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      Interface stress transfer model and modulus parameter equivalence method for composite materials embedded with tensile pre-strain shape memory alloy fibers

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          Abstract

          The constitutive model and modulus parameter equivalence of shape memory alloy composites (SMAC) serve as the foundation for the structural dynamic modeling of composite materials, which has a direct impact on the dynamic characteristics and modeling accuracy of SMAC. This article proposes a homogenization method for SMA composites considering interfacial phases, models the interface stress transfer of three-phase cylinders physically, and derives the axial and shear stresses of SMA fiber phase, interfacial phase, and matrix phase mathematically. The homogenization method and stress expression were then used to determine the macroscopic effective modulus of SMAC as well as the stress characteristics of the fiber phase and interface phase of SMA. The findings demonstrate the significance of volume fraction and tensile pre-strain in stress transfer between the fiber phase and interface phase at high temperatures. The maximum axial stress in the fiber phase is 705.05 MPa when the SMA is fully austenitic and the pre-strain increases to 5%. At 10% volume fraction of SMA, the fiber phase’s maximum axial stress can reach 1000 MPa. Ultimately, an experimental verification of the theoretical calculation method’s accuracy for the effective modulus of SMAC lays the groundwork for the dynamic modeling of SMAC structures.

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          Active composites based on shape memory polymers: overview, fabrication methods, applications, and future prospects

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            An Algorithm for Painting Large Objects Based on a Nine-Axis UR5 Robotic Manipulator

            An algorithm for automatically planning trajectories designed for painting large objects is proposed in this paper to eliminate the difficulty of painting large objects and ensure their surface quality. The algorithm was divided into three phases, comprising the target point acquisition phase, the trajectory planning phase, and the UR5 robot inverse solution acquisition phase. In the target point acquisition phase, the standard triangle language (STL) file, algorithm of principal component analyses (PCA), and k-dimensional tree (k-d tree) were employed to obtain the point cloud model of the car roof to be painted. Simultaneously, the point cloud data were compressed as per the requirements of the painting process. In the trajectory planning phase, combined with the maximum operating space of the UR5 robot, the painting trajectory of the target points was converted into multiple traveling salesman problem (TSP) models, and each TSP model was created with a genetic algorithm (GA). In the last phase, in conformity with the singularities of the UR5 robot’s motion space, the painting trajectory was divided into a recommended area trajectory and a non-recommended area trajectory and created by the analytical method and sequential quadratic programming (SQP). Finally, the proposed algorithm for painting large objects was deployed in a simulation experiment. Simulation results showed that the accuracy of the algorithm could meet the requirements of painting technology, and it has promising engineering practicability.
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              Characterization of a shape memory alloy hybrid composite plate subject to static loading

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                Author and article information

                Contributors
                Role: ConceptualizationRole: Project administrationRole: Resources
                Role: MethodologyRole: SoftwareRole: Writing – original draft
                Role: Formal analysisRole: Validation
                Role: InvestigationRole: Validation
                Role: Data curationRole: Validation
                Role: SupervisionRole: Visualization
                Role: ConceptualizationRole: Funding acquisitionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLOS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                14 May 2024
                2024
                : 19
                : 5
                : e0302729
                Affiliations
                [1 ] School of Mechanical Engineering, Hubei University of Technology, Wuhan, China
                [2 ] State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, China
                [3 ] Dongfeng Liuzhou Motor Co., Ltd., Liuzhou, China
                [4 ] School of Mechanical and Electrical Engineering, Hainan University, Haikou, China
                Semnan University, ISLAMIC REPUBLIC OF IRAN
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0001-7117-6255
                https://orcid.org/0000-0002-9709-7851
                https://orcid.org/0000-0001-8159-2420
                Article
                PONE-D-23-32369
                10.1371/journal.pone.0302729
                11093366
                f306359f-bb0c-4763-93eb-29e29ef4ba8c
                © 2024 Huang et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 5 October 2023
                : 11 April 2024
                Page count
                Figures: 13, Tables: 2, Pages: 24
                Funding
                Funded by: Guangxi Innovation Driven Development Special Fund Project
                Award ID: AA22068060-6
                Award Recipient :
                Funded by: Hubei University of Technology Doctoral Research Initiation Fund
                Award ID: XJ2022001101
                Award Recipient :
                This research was funded by Hubei University of Technology Doctoral Research Initiation Fund, grant number XJ2022001101 and Guangxi Innovation Driven Development Special Fund Project, grant number AA22068060-6. The Hubei University of Technology Doctoral Research Initiation Fund (approval number XJ2022001101) and the Guangxi Innovation and Development Special Fund project (approval number AA22068060-6) are responsible for funding this research. Both funds supplied funding for this study's computations, sample materials, processing, manufacturing, experimental testing, and other tasks. As the article's first author, Huang Yizhe headed the research design, data collection and analysis, publication decisions, and manuscript preparation. This study has significant scientific importance and hinges on project requirements.
                Categories
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                Materials Science
                Materials
                Composite Materials
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                Composite Materials
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                Physical Sciences
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                Classical Mechanics
                Mechanical Stress
                Shear Stresses
                Physical Sciences
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                Fibers
                Earth Sciences
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                Physical Sciences
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                Physical Sciences
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