4DMesh

Wang, Guanyun; Yang, Humphrey; Yan, Zeyu; Ulu, Nurcan Gecer; Tao, Ye; Gu, Jianzhe; Kara, Levent Burak; Yao, Lining

doi:10.1145/3242587.3242625

Cited by 72 publications

(25 citation statements)

References 59 publications

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“…While the prediction results can visualize the transformation trend, they are not sufficiently accurate to support design tasks that require high precision like modularization. In relatively small scales, Thermorph [1], Printed Paper Actuator [39], A-line [40], and bioLogic [46] combined parametric geometries with forward kinematics to simulate tree-topological patterns, but this approach is incompatible with more complex or larger patterns like 4DMesh [41] due to their omission of physical forces. To tackle more complex patterns, [32] and Geodesy [12,32] used linear mass-spring models to approximate the materials' transformation.…”

Section: Simulation In Morphing Materialsmentioning

confidence: 99%

“…Still, this approach requires taking small time steps to avoid divergence, leading to long simulation rollout (i.e., a trial of simulation) time and cannot afford real-time CAD interactions and iterations. Similarly, although elastic rods [31] have been used to assist the design of deformable objects, their limitations (i.e., tradeoff between noncircular cross-section shapes or viscoelastic materials [5]) make them inapplicable to certain morphing materials design spaces (e.g., the viscoelastic transformation of [40,41,47]). Compared to these methods, SimuLearn can provide more accurate predictions and support larger design spaces while requiring similar or less computation time.…”

Section: Simulation In Morphing Materialsmentioning

confidence: 99%

“…In recent years, the HCI community has become interested in using morphing materials to enable new modes of interactions. These materials allow us to create shapechanging interfaces that are electricity-free and can respond to surrounding stimuli [37], the wearer's physiological conditions [46], or to realize novel fabrication methods [41]. However, due to their spatiotemporal behaviors and nonlinear material properties, it is difficult to predict the performances of morphing materials design.…”

Section: Introductionmentioning

confidence: 99%

“…finite element analysis (FEA). Geometrical methods predict material performance by modeling the relationship between design parameters and experimental data (e.g., associating the length [1,41] or layer thickness [40] of a printed thermoplastic actuator with its resulting bending angle). While they are fast to compute, these methods often take few if any physical parameters into account, and thus are not physically accurate.…”

Section: Introductionmentioning

confidence: 99%

“…This method takes FEA-generated data to ensure simulation accuracy and uses ML to generalize and achieve fast computation. W apply this concept to 4DMesh-like 2x2 grid structures [41] to demonstrate this simulation technique and showcase SimuLearn's workflow applicability. Results show that SimuLearn can produce high-quality simulations (97% accuracy) in real-time (0.61 seconds, over 1000 times faster than state-of-the-art FEA models).…”

Section: Introductionmentioning

confidence: 99%

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SimuLearn

Yang

Qian

Liu

et al. 2020

Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology

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Morphing materials allow us to create new modalities of interaction and fabrication by leveraging the materials' dynamic behaviors. Yet, despite the ongoing rapid growth of computational tools within this realm, current developments are bottlenecked by the lack of an effective simulation method. As a result, existing design tools must trade-off between speed and accuracy to support a real-time interactive design scenario. In response, we introduce SimuLearn, a data-driven method that combines finite element analysis and machine learning to create real-time (0.61 seconds) and truthful (97% accuracy) morphing material simulators. We use mesh-like 4D printed structures to contextualize this method and prototype design tools to exemplify the design workflows and spaces enabled by a fast and accurate simulation method. Situating this work among existing literature, we believe SimuLearn is a timely addition to the HCI CAD toolbox that can enable the proliferation of morphing materials.

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Section: Simulation In Morphing Materialsmentioning

confidence: 99%

Section: Simulation In Morphing Materialsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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SimuLearn

Yang

Qian

Liu

et al. 2020

Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology

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Sustainable Morphing Matter: Design and Engineering Practices

Patel,

Zhong,

et al. 2023

Adv Materials Technologies

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Morphing matter that change shapes and properties in response to external stimuli have gained significant interests in material science, robotics, biomedical engineering, wearables, architecture, and design. Along with functional advances, there is growing pressure and interest in considering the environmental impact of morphing matter during its life cycle. The unique manufacturing and usage of morphing matter means that existing sustainable design frameworks and principles for general physical products may not apply directly. For example, manufacturing morphing matter often requires designing and predicting materials' behaviors over time, and using devices fabricated with morphing matter often involves harnessing renewable energy and self‐reconfiguration, which pose unique sustainability opportunities and challenges. This study reflects and summarizes the field's practice in sustainable manufacturing, transport, use, and end‐of‐life handling of morphing matter. The term “sustainable morphing matter” (SMM) is coined, suggesting that sustainability‐conscious factors can become an integral component of morphing matter. In addition, ways to apply sustainability‐conscious factors to augment the existing design pipeline of morphing matter are presented, and more quantitative and algorithmic‐level developments are needed to apply these factors rigorously to the design process.

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Polymer 4D printing: Advanced shape‐change and beyond

Imrie

Jin

2021

Journal of Polymer Science

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4D printing is an exciting branch of additive manufacturing. It relies on established 3D printing techniques to fabricate objects in much the same way. However, structures which fall into the 4D printed category have the ability to change with time, hence the “extra dimension.” The common perception of 4D printed objects is that of macroscopic single‐material structures limited to point‐to‐point shape change only, in response to either heat or water. However, in the area of polymer 4D printing, recent advancements challenge this understanding. A host of new polymeric materials have been designed which display a variety of wonderful effects brought about by unconventional stimuli, and advanced additive manufacturing techniques have been developed to accommodate them. As a result, the horizons of polymer 4D printing have been broadened beyond what was initially thought possible. In this review, we showcase the many studies which evolve the very definition of polymer 4D printing, and reveal emerging areas of research integral to its advancement.

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4DMesh

Cited by 72 publications

References 59 publications

SimuLearn

SimuLearn

Sustainable Morphing Matter: Design and Engineering Practices

Polymer 4D printing: Advanced shape‐change and beyond

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