Multi-objective optimization of machining parameters during milling of carbon-fiber-reinforced polyetheretherketone composites using grey relational analysis
Abstract:Short carbon-fiber-reinforced composites, especially short carbon-fiber-reinforced polyetheretherketone composites (CF-PEEK), are used extensively in the engineering field because of their superior properties. However, their surface quality and material removal rate need to be optimized to satisfy design and processing requirements. This work focused on a multi-objective optimization to minimize the surface roughness and maximize the material removal rate during machining by grey relational analysis with an an… Show more
“…There are no abnormal structures to be found in Figure 17(c). 79 As a result, the predicted cubic model for the response SN ratio accurately predicts the SN ratio value for the specific wear rate.…”
Section: Resultsmentioning
confidence: 72%
“…There are no abnormal structures to be found in Figure 14(c). 79 As a result, the general linear model is sufficient to evaluate the influence of wear test parameters on the specific wear rate a-MnO 2 -epoxy nanocomposites.…”
Section: Determinations Of Optimal Sliding Wear Parametersmentioning
Wear parameter optimization is critical for the successful use of materials in load-bearing applications. This study examines the optimization of various wear parameters using the Taguchi technique for the determination of the specific wear rate of alpha-manganese dioxide (α-MnO2)-epoxy nanocomposites. Four factors viz. wt.% loading of α-MnO2, sliding distance, sliding velocity, and normal load, were selected as controllable factors and a suitable L16 orthogonal array was chosen for conducting experiments. An X-ray diffractometer and field emission scanning electron microscope were used to examine phase identification, surface morphology, and chemical composition. With the addition of α-MnO2 in the epoxy, the tensile stress is decreased, while the elastic modulus is increased. The Taguchi result shows that the wear test in optimal condition is carried out with a wt.% loading of α-MnO2 of 0.5%, a sliding distance of 500 m, a sliding velocity of 1.5 m/s, and an applied normal load of 5 N. Analysis of variance shows the wt.% loading of α-MnO2 has the most influential parameter on specific wear rate with a contribution of 87.94%. The analysis also shows that the general linear model accurately describes the effect of sliding wear parameters on specific wear rates. The Monte Carlo simulated model shows that the experimental signal-to-noise ratios are consistent with the simulated model values. The confirmation test result shows that the % error between the predicted and experimental values of the specific wear rate at the optimum level combination is 4.67%. The worm surfaces of different trial runs show that the material removal process is caused by crack propagation. High-resolution scanning electron microscopic images of fractured surfaces show the formation of a stable tribo-layer at 0.5 wt.% α-MnO2 loading resulted in a significant decrease in the specific wear rate of the sample.
“…There are no abnormal structures to be found in Figure 17(c). 79 As a result, the predicted cubic model for the response SN ratio accurately predicts the SN ratio value for the specific wear rate.…”
Section: Resultsmentioning
confidence: 72%
“…There are no abnormal structures to be found in Figure 14(c). 79 As a result, the general linear model is sufficient to evaluate the influence of wear test parameters on the specific wear rate a-MnO 2 -epoxy nanocomposites.…”
Section: Determinations Of Optimal Sliding Wear Parametersmentioning
Wear parameter optimization is critical for the successful use of materials in load-bearing applications. This study examines the optimization of various wear parameters using the Taguchi technique for the determination of the specific wear rate of alpha-manganese dioxide (α-MnO2)-epoxy nanocomposites. Four factors viz. wt.% loading of α-MnO2, sliding distance, sliding velocity, and normal load, were selected as controllable factors and a suitable L16 orthogonal array was chosen for conducting experiments. An X-ray diffractometer and field emission scanning electron microscope were used to examine phase identification, surface morphology, and chemical composition. With the addition of α-MnO2 in the epoxy, the tensile stress is decreased, while the elastic modulus is increased. The Taguchi result shows that the wear test in optimal condition is carried out with a wt.% loading of α-MnO2 of 0.5%, a sliding distance of 500 m, a sliding velocity of 1.5 m/s, and an applied normal load of 5 N. Analysis of variance shows the wt.% loading of α-MnO2 has the most influential parameter on specific wear rate with a contribution of 87.94%. The analysis also shows that the general linear model accurately describes the effect of sliding wear parameters on specific wear rates. The Monte Carlo simulated model shows that the experimental signal-to-noise ratios are consistent with the simulated model values. The confirmation test result shows that the % error between the predicted and experimental values of the specific wear rate at the optimum level combination is 4.67%. The worm surfaces of different trial runs show that the material removal process is caused by crack propagation. High-resolution scanning electron microscopic images of fractured surfaces show the formation of a stable tribo-layer at 0.5 wt.% α-MnO2 loading resulted in a significant decrease in the specific wear rate of the sample.
“…From the figure, it is observed that there is no obvious pattern or abnormal structures. 72 Therefore, the developed model is adequate to evaluate the performance index of a process.Figure 12.(a) Normal probability plot of standard residual for predicted GRG and (b) plot of standard residual with respected to fitted values for predicted GRG.…”
Section: Resultsmentioning
confidence: 99%
“…From the figure, it is observed that there is no obvious pattern or abnormal structures. 72 Therefore, the developed model is adequate to evaluate the performance index of a process.…”
Section: Regression Model For Grey Relational Gradementioning
Engineering materials and their development are essential for a civilized society, and they have always played a key role in the industrialization of a country. The performance of materials can be increased by selecting appropriate fabrication process parameters. In this paper, the effect of fabrication processing parameters on multi-responses of lightweight material, namely, functionalized multiwalled carbon nanotubes-aluminum nanocomposite have been investigated through Taguchi-based grey relational analysis. Ethanol wt. %, milling time, compaction pressure, and sintering temperature were considered controlled parameters. Phase, surface morphology, and chemical compositions of the nanocomposite have been analyzed by X-ray diffraction, scanning electron microscopy, and energy-dispersive X-ray techniques. The scanning electron microscope images revealed that the optimal parameter combination is essential for decreasing void and crack formation during the powder metallurgy process and increasing the material performance index. Grey relational analysis was performed to evaluate optimal sample fabrication processing parameters by using grey relational grade as performance index measurement. The most influential parameter was investigated using main effect plots, interaction plots, contour plots, and analysis of variance. The results show that the optimal combination of sample fabrication parameters is A1B2C3D3. The results also show that the compaction pressure has a stronger correlation to the responses with a 46.92% contribution followed by sintering temperature. The % error between the predicted and experimental grey relational grades at the optimum-level combination is 1.83%. The Monte Carlo simulation data distribution plots show that the ranges of experimental mass density and hardness values are consistent with estimated simulated model values. Further, a regression equation has also been developed to establish the predictive model for the grey relational grade. The model analysis shows that the predictive model is adequate for evaluating grey relational grades. Therefore, this study confirms that the proposed approach can be a useful tool for improving materials’ performance.
“…In order to increase the production rate, extent of quality is to be compromised, and also the extent of production rates to be compromised in order to ensure product quality [12]. Therefore, it is essential to optimize quality and productivity simultaneously in order to overcome the con ict objectives of productivity and quality [13]. Taguchi proposed a robust design called as orthogonal arrays to optimize effects of input parameters on output responses.…”
Machining of composite materials is accompanied by sever challenges. Fiber pullouts, fiber breakages, delamination of laminates, matrix burnings are some of the challenges, which brings significant quality and performance reductions of the product. These challenges able to be reduced with controlled or optimized machining parameters. This study investigates the optimization of cutting parameters for end milling of enset fiber reinforced epoxy composite materials to obtain optimum surface roughness and material removal rate simultaneously. The experimental runs designed based on Taguchi L9 (33 ) orthogonal arrays. The influence of cutting parameters is determined by the analysis of variance (ANOVA). Optimization is performed by coupling grey relational analysis and principal component analysis. The results revealed that depth of cut has greater contributions followed by spindle speed and feed rate respectively on the gray relational grades (GRG). The optimum values of surface roughness and material removal rate were obtained at spindle speed of 1000rpm, feed rate of 300mm/min and depth of cut of 1.5mm.
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