A hybrid optimization technique to control the machining performance of graphene/carbon/polymer (epoxy) nanocomposites

Kumar, Jogendra; Verma, Rajesh; Mondal, Arpan Kumar; Singh, Vijay Kumar

doi:10.1177/09673911211046789

Cited by 7 publications

(2 citation statements)

References 29 publications

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“…In production, the selection of the most suitable process parameters is of great importance in order to reduce the cost and improve the quality. The selection of appropriate process parameters in an experimental process has an important contribution to the determination of the optimal or best experimental conditions [18,19]. One of the most important parameters that affect the quality of drilled hole in the processing of composites is the selection of the drill tool material, which is a direct function of the torque and thrust force generated during drilling.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Drilling Parameters in Drilling of MWCNT-Reinforced GFRP Nanocomposites Using Fuzzy AHP-Weighted Taguchi-Based MCDM Methods

Fedai

2023

Processes

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Many problems such as delamination, cracking, fiber tearing, ovality, and surface roughness are encountered in the drilling of glass-fiber-reinforced composite (GFRP) materials. In this study, the percentage of multi-walled carbon nano tube (MWCNT), cutting tool type, feed rate, and cutting speed were selected as control factors during the drilling of MWCNT-reinforced GFRP nanocomposites. The quality characteristics of the drilling process were determined as surface roughness, delamination, torque, and thrust force. The experiments were carried out in accordance with the Taguchi L27 orthogonal array. The lowest values obtained because of the experiments were Ra = 4.95 µm, Dm = 1.099, T = 14.78 N, and F = 44.24 N, respectively. However, since each of these outputs were obtained from different experimental trials, different multi-criteria decision-making (MCDM) methods were used to optimize all outputs at the same time. First, the criteria were weighted using the fuzzy AHP method, and then the outputs were optimized using multi-criteria decision-making methods (i.e., GRA, WASPAS and VIKOR). Very close optimal ranking was obtained in all three methods. The best results were obtained for Ra = 4.86 µm, Dm = 1.13, T = 55.57 N, and F = 48.00 N. In the next step, the performance values obtained from each MCDM method were re-optimized using the Taguchi S/N ratio method. By comparing between these models, a single optimal condition for drilling is proposed. Accordingly, A2B3C1D1 (Ra = 4.86 µm, Dm = 1.10, T = 17.47 N and F = 48.33 N) for FAHP-GRA and FAHP-WASPAS and A2B3C2D2 (Ra = 5.02 µm, Dm = 1.09, T = 37.19 N and F = 45.01 N) for FAHP-VIKOR were determined as the best performing experiments. Finally, validation tests were conducted to compare the performance of the experiments. As a result, the FAHP-GRA and FAHP-WASPAS optimization with Taguchi S/N gave an unweighted improvement of 82.9% and a weighted improvement of 10.04% compared to the results of the experiment with MCDM. Compared to the results of the experiments with MCDM, S/N FAHP-VIKOR provided an unweighted improvement of 52.75% and a weighted improvement of 8.19%. According to the results obtained, for this study, FAHP-GRA and FAHP-WASPAS are more effective optimization methods than FAHP-VIKOR.

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Section: Introductionmentioning

confidence: 99%

Optimization of Drilling Parameters in Drilling of MWCNT-Reinforced GFRP Nanocomposites Using Fuzzy AHP-Weighted Taguchi-Based MCDM Methods

Fedai

2023

Processes

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“…Based on their optimization results, a better surface finish was obtained at the lowest level of feed rate. Verma et al [20] applied a hybrid method during the milling of epoxy nanocomposites reinforced by graphene oxide/carbon fiber (G/CF) to optimize the material removal rate (MRR), cutting force (Fc), and surface roughness (Ra).…”

Section: Introductionmentioning

confidence: 99%

Experimental Investigation and Optimization of Turning Polymers Using RSM, GA, Hybrid FFD-GA, and MOGA Methods

et al. 2022

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The machining of polymers has become widely common in several components of industry 4.0 technology, i.e., mechanical and structural components and chemical and medical instruments, due to their unique characteristics such as: being strong and light-weight with high stiffness, chemical resistance, and heat and electricity insolation. Along with their properties, there is a need to attain a higher quality surface finish of machined parts. Therefore, this research concerns an experimental and analytical study dealing with the effect of process parameters on process performance during the turning two different types of polymers: high-density polyethylene (HDPE) and unreinforced polyamide (PA6). Firstly, the machining output responses (surface roughness (Ra), material removal rate (MRR), and chip formation (λc)) are experimentally investigated by varying cutting speed (vc), feed rate (f), and depth of cut (d) using the full factorial design of experiments (FFD). The second step concerns the statistical analysis of the input parameters’ effect on the output responses based on the analysis of variance and 3D response surface plots. The last step is the application of the RSM desirability function, genetic algorithm (GA), and hybrid FFD-GA techniques to determine the optimum cutting conditions of each output response. The lowest surface roughness for HDPE was obtained at vc = 50 m/min, f = 0.01 mm/rev, and d = 1.47 mm and for PA6 it was obtained at vc = 50 m/min, f = 0.01 mm/rev, and d = 1 mm. The highest material removal rate was obtained at vc = 150 m/min, f = 0.01 mm/rev, and d = 1.5 mm for both materials. At f = 0.01 mm/rev, d = 1.5 mm, and vc = 100 for HDPE, and vc = 77 m/min for PA6, the largest chip thickness ratios were obtained. Finally, the multi-objective genetic algorithm (MOGA) methodology was used and compared.

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Optimization of machining parameters during milling on glass fiber-reinforced textile composite

Pathak

Sharma

Mali

et al. 2023

Multiscale and Multidiscip. Model. Exp. and Des.

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A hybrid optimization technique to control the machining performance of graphene/carbon/polymer (epoxy) nanocomposites

Cited by 7 publications

References 29 publications

Optimization of Drilling Parameters in Drilling of MWCNT-Reinforced GFRP Nanocomposites Using Fuzzy AHP-Weighted Taguchi-Based MCDM Methods

Optimization of Drilling Parameters in Drilling of MWCNT-Reinforced GFRP Nanocomposites Using Fuzzy AHP-Weighted Taguchi-Based MCDM Methods

Experimental Investigation and Optimization of Turning Polymers Using RSM, GA, Hybrid FFD-GA, and MOGA Methods

Optimization of machining parameters during milling on glass fiber-reinforced textile composite

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