Modeling and evolutionary computation on drilling of carbon fiber-reinforced polymer nanocomposite: an integrated approach using RSM based PSO

Vijayan, D.; Rajmohan, T.

doi:10.1007/s40430-019-1892-7

Cited by 24 publications

(4 citation statements)

References 43 publications

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“…Swarm intelligence algorithm is an intelligent bee colony algorithm that simulates bees searching for nectar. These algorithms have several advantages, including high-quality solutions, excellent local search capabilities, and an updated mechanism for solving problems [24,25]. In addition to improving local search capabilities, this also increases swarm stochasticity.…”

Section: Swarm Intelligence Algorithmmentioning

confidence: 99%

Experimental Studies on Abrasive Water Jet Cutting of Nano SiC Particles Filled Hybrid Basalt-Glass Fibre-Reinforced Epoxy Composites

Vijayabhaskar,

Rajmohan,

Vijayan

et al. 2023

J. Res. Updates Polym. Sci.

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Abrasive water jet machining (AWJM) is extensively beneficial in machining materials that are hard to cut. This investigation deals with AWJM of Nano SiC filled Epoxy reinforced with basalt-glass fiber hybrid composite. The composite is prepared by compression moulding technique. Experimental trails are performed to evaluate the impact of every process parameter on the responses i.e., surface roughness (Ra) and Material Removal Rate (MRR). The experiments are conducted by changing the standoff distance (SD), traverse speed (TS) and water pressure. The performance of the conducted experiment is analysed using a Swarm intelligence algorithm. Surface roughness and MRR are maximized by using the combination of optimum process parameter levels of 9.72 mm/min speed, 5.78 mm stand-off distance and 553 MPa jet pressure. Scanning Electron Microscopic (SEM) images are employed in detecting the morphology of machined surface and confirmed the presence of voids and fibre pull-out.

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Section: Swarm Intelligence Algorithmmentioning

confidence: 99%

Experimental Studies on Abrasive Water Jet Cutting of Nano SiC Particles Filled Hybrid Basalt-Glass Fibre-Reinforced Epoxy Composites

Vijayabhaskar,

Rajmohan,

Vijayan

et al. 2023

J. Res. Updates Polym. Sci.

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“…Step 2: The predictive models of the R a , R y , and T M are then developed regarding the process inputs using RSM models [16,17].…”

Section: Optimization Frameworkmentioning

confidence: 99%

Multi-responses optimization of finishing honing process for surface quality and production rate

Nguyen

Duong

2020

J Braz. Soc. Mech. Sci. Eng.

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The finishing honing process is an effective machining to enhance surface properties. The objective of this work is to optimize the machining parameters, including the tangential speed (H), linear speed (L), and grit size (G) for minimizing the average roughness (R a ), maximum height roughness (R y ), and machining time (T M ). The honing experiments were performed with the aids of an industrial machine and the Box-Behnken experimental matrix. The nonlinear relationships between machining parameters and honing responses were developed using response surface method models. Subsequently, two optimization techniques, including the desirability approach and non-dominated sorting genetic algorithm II (NSGA II), were used to solve the trade-off analysis among three technological responses and find the optimal factors. Finally, the machining time reductions were assessed in consideration of constrained roughness properties. The obtained results showed that surface roughness and machining time were strongly influenced by abrasive grit size, followed by the tangential speed and linear speed. The optimal values of the H, L, and G were 36.0 m/min, 9.5 m/min, and 220 FEPA, respectively. The reductions in the average roughness, maximum height roughness, and machining time are 53.13%, 8.93%, and 13.95%, respectively, as compared to common values used. Moreover, the genetic algorithm-based approach could be employed to produce reliable values in comparison with the desirability approach. The outcome is expected as a technical solution to enhance the surface properties and productivity of the finishing honing process.

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“…Singh et al 31 used PSO and bacteria foraging optimization (BFO) to obtain the optimal processing parameters for milling of Inconel 718 alloy, and their results indicated that PSO was more effective. Vijayan et al 32 used response surface method (RSM) hybrid PSO to optimize the parameters for drilling of carbon fiber-reinforced polymer (CFRP). Gupta et al 33 optimized the processing parameters for machining of a nickel-based superalloy by combining PSO with TLBO.…”

Section: Introductionmentioning

confidence: 99%

Prediction and optimization of hobbing parameters for minimizing energy consumption and gear geometric deviations

Wang,

Dong

2024

Advances in Mechanical Engineering

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Improving gear precision and achieving green sustainability in gear machining are two important aspects of the gear manufacturing process. However, to achieve these two goals simultaneously, it may be necessary to make trade-offs when selecting the gear processing parameters. In this work, both energy consumption and gear geometric deviations were considered simultaneously to optimize the hobbing parameters. The relationships between the hobbing parameters, energy consumption, and gear geometric deviations were modeled using the response surface method (RSM). The statistical significance of the model was tested using analysis of variance (ANOVA). An improved multi-objective particle swarm optimization (IMOPSO) was then performed to solve optimization problems that involved multiple and conflicting objectives in the hobbing process. The results obtained indicate that both the energy consumption ( E) and the gear geometric deviations are parameter-dependent. The feed rate ( f) and the spindle speed ( n) have opposing effects on both energy consumption E and the gear geometric deviations. The optimum hobbing parameter sets obtained from the calculated Pareto frontier can provide a feasible solution for manufacturers to solve the trade-off problems that occur in the hobbing process, and the experimental results confirmed the effectiveness of the IMOPSO approach.

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Modeling and evolutionary computation on drilling of carbon fiber-reinforced polymer nanocomposite: an integrated approach using RSM based PSO

Cited by 24 publications

References 43 publications

Experimental Studies on Abrasive Water Jet Cutting of Nano SiC Particles Filled Hybrid Basalt-Glass Fibre-Reinforced Epoxy Composites

Experimental Studies on Abrasive Water Jet Cutting of Nano SiC Particles Filled Hybrid Basalt-Glass Fibre-Reinforced Epoxy Composites

Multi-responses optimization of finishing honing process for surface quality and production rate

Prediction and optimization of hobbing parameters for minimizing energy consumption and gear geometric deviations

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