials[101]. (B) Microstructural design of samples with circular defects using 3D magnetic printing that can program the orientation of stiff platelets[105]. (C) Snapshots displaying deformation and fracture mechanisms for a 3D-printed specimen with a brick block-like material layout[111,112]. (D) Design of material arrangement in a conch shell-inspired structure and the mechanisms of crack propagation in the structures[117]. (E) Schematic of a two-component dispenser and a printed hand-like structure with gradient materials[120]. (F) Design and print of a limb-mimic structure using gradient digital light processing[121].

    Complex-oriented fiber reinforced materials can also be designed based on multi-material inkjet 3D printing and topological optimization. Boddeti et al.[103,104] developed a general framework that integrates the workflow for the optimal design and fabrication of novel multiscale laminated composites with spatially varying microstructures. The developed approach in their work allows for design automation, material compilation and voxel-based digital fabrication. Apart from the above methods, external field-assisted 3D printing techniques have also been utilized by researchers to fabricate bioinspired composite reinforcement architectures. Martin et al.[105] [Figure 5B] and Kokkinis et al.[106] proposed the use of magnetic 3D printing to allow for the rapid assembly of magnetized stiff platelets in rather complex orientations. Materials that replicate the microstructural composition of abalone shell, peacock mantis shrimp and mammalian cortical bone were successfully designed and fabricated. In addition to short fiber or particle reinforcement, long or continuous fiber reinforcement is also chosen by some natural materials, such as bone, to simultaneously achieve excellent stiffness and toughness. Mueller et al.[107] borrowed this design inspiration and created a cellular composite composed of flexible epoxy core-elastomeric silicone interfacial layer-brittle epoxy shell motifs through a multicore-shell, 3D printing technique. The produced material is found to exhibit both high stiffness and toughness. Boddeti et al.[108] applied their approach previously developed for short fiber reinforced composites to continuous fiber reinforced composites and realized the creation of laminated material with variable stiffness. Bioinspired materials have also been achieved by other printing techniques, such as FDM[109].

    In nature, some structural materials exhibit fracture-resistant properties by arranging two dissimilar constituents in a brick block-like layout. Such a material layout can greatly promote the deflection of crack propagation and energy dissipation[110]. A typical example is nacre, which is composed of a stiff and a compliant phase with a structured brick-and-mortar arrangement. Dimas et al.[111,112] computationally designed two types of nacre-like composite materials with predicted fracture response based on multi-material 3D printing [Figure 5C]. It was shown that the synthesized composites exhibit an order of magnitude larger toughness and modulus than its fundamental building blocks. Going beyond simple tension or compression loadings, Gu et al.[113] created nacre-like composites with superior impact performance compared to their monolithic stiff phase. In their design, two base materials with vastly different properties were firstly assembled in a ply with an architecture similar to nacre, these plies were then stacked with orientation angles of 0° and 90° to form a laminate construct. In another study, the same authors proposed an approach that can generate the optimized material layout starting from a composite system with a distinct set of unit cells. This approach combines machine learning with a self-learning algorithm and can design hierarchical materials with tougher and stronger properties[114].

    In addition to nacre, cortical bone is another natural composite material that adopts a staggered layout of stiff and soft phases. Libonati et al.[115] designed and tested synthetic composites with a pattern inspired by the microstructure of cortical bone and the results demonstrate that the bone-inspired design is critical for toughness amplification and balance with material strength. In their design, the soft phases were used as inclusions while the stiff phases were isolated by the soft inclusions. Although this design can efficiently increase toughness, it limits the level to which the modulus of the composite can be elevated. Lei et al.[116] avoided this limitation by using stiff phases as inclusions and creating a different enhancement design for the staggered composite. In addition to 2D patterns, a 3D hierarchical pattern for material layout was proposed by Gu et al.[117] to obtain a composite with enhanced impact resistance, the inspiration of which derives from biological conch shells [Figure 5D].

    Many biological tissues in the human body are composed of gradient material constituents. For example, the skeletal muscle is divided into muscle belly and tendons. The muscle belly is made up of muscle fibers and is red and soft with contractility, while tendons are made up of dense connective tissue and are white and hard with no contractility.

    The 3D printing of gradient materials has found biomedical applications in fields covering tissue engineering[118] and prosthetic replacement[119]. Kokkinis et al.[120] developed a 3D printing platform to fabricate elastomer gradients spanning three orders of magnitude in elastic modulus. To demonstrate the potential applications, they designed a structure that resembled a hand using graded materials for the interface and three other different materials for distinct regions in the “hand” [Figure 5E][120]. Kuang et al.[121] conceived the concept of using grayscale digital light processing to 3D print highly functionally graded materials. One of their demonstrated examples included a structure that mimics a human limb where the soft muscle region was printed using G85 light while the stiff bone region was printed using G0 light [Figure 5F][121]. Bioinspired graded composites can also be designed based on other 3D printing techniques, such as multi-material inkjet printing[122,123] and extrusion-based multi-material printing[124,125].

    MECHANICAL DESIGNS FOR 4D SHAPE-CHANGING STRUCTURES

    4D printing is a novel concept where additive manufactured structures can change their shape or function upon external actuation[126]. Currently, most 4D printing research is based on soft materials, including polymers, hydrogels and biomaterials[127], whose mechanical programming is the most crucial design step. In 4D printings based on active materials, mechanical design is required to predict the deformation pattern and actuation speed. In 4D printings based on structural innovation, the mechanics of beams, shells or frames should be utilized to provide information regarding the geometry, deformation route and stability.

    The design strategy for 4D printed active materials is similar to the mechanical design of classical materials that respond to external stimuli. Firstly, the constitutive model of the material coupled with the external stimulus field is developed to describe the fundamental rule of deformation. The constitutive model is then incorporated into theoretical calculations or numerical simulations to precisely predict actuation[128,129]. The optimal design configuration can be obtained by iterative theoretical designs and experiment verifications. Special concerns in designing 4D printed active materials include the correlations between their responsive properties and the manufacturing process. For example, changing the printing parameters leads to either residual tensile stress or residual compressive stress in additive manufactured shape memory polymers, whose evolution rule could be included in the mechanical design theory[130-132] [Figure 6A]. Another example is additive manufactured hydrogels whose local swelling ability is correlated with the printing process, so the prediction and pre-definition of local material heterogeneity are necessary for programmable deformation after the hydrogel structure is immersed in solution[133-135] [Figure 6B].

    Figure 6. Mechanical designs for 4D shape-changing structures. (A) A 4D printed shape memory hinge that can be repeatedly programmed and automatically deformed upon electric heating[130]. (B) A 4D printed hydrogel flower that gradually closes due to swelling dynamics[133]. (C) 4D printed reversible shape-changing soft structures based on the buckling of a non-Euclidean plate[153]. (D) 4D printed soft actuators with programmable twisting and bending deformation[155].

    In 4D printed liquid crystal elastomers and magnetoactive polymers that have anisotropic actuation behaviors, the microscopic analysis of particle alignment and the macroscopic planning of the printing path are both critical design issues[136,137]. Recently, many data-driven topological optimization and inverse design methods have been proposed and have realized the fast customerization of 4D shape-changing soft structures and increased the design space for complex deformation patterns[138-140] [Figure 7A-D]. Wu et al.[138] and Hamel et al.[140] used an evolutionary algorithm to design the actuated shape of a bi-material beam, where the designed configurations could be directly voxelized for additive manufacturing. Sossou et al.[139] developed a computational design tool that integrated the design, modeling and inverse optimization of complex 4D shape morphing structures. Zhang et al.[141] used a convolutional neural network to quickly predict the large deformation of digital active materials. In the work of Su et al.[142], machine learning was adopted to establish the correlation between manufacturing parameters and experimentally obtained curvatures of shape morphing SU-8 strips.

    Figure 7. Data-driven inverse design for 4D shape-changing structures. (A) Evolutionary algorithm for 4D shape-morphing beams[138]. (B) Convolutional neural network for 4D shape-morphing beams[141]. (C) 4D shape morphing wing designed by evolutionary algorithm[139]. (D) Machine learning prediction for the dynamic curvatures of SU-8 shape morphing strips[142].

    In addition to 4D printing based on active materials, structural mixing and hierarchy is another strategy that can induce automatic shape changing even for non-active materials. Here, we briefly introduce some deformation modes that are commonly used in 4D printing designs. Bending based on laminate or gradient design is probably the most widely adopted deformation mode and its theoretical foundation has been well established[143]. When designing 4D shape-shifting soft structures, residual stress and material heterogeneity related to the manufacturing process should be included in the beam model[144,145]. The shape of many 4D printed soft structures might also evolve dynamically and in this case, the dynamic beam model with the inertial term can be adopted[146,147]. It should be noted that bending is actually different to “folding”, which always creates plastic creases or microscale damage, and two adjacent plates can be perfectly overlapped together[148,149]. However, if the bent region is relatively small compared with the overall dimensions, folding deformation can be approximated by bending[150,151].

    The second type of commonly adopted deformation mode is buckling, which comes from the instability of potential energy[152]. Non-Euclidean plates that can deform from a planar configuration to a 3D surface[153,154] [Figure 6C] and twisting ribbons that automatically rotate upon actuation[155] [Figure 6D] are typical examples of 4D buckling designs. Non-developable 3D surfaces can be easily realized in 4D printing through a combination of buckling and computational geometry but the transformation of such surfaces should be further helped by bending designs[156-158]. In the theory of stability, an equilibrium state might not be stable, thus some 4D buckling structures exhibit different deformation patterns before they finally reach the stable state to minimize their potential energies[159,160]. Another common deformation mode is the large-scale motion of flexible mechanisms. In this case, the macroscopic shape changing upon actuation is neither driven by individual bending elements or by local instability but is instead driven by the shifting of the overall energy barrier. For example, Chen et al.[161] additive manufactured a shape memory robotic frame that can automatically propel upon heating and a reduction in the energy barrier. Wang et al.[150] found that an additive manufactured and stably folded square-twist origami became unstable when the energy barrier was decreased by softening the flat origami panels.

    PERSPECTIVES AND CONCLUSIONS

    Additive manufacturing has broken the shackles of traditional manufacturing technology and brought new bottom-up manufacturing concepts, thereby offering researchers numerous opportunities to design soft materials with superior structural and functional properties. With state-of-the-art of additive manufacturing, materials that mimic the mechanical properties of biological materials and materials that can actively change shape after been manufactured have been created, which permit applications in biomedical engineering, multifunctional electronics and the aerospace industry. However, there are also some challenges underlying current additive manufacturing techniques. Soft materials are known to easily deform compared to metals and ceramics and shape distortion is therefore commonly observed in the printed parts using various polymer based-printing techniques. Efficient models and simulations must be developed for better understanding these manufacturing process to optimize printing parameters and improve shape accuracy. In addition, the issue of interfacial strength in printed parts with different materials has also aroused concern from researchers. With the rapid development of multi-material 3D printing, it is significantly important to develop corresponding approaches for controlling or even optimizing various material interfaces during the printing process. The examples highlighted here only cover the mechanical designs for additive manufactured materials from limited aspects and are thus far from the entire content of additive manufacturing-based designs. In the future, it can be imagined that designs will be fully integrated with the additive manufacturing process to create more novel materials with superior properties.

    DECLARATIONS

    Authors’ contributions

    Designed and wrote the original draft: Zhang Q, Zhao Z

    Reviewed and revised the manuscript: Shi Y

    Availability of data and materials

    Not applicable.

    Financial support and sponsorship

    Zhao Z acknowledges the support from the National Natural Science Foundation of China (12002032). Zhang Q greatly acknowledges the start-up funding (Grant No. 90YAH21131) supported from Nanjing University of Aeronautics and Astronautics.

    Conflicts of interest

    All authors declared that there are no conflicts of interest.

    Ethical approval and consent to participate

    Not applicable.

    Consent for publication

    Not applicable.

    Copyright

    © The Author(s) 2022.

    References

    • 1. Zhou L, Fu J, He Y. A review of 3D printing technologies for soft polymer materials. Adv Funct Mater 2020;30:2000187.

      DOI
    • 2. Ngo TD, Kashani A, Imbalzano G, Nguyen KT, Hui D. Additive manufacturing (3D printing): a review of materials, methods, applications and challenges. Compos B Eng 2018;143:172-96.

      DOI
    • 3. Chen Z, Turng L. A review of current developments in process and quality control for injection molding. Adv Polym Technol 2005;24:165-82.

      DOI
    • 4. Gordon S, Hillery MT. A review of the cutting of composite materials. Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications 2003;217:35-45.

      DOI
    • 5. Xia Y, Whitesides GM. Soft lithography. Angew Chem Int Ed Engl 1998;37:550-75.

      DOIPubMed
    • 6. Keneth E, Kamyshny A, Totaro M, Beccai L, Magdassi S. 3D printing materials for soft robotics. Adv Mater 2021;33:e2003387.

      DOIPubMed
    • 7. Zhang Y, Zhang F, Yan Z, et al. Printing, folding and assembly methods for forming 3D mesostructures in advanced materials. Nat Rev Mater 2017:2.

      DOI
    • 8. Derakhshanfar S, Mbeleck R, Xu K, Zhang X, Zhong W, Xing M. 3D bioprinting for biomedical devices and tissue engineering: A review of recent trends and advances. Bioact Mater 2018;3:144-56.

      DOIPubMed PMC
    • 9. Hubbard JD, Acevedo R, Edwards KM, et al. Fully 3D-printed soft robots with integrated fluidic circuitry. Sci Adv 2021;7:eabe5257.

      DOIPubMed PMC
    • 10. Hensleigh R, Cui H, Xu Z, et al. Charge-programmed three-dimensional printing for multi-material electronic devices. Nat Electron 2020;3:216-24.

      DOI
    • 11. Liu X, Yuk H, Lin S, et al. 3D printing of living responsive materials and devices. Adv Mater 2018;30:1704821.

      DOIPubMed
    • 12. Montgomery SM, Kuang X, Armstrong CD, Qi HJ. Recent advances in additive manufacturing of active mechanical metamaterials. Curr Opin Solid State Mater Sci 2020;24:100869.

      DOI
    • 13. Yuan C, Kowsari K, Panjwani S, et al. Ultrafast three-dimensional printing of optically smooth microlens arrays by oscillation-assisted digital light processing. ACS Appl Mater Interfaces 2019;11:40662-8.

      DOIPubMed
    • 14. Feng S, Zhu P, Zheng H, et al. Three-dimensional capillary ratchet-induced liquid directional steering. Science 2021;373:1344-8.

      DOIPubMed
    • 15. Ashby MF, Johnson K. Materials and design: the art and science of material selection in product design. 3rd ed. Oxford: Butterworth-Heinemann. 2013.

      DOI
    • 16. Howell P, Kozyreff G, Ockendon J. Applied solid mechanics. Cambridge: Cambridge University Press; 2009.

      DOI
    • 17. Thompson MK, Moroni G, Vaneker T, et al. Design for Additive Manufacturing: Trends, opportunities, considerations, and constraints. CIRP Annals 2016;65:737-60.

      DOI
    • 18. Bandyopadhyay A, Bose S. Additive manufacturings. 2nd ed. Boca Raton: CRC press; 2019.

      DOI
    • 19. McCracken JM, Donovan BR, White TJ. Materials as machines. Adv Mater 2020;32:e1906564.

      DOIPubMed
    • 20. Yee DW, Greer JR. Three-dimensional chemical reactors: in situ materials synthesis to advance vat photopolymerization. Polym Int 2021;70:964-76.

      DOI
    • 21. Kok Y, Tan X, Wang P, et al. Anisotropy and heterogeneity of microstructure and mechanical properties in metal additive manufacturing: a critical review. Mater Des 2018;139:565-86.

      DOI
    • 22. Wang X, Jiang M, Zhou Z, Gou J, Hui D. 3D printing of polymer matrix composites: a review and prospective. Compos B Eng 2017;110:442-58.

      DOI
    • 23. Narupai B, Nelson A. 100th anniversary of macromolecular science viewpoint: macromolecular materials for additive manufacturing. ACS Macro Lett 2020;9:627-38.

      DOI
    • 24. Ge Q, Li Z, Wang Z, et al. Projection micro stereolithography based 3D printing and its applications. Int J Extrem Manuf 2020;2:022004.

      DOI
    • 25. Boydston AJ, Cui J, Lee C, Lynde BE, Schilling CA. 100th anniversary of macromolecular science viewpoint: integrating chemistry and engineering to enable additive manufacturing with high-performance polymers. ACS Macro Lett 2020;9:1119-29.

      DOI
    • 26. Truby RL, Lewis JA. Printing soft matter in three dimensions. Nature 2016;540:371-8.

      DOIPubMed
    • 27. Hull CW. Apparatus for production of three-dimensional objects by stereolithography; 0. Available from: https://patents.google.com/patent/US4575330A/en [Last accessed on 11 Feb 2022].

    • 28. Beaman JJ, Deckard CR. Selective laser sintering with assisted powder handling; 0. Available from: https://patents.google.com/patent/US4938816A/en [Last accessed on 11 Feb 2022].

    • 29. Sun C, Fang N, Wu D, Zhang X. Projection micro-stereolithography using digital micro-mirror dynamic mask. Sens Actuator A Phys 2005;121:113-20.

      DOI
    • 30. Zheng X, Deotte J, Alonso MP, et al. Design and optimization of a light-emitting diode projection micro-stereolithography three-dimensional manufacturing system. Rev Sci Instrum 2012;83:125001.

      DOIPubMed
    • 31. Cumpston BH, Ananthavel SP, Barlow S, et al. Two-photon polymerization initiators for three-dimensional optical data storage and microfabrication. Nature 1999;398:51-4.

      DOI
    • 32. Derby B. Inkjet printing of functional and structural materials: fluid property requirements, feature stability, and resolution. Annu Rev Mater Res 2010;40:395-414.

      DOI
    • 33. Crump S S. Apparatus and method for creating three-dimensional objects; 0. Available from: https://patents.google.com/patent/US5121329A/en [Last accessed on 11 Feb 2022].

    • 34. Zein I, Hutmacher DW, Tan KC, Teoh SH. Fused deposition modeling of novel scaffold architectures for tissue engineering applications. Biomaterials 2002;23:1169-85.

      DOIPubMed
    • 35. Lewis J. Direct ink writing of 3D functional materials. Adv Funct Mater 2006;16:2193-204.

      DOI
    • 36. Lewis JA, Gratson GM. Direct writing in three dimensions. Mater Today 2004;7:32-9.

      DOIPubMed
    • 37. Farahani RD, Dubé M, Therriault D. Three-dimensional printing of multifunctional nanocomposites: manufacturing techniques and applications. Adv Mater 2016;28:5794-821.

      DOIPubMed
    • 38. MacDonald E, Wicker R. Multiprocess 3D printing for increasing component functionality. Science 2016;353:aaf2093.

      DOIPubMed
    • 39. Gibson I, Rosen D, Stucker B, Khorasani M. Additive manufacturing technologies. Cham: Springer; 2021.

      DOI
    • 40. Karalekas D, Aggelopoulos A. Study of shrinkage strains in a stereolithography cured acrylic photopolymer resin. J Mater Process Technol 2003;136:146-50.

      DOI
    • 41. Jian Y, He Y, Jiang T, Li C, Yang W, Nie J. Polymerization shrinkage of (meth)acrylate determined by reflective laser beam scanning. J Polym Sci B Polym Phys 2012;50:923-8.

      DOI
    • 42. Jacobs PF. Rapid prototyping & manufacturing: fundamentals of stereolithography. J Manuf Syst 1993;12:430-3.

      DOI
    • 43. Xu Y, Imamura M, Nakagawa T. Micro-hardness measurement of photopolymer in stereolithography. J Photopol Sci Technol 1997;10:181-6.

      DOI
    • 44. Tang Y, Henderson CL, Muzzy J, Rosen DW. .

      DOI
    • 45. Bugeda G, Cervera M, Lombera G, Onate E. Numerical analysis of stereolithography processes using the finite element method. Rapid Prototyp J 1995;1:13-23.

      DOI
    • 46. Wiedemann B, Dusel K, Eschl J. Investigation into the influence of material and process on part distortion. Rapid Prototyp J 1995;1:17-22.

      DOI
    • 47. Huang Y, Jiang C. Curl distortion analysis during photopolymerisation of stereolithography using dynamic finite element method. Int J Adv Manuf Technol 2003;21:586-95.

      DOI
    • 48. Jiang C, Huang Y, Liu C. Dynamic finite element analysis of photopolymerization in stereolithography. Rapid Prototyp J 2006;12:173-80.

      DOI
    • 49. Westbeek S, Remmers J, van Dommelen J, Maalderink H, Geers M. Prediction of the deformed geometry of vat photo-polymerized components using a multi-physical modeling framework. Additive Manufacturing 2021;40:101922.

      DOI
    • 50. Westbeek S, Remmers J, van Dommelen J, Geers M. Multi-scale process simulation for additive manufacturing through particle filled vat photopolymerization. Comput Mater Sci 2020;180:109647.

      DOI
    • 51. Wu T. Theoretical modeling and experimental characterization of stress and crack development in parts manufactured through large area maskless photopolymerization. Available from: https://smartech.gatech.edu/handle/1853/54274 [Last accessed on 11 Feb 2022].

    • 52. Wang Z, Liang H, Dobrynin AV. Computer simulations of continuous 3-D printing. Macromolecules 2017;50:7794-800.

      DOI
    • 53. Zhang Q, Weng S, Hamel CM, et al. Design for the reduction of volume shrinkage-induced distortion in digital light processing 3D printing. Extreme Mech Lett 2021;48:101403.

      DOI
    • 54. Wu D, Zhao Z, Zhang Q, Qi HJ, Fang D. Mechanics of shape distortion of DLP 3D printed structures during UV post-curing. Soft Matter 2019;15:6151-9.

      DOIPubMed
    • 55. Armillotta A, Bellotti M, Cavallaro M. Warpage of FDM parts: Experimental tests and analytic model. Robot Comput Integr Manuf 2018;50:140-52.

      DOI
    • 56. Wang T, Xi J, Jin Y. A model research for prototype warp deformation in the FDM process. Int J Adv Manuf Technol 2007;33:1087-96.

      DOI
    • 57. Zhu Z, Dhokia V, Nassehi A, Newman ST. Investigation of part distortions as a result of hybrid manufacturing. Robot Comput Integr Manuf 2016;37:23-32.

      DOI
    • 58. Liu XH, Li SP, Liu Z, Zheng XH, Chen XH, Wang ZB. An investigation on distortion of PLA thin-plate part in the FDM process. Int J Adv Manuf Technol 2015;79:1117-26.

      DOI
    • 59. Zhang Y, Chou YK. Three-dimensional finite element analysis simulations of the fused deposition modelling process. Proc Inst Mech Eng B J Eng Manuf 2006;220:1663-71.

      DOI
    • 60. Zhang Y, Chou K. A parametric study of part distortions in fused deposition modelling using three-dimensional finite element analysis. Proc Inst Mech Eng B J Eng Manuf 2008;222:959-68.

      DOI
    • 61. Dalgarno KW, Childs TRC, Rowntree I, Rothwell L. .

    • 62. Yang H, Hwang P, Lee S. A study on shrinkage compensation of the SLS process by using the Taguchi method. Int J Mach Tools Manuf 2002;42:1203-12.

      DOI
    • 63. Schmutzler C, Zimmermann A, Zaeh MF. Compensating warpage of 3D printed parts using free-form deformation. Procedia CIRP 2016;41:1017-22.

      DOI
    • 64. Yin J, Lu C, Fu J, Huang Y, Zheng Y. Interfacial bonding during multi-material fused deposition modeling (FDM) process due to inter-molecular diffusion. Mater Des 2018;150:104-12.

      DOI
    • 65. Sun Q, Rizvi G, Bellehumeur C, Gu P. Effect of processing conditions on the bonding quality of FDM polymer filaments. Rapid Prototyp J 2008;14:72-80.

      DOI
    • 66. Ge T, Robbins MO, Perahia D, Grest GS. Healing of polymer interfaces: interfacial dynamics, entanglements, and strength. Phys Rev E Stat Nonlin Soft Matter Phys 2014;90:012602.

      DOIPubMed
    • 67. Yu K, Ge Q, Qi HJ. Reduced time as a unified parameter determining fixity and free recovery of shape memory polymers. Nat Commun 2014;5:3066.

      DOIPubMed
    • 68. Yang T, Liechti KM, Huang R. A multiscale cohesive zone model for rate-dependent fracture of interfaces. J Mech Phys Solids 2020;145:104142.

      DOI
    • 69. Duty C, Ajinjeru C, Kishore V, et al. What makes a material printable? J Manuf Process 2018;35:526-37.

      DOI
    • 70. Gojzewski H, Guo Z, Grzelachowska W, et al. Layer-by-layer printing of photopolymers in 3D: how weak is the interface? ACS Appl Mater Interfaces 2020;12:8908-14.

      DOIPubMed PMC
    • 71. Zhao Z, Wu D, Chen H, Jerry Qi H, Fang D. Indentation experiments and simulations of nonuniformly photocrosslinked polymers in 3D printed structures. Additive Manufacturing 2020;35:101420.

      DOI
    • 72. Zhao Z, Mu X, Wu J, Qi HJ, Fang D. Effects of oxygen on interfacial strength of incremental forming of materials by photopolymerization. Extreme Mech Lett 2016;9:108-18.

      DOI
    • 73. Tumbleston JR, Shirvanyants D, Ermoshkin N, et al. Additive manufacturing. Continuous liquid interface production of 3D objects. Science 2015;347:1349-52.

      DOIPubMed
    • 74. Regehly M, Garmshausen Y, Reuter M, et al. Xolography for linear volumetric 3D printing. Nature 2020;588:620-4.

      DOIPubMed
    • 75. Wan X, Luo L, Liu Y, Leng J. Direct ink writing based 4D printing of materials and their applications. Adv Sci (Weinh) 2020;7:2001000.

      DOIPubMed PMC
    • 76. Mao Y, Yu K, Isakov MS, Wu J, Dunn ML, Jerry Qi H. Sequential self-folding structures by 3D printed digital shape memory polymers. Sci Rep 2015;5:13616.

      DOIPubMed PMC
    • 77. Ge Q, Sakhaei AH, Lee H, Dunn CK, Fang NX, Dunn ML. Multimaterial 4D Printing with tailorable shape memory polymers. Sci Rep 2016;6:31110.

      DOIPubMed PMC
    • 78. Zhang H, Cheng X, Yan D, Zhang Y, Fang D. A nonlinear mechanics model of soft network metamaterials with unusual swelling behavior and tunable phononic band gaps. Compos Sci Technol 2019;183:107822.

      DOI
    • 79. Skylar-Scott MA, Mueller J, Visser CW, Lewis JA. Voxelated soft matter via multimaterial multinozzle 3D printing. Nature 2019;575:330-5.

      DOIPubMed
    • 80. Hardin JO, Ober TJ, Valentine AD, Lewis JA. Microfluidic Printheads for multimaterial 3D printing of viscoelastic inks. Adv Mater 2015;27:3279-84.

      DOIPubMed
    • 81. Yang H, Li C, Yang M, et al. Printing hydrogels and elastomers in arbitrary sequence with strong adhesion. Adv Funct Mater 2019; doi: 10.1002/adfm.201901721.

      DOI
    • 82. Ge Q, Chen Z, Cheng J, et al. 3D printing of highly stretchable hydrogel with diverse UV curable polymers. Sci Adv 2021;7:eaba4261.

      DOIPubMed PMC
    • 83. Dolinski ND, Callaway EB, Sample CS, et al. Tough multimaterial interfaces through wavelength-selective 3D printing. ACS Appl Mater Interfaces 2021;13:22065-72.

      DOIPubMed
    • 84. Li Y, Ortiz C, Boyce MC. A generalized mechanical model for suture interfaces of arbitrary geometry. J Mech Phys Solids 2013;61:1144-67.

      DOI
    • 85. Dunlop JW, Weinkamer R, Fratzl P. Artful interfaces within biological materials. Mater Today 2011;14:70-8.

      DOI
    • 86. Roach DJ, Hamel CM, Dunn CK, Johnson MV, Kuang X, Qi HJ. The m4 3D printer: A multi-material multi-method additive manufacturing platform for future 3D printed structures. Additive Manufacturing 2019;29:100819.

      DOI
    • 87. Wu D, Zhao Z, Wang P, et al. Structured Interfaces for improving the tensile strength and toughness of stiff/highly stretchable polymer hybrids. Adv Mater Technol 2020;5:2000652.

      DOI
    • 88. Rivera J, Hosseini MS, Restrepo D, et al. Toughening mechanisms of the elytra of the diabolical ironclad beetle. Nature 2020;586:543-8.

      DOIPubMed
    • 89. Chen J, Liu X, Tian Y, et al. 3D-printed anisotropic polymer materials for functional applications. Adv Mater 2022;34:e2102877.

      DOIPubMed
    • 90. Monzón M, Ortega Z, Hernández A, Paz R, Ortega F. Anisotropy of photopolymer parts made by digital light processing. Materials (Basel) 2017;10:64.

      DOIPubMed PMC
    • 91. Yao T, Ye J, Deng Z, Zhang K, Ma Y, Ouyang H. Tensile failure strength and separation angle of FDM 3D printing PLA material: experimental and theoretical analyses. Compos B Eng 2020;188:107894.

      DOI
    • 92. Chen K, Kuang X, Li V, Kang G, Qi HJ. Fabrication of tough epoxy with shape memory effects by UV-assisted direct-ink write printing. Soft Matter 2018;14:1879-86.

      DOIPubMed
    • 93. Tancogne-dejean T, Mohr D. Elastically-isotropic truss lattice materials of reduced plastic anisotropy. Int J Solids Struct 2018;138:24-39.

      DOI
    • 94. Zhang P, To AC. Transversely isotropic hyperelastic-viscoplastic model for glassy polymers with application to additive manufactured photopolymers. Int J Plast 2016;80:56-74.

      DOI
    • 95. Chen K, Teo HWB, Rao W, et al. Experimental and modeling investigation on the viscoelastic-viscoplastic deformation of polyamide 12 printed by Multi Jet Fusion. Int J Plast 2021;143:103029.

      DOI
    • 96. Liu J, To AC. Deposition path planning-integrated structural topology optimization for 3D additive manufacturing subject to self-support constraint. Computer-Aided Design 2017;91:27-45.

      DOI
    • 97. Kickelbick G. .

      DOI
    • 98. Gu G, Su I, Sharma S, Voros JL, Qin Z, Buehler MJ. Three-dimensional-printing of bio-inspired composites. J Biomech Eng 2016;138:021006.

      DOIPubMed PMC
    • 99. Barthelat F, Yin Z, Buehler MJ. Structure and mechanics of interfaces in biological materials. Nat Rev Mater 2016:1.

      DOI
    • 100. Huang W, Restrepo D, Jung JY, et al. Multiscale toughening mechanisms in biological materials and bioinspired designs. Adv Mater 2019;31:e1901561.

      DOIPubMed
    • 101. Compton BG, Lewis JA. 3D-printing of lightweight cellular composites. Adv Mater 2014;26:5930-5.

      DOIPubMed
    • 102. Raney JR, Compton BG, Mueller J, Ober TJ, Shea K, Lewis JA. Rotational 3D printing of damage-tolerant composites with programmable mechanics. Proc Natl Acad Sci U S A 2018;115:1198-203.

      DOIPubMed PMC
    • 103. Boddeti N, Ding Z, Kaijima S, Maute K, Dunn ML. Simultaneous digital design and additive manufacture of structures and materials. Sci Rep 2018;8:15560.

      DOIPubMed PMC
    • 104. Boddeti N, Rosen DW, Maute K, Dunn ML. Multiscale optimal design and fabrication of laminated composites. Compos Struct 2019;228:111366.

      DOI
    • 105. Martin JJ, Fiore BE, Erb RM. Designing bioinspired composite reinforcement architectures via 3D magnetic printing. Nat Commun 2015;6:8641.

      DOIPubMed PMC
    • 106. Kokkinis D, Schaffner M, Studart AR. Multimaterial magnetically assisted 3D printing of composite materials. Nat Commun 2015;6:8643.

      DOIPubMed PMC
    • 107. Mueller J, Raney JR, Shea K, Lewis JA. Architected lattices with high stiffness and toughness via multicore-shell 3D printing. Adv Mater 2018;30:e1705001.

      DOIPubMed
    • 108. Boddeti N, Tang Y, Maute K, Rosen DW, Dunn ML. Optimal design and manufacture of variable stiffness laminated continuous fiber reinforced composites. Sci Rep 2020;10:16507.

      DOIPubMed PMC
    • 109. Sun Y, Tian W, Zhang T, Chen P, Li M. Strength and toughness enhancement in 3d printing via bioinspired tool path. Mater Des 2020;185:108239.

      DOI
    • 110. Wegst UG, Bai H, Saiz E, Tomsia AP, Ritchie RO. Bioinspired structural materials. Nat Mater 2015;14:23-36.

      DOIPubMed
    • 111. Dimas LS, Bratzel GH, Eylon I, Buehler MJ. Tough composites inspired by mineralized natural materials: computation, 3D printing, and testing. Adv Funct Mater 2013;23:4629-38.

      DOI
    • 112. Dimas LS, Buehler MJ. Modeling and additive manufacturing of bio-inspired composites with tunable fracture mechanical properties. Soft Matter 2014;10:4436-42.

      DOIPubMed
    • 113. Gu GX, Takaffoli M, Hsieh AJ, Buehler MJ. Biomimetic additive manufactured polymer composites for improved impact resistance. Extreme Mech Lett 2016;9:317-23.

      DOI
    • 114. Gu GX, Chen C, Richmond DJ, Buehler MJ. Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment. Mater Horiz 2018;5:939-45.

      DOI
    • 115. Libonati F, Gu GX, Qin Z, Vergani L, Buehler MJ. Bone-inspired materials by design: toughness amplification observed using 3D printing and testing. Adv Eng Mater 2016;18:1354-63.

      DOI
    • 116. Lei M, Hamel CM, Yuan C, Lu H, Qi HJ. 3D printed two-dimensional periodic structures with tailored in-plane dynamic responses and fracture behaviors. Compos Sci Technol 2018;159:189-98.

      DOI
    • 117. Gu GX, Takaffoli M, Buehler MJ. Hierarchically enhanced impact resistance of bioinspired composites. Adv Mater 2017;29:1700060.

      DOIPubMed
    • 118. Sant S, Hancock MJ, Donnelly JP, Iyer D, Khademhosseini A. Biomimetic gradient hydrogels for tissue engineering. Can J Chem Eng 2010;88:899-911.

      DOIPubMed PMC
    • 119. Oshkour AA, Abu Osman NA, Yau YH, Tarlochan F, Abas WA. Design of new generation femoral prostheses using functionally graded materials: a finite element analysis. Proc Inst Mech Eng H 2013;227:3-17.

      DOIPubMed
    • 120. Kokkinis D, Bouville F, Studart AR. 3D printing of materials with tunable failure via bioinspired mechanical gradients. Adv Mater 2018;30:e1705808.

      DOIPubMed
    • 121. Kuang X, Wu J, Chen K, et al. Grayscale digital light processing 3D printing for highly functionally graded materials. Sci Adv 2019;5:eaav5790.

      DOIPubMed PMC
    • 122. Bartlett NW, Tolley MT, Overvelde JT, et al. SOFT ROBOTICS. A 3D-printed, functionally graded soft robot powered by combustion. Science 2015;349:161-5.

      DOIPubMed
    • 123. Ituarte IF, Boddeti N, Hassani V, Dunn ML, Rosen DW. Design and additive manufacture of functionally graded structures based on digital materials. Additive Manufacturing 2019;30:100839.

      DOI
    • 124. Chen K, Zhang L, Kuang X, et al. Dynamic photomask-assisted direct ink writing multimaterial for multilevel triboelectric nanogenerator. Adv Funct Mater 2019;29:1903568.

      DOI
    • 125. Giachini PAGS, Gupta SS, Wang W, et al. Additive manufacturing of cellulose-based materials with continuous, multidirectional stiffness gradients. Sci Adv 2020;6:eaay0929.

      DOIPubMed PMC
    • 126. Kuang X, Roach DJ, Wu J, et al. Advances in 4D printing: materials and applications. Adv Funct Mater 2019;29:1805290.

      DOI
    • 127. Rastogi P, Kandasubramanian B. Breakthrough in the printing tactics for stimuli-responsive materials: 4D printing. Chem Eng J 2019;366:264-304.

      DOI
    • 128. Zhang B, Li H, Cheng J, et al. Mechanically robust and UV-curable shape-memory polymers for digital light processing based 4D printing. Adv Mater 2021;33:e2101298.

      DOIPubMed
    • 129. Zhao Q, Qi HJ, Xie T. Recent progress in shape memory polymer: new behavior, enabling materials, and mechanistic understanding. Prog Polym Sci 2015;49-50:79-120.

      DOI
    • 130. Chen Z, Huang G, Trase I, Han X, Mei Y. Mechanical self-assembly of a strain-engineered flexible layer: wrinkling, rolling, and twisting. Phys Rev Applied 2016:5.

      DOI
    • 131. Manen T, Janbaz S, Zadpoor AA. Programming 2D/3D shape-shifting with hobbyist 3D printers. Mater Horiz 2017;4:1064-9.

      DOIPubMed PMC
    • 132. Ding Z, Yuan C, Peng X, Wang T, Qi HJ, Dunn ML. Direct 4D printing via active composite materials. Sci Adv 2017;3:e1602890.

      DOIPubMed PMC
    • 133. Gladman AS, Matsumoto EA, Nuzzo RG, Mahadevan L, Lewis JA. Biomimetic 4D printing. Nat Mater 2016;15:413-8.

      DOIPubMed
    • 134. Li Q, Xu Z, Ji S, et al. Kinetics-induced morphing of three-dimensional-printed gel structures based on geometric asymmetry. J Appl Mech 2020;87:071008.

      DOI
    • 135. Zhao Z, Qi HJ, Fang D. A finite deformation theory of desolvation and swelling in partially photo-cross-linked polymer networks for 3D/4D printing applications. Soft Matter 2019;15:1005-16.

      DOIPubMed
    • 136. Pozo M, Sol JAHP, Schenning APHJ, Debije MG. 4D Printing of liquid crystals: what’s right for me? Adv Mater 2022;34:e2104390.

      DOIPubMed
    • 137. Kim Y, Yuk H, Zhao R, Chester SA, Zhao X. Printing ferromagnetic domains for untethered fast-transforming soft materials. Nature 2018;558:274-9.

      DOIPubMed
    • 138. Wu S, Hamel CM, Ze Q, Yang F, Qi HJ, Zhao R. Evolutionary algorithm-guided voxel-encoding printing of functional hard-magnetic soft active materials. Advanced Intelligent Systems 2020;2:2000060.

      DOI
    • 139. Sossou G, Demoly F, Belkebir H, Qi HJ, Gomes S, Montavon G. Design for 4D printing: modeling and computation of smart materials distributions. Mater Des 2019;181:108074.

      DOI
    • 140. Hamel CM, Roach DJ, Long KN, Demoly F, Dunn ML, Qi HJ. Machine-learning based design of active composite structures for 4D printing. Smart Mater Struct 2019;28:065005.

      DOI
    • 141. Zhang Z, Gu GX. Finite-element-based deep-learning model for deformation behavior of digital materials. Adv Theory Simul 2020;3:2000031.

      DOI
    • 142. Su J, Li D, Xie Y, et al. A machine learning workflow for 4D printing: understand and predict morphing behaviors of printed active structures. Smart Mater Struct 2020;30:015028.

      DOI
    • 143. Gere JM, Goodno BJ. Mechanics of materials. Available from: https://www.cengageasia.com/TitleDetails/isbn/9781337093347 [Last accessed on 11 Feb 2022].

    • 144. Zhao Z, Wu J, Mu X, Chen H, Qi HJ, Fang D. Origami by frontal photopolymerization. Sci Adv 2017;3:e1602326.

      DOIPubMed PMC
    • 145. Ge Q, Qi HJ, Dunn ML. Active materials by four-dimension printing. Appl Phys Lett 2013;103:131901.

      DOI
    • 146. Vantomme G, Elands LCM, Gelebart AH, et al. Coupled liquid crystalline oscillators in Huygens’ synchrony. Nat Mater 2021;20:1702-6.

      DOIPubMed PMC
    • 147. Korner K, Kuenstler AS, Hayward RC, Audoly B, Bhattacharya K. A nonlinear beam model of photomotile structures. Proc Natl Acad Sci U S A 2020;117:9762-70.

      DOIPubMed PMC
    • 148. Zhang H, Feng H, Huang J, Paik J. Generalized modeling of origami folding joints. Extreme Mech Lett 2021;45:101213.

      DOI
    • 149. Giampieri A, Perego U, Borsari R. A constitutive model for the mechanical response of the folding of creased paperboard. Int J Solids Struct 2011;48:2275-87.

      DOI
    • 150. Wang L, Song W, Zhang Y, et al. Active reconfigurable tristable square-twist origami. Adv Funct Mater 2020;30:1909087.

      DOI
    • 151. Liu G, Zhao Y, Wu G, Lu J. Origami and 4D printing of elastomer-derived ceramic structures. Sci Adv 2018;4:eaat0641.

      DOIPubMed PMC
    • 152. Kochmann DM, Bertoldi K. Exploiting microstructural instabilities in solids and structures: from metamaterials to structural transitions. Appl Mech Rev 2017;69:050801.

      DOI
    • 153. Nojoomi A, Arslan H, Lee K, Yum K. Bioinspired 3D structures with programmable morphologies and motions. Nat Commun 2018;9:3705.

      DOIPubMed PMC
    • 154. Efrati E, Sharon E, Kupferman R. Elastic theory of unconstrained non-Euclidean plates. J Mech Phys Solids 2009;57:762-75.

      DOI
    • 155. Ding Z, Weeger O, Qi HJ, Dunn ML. 4D rods: 3D structures via programmable 1D composite rods. Mater Des 2018;137:256-65.

      DOI
    • 156. Zhang Q, Kuang X, Weng S, et al. Shape-memory balloon structures by pneumatic multi-material 4D printing. Adv Funct Mater 2021;31:2010872.

      DOI
    • 157. Siéfert E, Reyssat E, Bico J, Roman B. Bio-inspired pneumatic shape-morphing elastomers. Nat Mater 2019;18:24-8.

      DOIPubMed
    • 158. Boley JW, van Rees WM, Lissandrello C, et al. Shape-shifting structured lattices via multimaterial 4D printing. Proc Natl Acad Sci U S A 2019;116:20856-62.

      DOIPubMed PMC
    • 159. Hua M, Kim C, Du Y, Wu D, Bai R, He X. Swaying gel: chemo-mechanical self-oscillation based on dynamic buckling. Matter 2021;4:1029-41.

      DOI
    • 160. Zhao Z, Kuang X, Yuan C, Qi HJ, Fang D. Hydrophilic/hydrophobic composite shape-shifting structures. ACS Appl Mater Interfaces 2018;10:19932-9.

      DOIPubMed
    • 161. Chen T, Bilal OR, Shea K, Daraio C. Harnessing bistability for directional propulsion of soft, untethered robots. Proc Natl Acad Sci U S A 2018;115:5698-702.

      DOIPubMed PMC

    Cite This Article

    Zhang Q, Shi Y, Zhao Z. A brief review of mechanical designs for additive manufactured soft materials. Soft Sci 2022;2:2. http://dx.doi.org/10.20517/ss.2021.22

    Views
    1005
    Downloads
    273
    Citations
     0
    Comments
    0

    3

    Comments

    Comments must be written in English. Spam, offensive content, impersonation, and private information will not be permitted. If any comment is reported and identified as inappropriate content by OAE staff, the comment will be removed without notice. If you have any queries or need any help, please contact us at support@oaepublish.com.

    © 2016-2022 OAE Publishing Inc., except certain content provided by third parties