Integrating resistance-based sensing into fused filament fabricated mechanical metamaterial structure

Khan, Imran; Yaseen, Muhammad; Ahmad, Muhammad Mustafa; Abas, Muhammad; Shah, Muhammad Imran; Hussain, Hanbal; Hira, Fatima

doi:10.1007/s40964-024-00635-8

Cited by 2 publications

(2 citation statements)

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“…The distance of each wolf from the positions of the alpha, beta, and delta wolves is computed using Equation (6). The position of each wolf is updated based on its distance from the alpha, beta, and delta wolves using Equation (7).…”

Section: Grey Wolf Optimization (Gwo)mentioning

confidence: 99%

“…Additive Manufacturing (AM) techniques, particularly fused deposition modeling (FDM), have gained remarkable attention in recent years owing to their versatility, cost-effectiveness, rapid prototyping, and functional parts capabilities with good properties [ 1 ]. Their applications have been explored in aerospace, automobile, medical, biomedical, and electronic fields [ 2 , 3 , 4 , 5 , 6 , 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

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Parametric Optimization of FDM Process for PA12-CF Parts Using Integrated Response Surface Methodology, Grey Relational Analysis, and Grey Wolf Optimization

Almuflih,

Abas,

Khan

et al. 2024

Polymers

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Efficiently managing multiple process parameters is critical for achieving optimal performance in additive manufacturing. This study investigates the relationship between eight key parameters in fused deposition modeling (FDM) and their impact on responses like average surface roughness (Ra), tensile strength (TS), and flexural strength (FS) of carbon fiber-reinforced polyamide 12 (PA 12-CF) material. The study integrates response surface methodology (RSM), grey relational analysis (GRA), and grey wolf optimization (GWO) to achieve this goal. A total of 51 experiments were planned using a definitive screening design (DSD) based on response RSM. The printing process parameters, including layer thickness, infill density, and build orientation, significantly affect Ra, TS, and FS. GRA combines responses into a single measure, grey relational grade (GRG), and a regression model is developed. GWO is then employed to optimize GRG across parameters. Comparison with GRA-optimized parameters demonstrates GWO’s ability to discover refined solutions, reducing average surface roughness to 4.63 μm and increasing tensile strength and flexural strength to 88.5 MPa and 103.12 MPa, respectively. Practical implications highlight the significance of GWO in industrial settings, where optimized parameters lead to reduced costs and improved product quality. This integrated approach offers a systematic methodology for optimizing FDM processes, ensuring robustness and efficiency in additive manufacturing applications.

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Section: Grey Wolf Optimization (Gwo)mentioning

confidence: 99%