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Artificial Intelligence and Digitalization for Sustainable Development. 10th EAI International Conference, ICAST 2022, Bahir Dar, Ethiopia, November 4-6, 2022, Proceedings

Research Article

Process Parameter Optimization of Single Lap-Adhesive Joint Date Palm Fiber Reinforced Polyester Composite Using ANN-Genetic Algorism

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  • @INPROCEEDINGS{10.1007/978-3-031-28725-1_2,
        author={Ermias Wubete Fenta and Assefa Asmare Tsegaw},
        title={Process Parameter Optimization of Single Lap-Adhesive Joint Date Palm Fiber Reinforced Polyester Composite Using ANN-Genetic Algorism},
        proceedings={Artificial Intelligence and Digitalization for Sustainable Development. 10th EAI International Conference, ICAST 2022, Bahir Dar, Ethiopia, November 4-6, 2022, Proceedings},
        proceedings_a={ICAST},
        year={2023},
        month={3},
        keywords={DPFRPC Single lap Adhesive joint ANN GA Tensile strength},
        doi={10.1007/978-3-031-28725-1_2}
    }
    
  • Ermias Wubete Fenta
    Assefa Asmare Tsegaw
    Year: 2023
    Process Parameter Optimization of Single Lap-Adhesive Joint Date Palm Fiber Reinforced Polyester Composite Using ANN-Genetic Algorism
    ICAST
    Springer
    DOI: 10.1007/978-3-031-28725-1_2
Ermias Wubete Fenta,*, Assefa Asmare Tsegaw
    *Contact email: ermiw2010@gmail.com

    Abstract

    Adhesive joining of composite materials is rapidly increasing in different engineering application areas such as aerospace, maritime and automotive, due to its potential for lightweight products. However, the use of adhesive joining for this purpose might lead to failure when a tensile load is acting on the composite. This work focus on the process parameters optimization of single lap adhesive joint Date palm fiber reinforced polyester composite (DPFRPC) to improve its joint strength. The study was conducted experimentally by making single-lap adhesive joining of DPFRPC under tensile testing. The key parameters influencing the adhesively joint's performance such as overlapping length (24, 40, and 56 mm), width (20, 28, and 36 mm), and adhesive thickness (0.5, 0.75, 1 mm) were studied using L9orthogonal array experimental design. Artificial neural network (ANN) modeling tool was utilized to relate input and output parameters. The best parameter combinations were found using a genetic algorithm (GA) optimization technique. Using this technique, the optimum parameters of single lap adhesive joint DPFRPC were, 56 mm overlapping length, 36 mm width, and 0.95 mm adhesive thickness, with a load carrying capacity of 9.48 kN.

    Keywords
    DPFRPC Single lap Adhesive joint ANN GA Tensile strength
    Published
    2023-03-19
    Appears in
    SpringerLink
    http://dx.doi.org/10.1007/978-3-031-28725-1_2
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