Switchable Bistability in 3D Printed Shells With Bio-Inspired Architectures and Spatially Distributed Pre-Stress

Ang, Karl Jin; Riley, Katherine S.; Faber, Jakob; Arrieta, Andres F.

doi:10.1115/smasis2018-8208

Cited by 5 publications

(2 citation statements)

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“…However, due to material properties, the FE method can only be reliable for deformations. Various similar research was conducted for better-controlled deformation using FE models considering the influence of printing parameters, such as plate thickness [18], printing speed [19], and the printing direction [20]. These studies mostly focused on the effect of a single printing parameter to achieve shape morphing properties.…”

Section: State-of-the-art Reviewmentioning

confidence: 99%

Programmable Thermo-Responsive Self-Morphing Structures Design and Performance

et al. 2022

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Additive manufacturing (AM), also known as 3D printing, was introduced to design complicated structures/geometries that overcome the manufacturability limitations of traditional manufacturing processes. However, like any other manufacturing technique, AM also has its limitations, such as the need of support structures for overhangs, long build time etc. To overcome these limitations of 3D printing, 4D printing was introduced, which utilizes smart materials and processes to create shapeshifting structures with the external stimuli, such as temperature, humidity, magnetism, etc. The state-of-the-art 4D printing technology focuses on the “form” of the 4D prints through the multi-material variability. However, the quantitative morphing analysis is largely absent in the existing literature on 4D printing. In this research, the inherited material anisotropic behaviors from the AM processes are utilized to drive the morphing behaviors. In addition, the quantitative morphing analysis is performed for designing and controlling the shapeshifting. A material–process–performance 4D printing prediction framework has been developed through a novel dual-way multi-dimensional machine learning model. The morphing evaluation metrics, bending angle and curvature, are obtained and archived at 99% and 93.5% R2, respectively. Based on the proposed method, the material and production time consumption can be reduced by around 65–90%, which justifies that the proposed method can re-imagine the digital–physical production cycle.

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Section: State-of-the-art Reviewmentioning

confidence: 99%

Programmable Thermo-Responsive Self-Morphing Structures Design and Performance

et al. 2022

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“…The SME programming procedure involves a thermomechanical cycle that is associated with applying strain at high temperatures (heating and loading) and with storing at low temperatures (unloading and fixing) [10]. These steps can be done continuously during the extrusion and deposition stages of printing by the FDM process, which is called in-printing programming [22,23]. The nature of extrusion in the FDM method combined with the deposition of a traveling head causes an amount of strain to be applied on the molten state chains during printing.…”

Section: Introductionmentioning

confidence: 99%

A New Strategy for Achieving Shape Memory Effects in 4D Printed Two-Layer Composite Structures

et al. 2022

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In this study, a new strategy and design for achieving a shape memory effect (SME) and 4D printed two-layer composite structures is unveiled, thanks to fused deposition modeling (FDM) biomaterial printing of commercial filaments, which do not have an SME. We used ABS and PCL as two well-known thermoplastics, and TPU as elastomer filaments that were printed in a two-layer structure. The thermoplastic layer plays the role of constraint for the elastomeric layer. A rubber-to-glass transition of the thermoplastic layer acts as a switching phenomenon that provides the capability of stabilizing the temporary shape, as well as storing the deformation stress for the subsequent recovery of the permanent shape by phase changing the thermoplastic layer in the opposite direction. The results show that ABS–TPU had fixity and recovery ratios above 90%. The PCL–TPU composite structure also demonstrated complete recovery, but its fixity was 77.42%. The difference in the SME of the two composite structures is related to the transition for each thermoplastic and programming temperature. Additionally, in the early cycles, the shape-memory performance decreased, and in the fourth and fifth cycles, it almost stabilized. The scanning electron microscopy (SEM) photographs illustrated superior interfacial bonding and part integrity in the case of multi-material 3D printing.

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