Dataset Information

Bio-Inspired Motion Mechanisms: Computational Design and Material Programming of Self-Adjusting 4D-Printed Wearable Systems.

ABSTRACT: This paper presents a material programming approach for designing 4D-printed self-shaping material systems based on biological role models. Plants have inspired numerous adaptive systems that move without using any operating energy; however, these systems are typically designed and fabricated in the form of simplified bilayers. This work introduces computational design methods for 4D-printing bio-inspired behaviors with compounded mechanisms. To emulate the anisotropic arrangement of motile plant structures, material systems are tailored at the mesoscale using extrusion-based 3D-printing. The methodology is demonstrated by transferring the principle of force generation by a twining plant (Dioscorea bulbifera) to the application of a self-tightening splint. Through the tensioning of its stem helix, D. bulbifera exhibits a squeezing force on its support to provide stability against gravity. The functional strategies of D. bulbifera are abstracted and translated to customized 4D-printed material systems. The squeezing forces of these bio-inspired motion mechanisms are then evaluated. Finally, the function of self-tightening is prototyped in a wrist-forearm splint-a common orthotic device for alignment. The presented approach enables the transfer of novel and expanded biomimetic design strategies to 4D-printed motion mechanisms, further opening the design space to new types of adaptive creations for wearable assistive technologies and beyond.

SUBMITTER: Cheng T

PROVIDER: S-EPMC8261511 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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Bio-Inspired Motion Mechanisms: Computational Design and Material Programming of Self-Adjusting 4D-Printed Wearable Systems.

Cheng Tiffany T Thielen Marc M Poppinga Simon S Tahouni Yasaman Y Wood Dylan D Steinberg Thorsten T Menges Achim A Speck Thomas T

Advanced science (Weinheim, Baden-Wurttemberg, Germany) 20210514 13

This paper presents a material programming approach for designing 4D-printed self-shaping material systems based on biological role models. Plants have inspired numerous adaptive systems that move without using any operating energy; however, these systems are typically designed and fabricated in the form of simplified bilayers. This work introduces computational design methods for 4D-printing bio-inspired behaviors with compounded mechanisms. To emulate the anisotropic arrangement of motile plan ...[more]

PMID: 34258167

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Project description:In agriculture, crop planning and land distribution have been important research subjects. The distribution of land involves several multi-functional tasks, such as maximizing output and profit and minimizing costs. These functions are influenced by a variety of uncertain elements, including yield, crop price, and indeterminate factors like seed growth and suitable fertilizer. In order to address this problem, other researchers have used fuzzy and intuitionistic fuzzy optimization approaches, which did not include the indeterminacy membership functions. However, the neutrosophic optimization technique addresses the problem by using individual truth, falsity, and indeterminacy membership functions. So, to improve the optimal solution, the Neutrosophic Goal Programming (NGP) problem with hexagonal intuitionistic parameters is employed in this study. The membership functions for truth, indeterminacy, and falsity are constructed using hyperbolic, exponential, and linear membership functions. Minimizing the under deviations of truth, over deviations of indeterminacy, and falsity yields the NGP achievement function, which is used to attain optimal expenditure, production, and profit under the constraints of labour, land, food requirements, and water. Bio-inspired computing has been a major research topic in recent years. Optimization is mostly accomplished through the use of bio-inspired algorithms, which draw inspiration from natural behaviour. Bio-inspired algorithms are highly efficient in exploring large solution spaces, and helps to manage trade-offs between various goals, and providing the global optimal solution. Consequently, bio-inspired algorithms such as Grey Wolf Optimization (GWO), Social Group Optimization (SGO), and Particle Swarm Optimization (PSO) are employed in the current work to determine the global optimal solutions for the NGP achievement function. The data for the study was collected from the medium-sized farmers in Ariyalur District, Tamil Nadu, India. To illustrate the uniqueness and application of the developed method, the optimal solutions of the suggested method are compared with Zimmermann, Angelov, and Torabi techniques. The proposed technique demonstrates that the bioinspired algorithms' optimal solution to the neutrosophic goal is superior to the existing approaches.

Dataset Information

Bio-Inspired Motion Mechanisms: Computational Design and Material Programming of Self-Adjusting 4D-Printed Wearable Systems.

Publications

Bio-Inspired Motion Mechanisms: Computational Design and Material Programming of Self-Adjusting 4D-Printed Wearable Systems.

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