Optimization of Machining Parameters of Natural/Glass Fiber with Nanoclay Polymer Composite Using Response Surface Methodology

Ragunath, S.; Rathod, Meenakshi L.; Saravanan, K. G.; Rakesh, N.; Kifetew, Melkamu

doi:https://doi.org/10.1155/2023/9485769

Journal of Nanomaterials

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Manufacturing Methods and Characterization of Nanomaterials for Industry 4.0

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Research Article | Open Access

Volume 2023 | Article ID 9485769 | https://doi.org/10.1155/2023/9485769

Optimization of Machining Parameters of Natural/Glass Fiber with Nanoclay Polymer Composite Using Response Surface Methodology

S. Ragunath,¹Meenakshi L. Rathod,²K. G. Saravanan,³N. Rakesh,⁴and Melkamu Kifetew⁵

Academic Editor: Samson Jerold Samuel Chelladurai

Received08 Sept 2022

Revised15 Dec 2022

Accepted02 May 2023

Published02 Jun 2023

Abstract

Machining processes are one of the most important finishing operations in the fabrication of composites, which contain natural fibers. However, it is difficult to attain a better fishing on the final components. Hence, an attempt has been made in the work to achieve a good surface finish in compression-molded hybrid fiber composites containing nanoclay particles by optimizing the milling parameters. Experiments were conducted by using Box–Behnken design (response surface methodology (RSM)) to optimize the milling process parameters such as spindle speed (16, 24, and 32 rpm), feed rate (0.1, 0.2, and 0.3 mm/rev.), and depth of cut (1, 1.5, 2 mm) along with different vol% of nanoclay content (3%, 6%, and 9%). The surface roughness of machined fiber composite was measured, and the most influential parameters were analyzed by analysis of variance, evaluation of signal-to-noise ratio, and mathematical models of responses were developed by RSM. The experimental results (A2B1C4D3) indicated that the feed rate is one of the most significant parameters, followed by nanoclay content, depth of cut, and spindle speed. Surface roughness was found to decrease continuously (2.18–2.08 µm) with increasing nanoclay content (up to 6%) at a certain limit and further addition of clay content (above 6%); the results were declined (2.42 µm) for the same levels of other parameters.

1. Introduction

Natural fiber-reinforced polymer composites are increasing by employed for several applications, including transportation industry, aerospace, offshore and marine industries, automobile, military and defense, power generation, and household applications. For such applications, these composites required a combination of good mechanical, thermal, physical, and chemical properties along with excellent machining characteristics [1–6]. The addition of nanoclay as filler materials with natural fibers were increased the mechanical and impact properties of the developed composite because clay content increased the bonding strength between reinforcement and matrix materials. The final results indicated that the tensile strength, impact strength, and compression strength of fiber-reinforced polymer composites showed significant improvement with the addition of filler content. Naturally available materials have wax, cellulose, hemicellulose content, and lignin has moisture content. It directly affects the strength of fabricated composite specimens. Hence, the raw fibers need to be treated under alkali solutions. The chemical treatment offered better mechanical properties due to excellent adhesion between fiber and epoxy materials [7–10].

Among the various machining techniques, milling is one of the best-suited operations to produce precise and high-quality surface in fiber-reinforced polymer composites [11]. Unlike metal matrix composites, milling is a challenging operation in fibre reinforced plastic composites (FRPC). Since fiber pullouts, delamination, fiber damages, microcracking, and interlaminar crack propagation can occur. To avoid these defects in FRPC while machining, proper selection of process parameters is essential [12–16]. Optimization of surface roughness of glass fiber-reinforced plastic composites is influenced by factors such as cutting speed, feed rate, and depth of cut. Machining experiments were conducted using L27 orthogonal array by Pareto, and results were analyzed by analysis of variance (ANOVA) analysis and signal-to-noise (S/N) ratio, and it was concluded that feed rate was one of the most significant factors, followed by cutting speed [17].

Machining characteristics of glass fiber-reinforced plastics (GFRP) composites were evaluated through Grey Relational Analysis and Taguchi L27 orthogonal array techniques by Palanikumar et al. [18] metal removal rate, tool wear, and surface roughness were optimized with different process parameters. Experimental results showed that fiber orientation had the greatest influence, followed by machining time and feed rate [18]. Systematic investigations on milling of unidirectional carbon fiber-reinforced plastics composites with varying fiber orientations were carried out by Hintze et al. [19]. Results indicated that fiber orientation and choice of milling cutter were the most significant factors for the delamination of composite. During machining, delamination effects increased with repeated usage of cutting tools [18–21].

Taguchi optimization of tool life, cutting force, and surface roughness were evaluated by a study of process parameters, namely cutting speed, feed rate, and depth of cut by Lin [22]. Multiple performance characteristics evaluation by Grey Relational Analysis showed that these factors could be improved optimum cutting parameters [22]. Detailed machining studies were carried out by Azmi et al. [23] to understand the interaction effects of milling parameters (spindle speed, feed rate, and depth of cut) in kenaf fiber-reinforced plastic composites. A full factorial design was used to optimize the surface roughness factor of machined surfaces, and mathematical models were developed by response surface methodology (RSM). ANOVA was used to determine the most significant factors among the selected process parameters. It was concluded that the spindle speed and feed rate were the most influencing factors of surface roughness. During the machining of fiber composites by Vinayagamoorthy et al. [24], the optimum thrust force was obtained by using high level of speed, feed, and medium level of depth of cut, whereas torque, high speed, low feed, and low depth of cut to improve surface finishing of machined fiber composites. Depth of cut and spindle were the most influential factors on machining fiber-reinforced polymer composites [24].

Taguchi optimization method was employed in optimizing the surface roughness of the milling operation of kenaf fiber-reinforced plastic composite. Process parameters combinations chosen were cutting speed, feed rate, and depth of cut in the range of 500–1,000 rpm, 200–1,200 mm/min, and 1.00–2.00 mm, respectively. Results showed that feed rate and the cutting speed were the most influencing factors for surface roughness. Optimum process parameters obtained were 1,000 rpm cutting speed, 200 mm/min feed rate, and 1.00 mm depth of cut [25]. Babu et al. [26] were employed with three levels of cutting speed and feed rate by 5 mm diameter of end mill cutter for machining on hemp fiber-reinforced plastics, jute fiber-reinforced plastics, banana fiber-reinforced plastics, and GFRP using Taguchi technique to evaluate the factor that influences delamination factor and surface roughness. Taguchi method and Fuzzy logic optimizations were carried out on woven type jute fiber-reinforced polymer composites by Vinayagamoorthy and Rajeswari [27] using 7 mm end mill cutter by varying process parameters, namely spindle speed (210, 660, 1,750 rpm), feed rate (0.04, 0.08, 0.15 mm/rev.), and depth of cut (1, 1.5, 2 mm). The authors reported that high level of speed, feed rate, and mean level of depth of cut were observed as optimum machining parameters for obtaining high thrust force. Also, it was found that low level of depth of cut, feed rate, and high level of speed offered high torque.

From the extensive literature survey, through machining of fiber composites is a difficult operation; suitable selection of materials, proper combination of machining process parameters, and use of optimization techniques can help in achieving good machined surfaces. Most of the researchers have studied milling investigations on either single or hybrid polymer composites. The present experimental study on natural fibers and glass fiber reinforced with the different percentile of nanoclay polymer hybrid composites. End milling operation of composites with varying addition of nanoclay to optimize cutting speed, feed rate, and depth of cut. The mathematical models have been developed by using RSM, and the results were correlated with selected process parameters. S/N ratio and ANOVA analyses were examined to determine the most influential factor among selected process parameters to minimize surface roughness during milling.

2. Experimental Procedure

2.1. Materials and Methods

A 40% of reinforcement materials in the composition of developed composites were produced better mechanical strength via high interfacial bending strength between the matrix and reinforcement materials. Increasing fiber loading with matrix material leads to decline strength of composite. Hence, the 5% of NaOH-treated natural fibers, namely jute and sisal fibers, contain 10 vol% each. The natural fiber alone does not meet the required strength of the developed composites. To overcome this, synthetic fibers have been used with 15 vol% of mat-type glass fiber improve the impact properties of developed polymer composite. A small amount of nanoclay material is added as filler content with reinforcement materials to increase bonding the strength of composites. During the machining of composite structure, the delamination occurs due to poor bonding between matrix and reinforcement materials. Almost 60% of matrix materials are taken as the ratio of 10 : 1 (LY-556 epoxy resin: HY-951 Hardener) were used for rapid curing in fabrication work and to achieve a fine finished specimens on natural fiber composites. Table 1 represents the mechanical characteristics of preferred materials for the fabrication of composites [28].

Compression molding technique was employed to fabricate fiber-reinforced polymer hybrid composites with different vol% of nanoclay content having dimensions of 300 × 300 × 10 mm³. The compositions of selected materials are given in Table 2. During the fabrication of composites, glass fiber is located around the chopped form of natural fibers to cover the developed specimens for increasing bonding strength meanwhile to reduces the delamination of fibers on machining. Addition of nanoclay content is to control the melting of polymer due to thermal gradient. The specified compositions of sisal, jute fibers, and nanoclay with reinforcement materials (LY-556 epoxy resin: HY-951 Hardener) were poured inside the glass fiber on a molten box. The fabrication of composite is done under 500 psi at 95°C for 1.5 hr.

2.2. Machinbility Studies

In optimization, design of experiment plays a vital role in improving the consistency of results and to minimize the number of experiments without deviation of accuracy. RSM is one of the powerful tools for modeling and analyzing the controllable factors on responses, such as surface roughness. Delamination is one of the major defects occurring during machining fiber composites and can be minimized by improving the degree of bonding between lamina. In this work, nanoclay particles have been incorporated to enhance the interlamina bonding. For investigation on the significance of nanoclay content in machinability, the varying amount of nanoclay in composites was also considered as a material variable in designing the experiments. Table 3 shows selected independent factor and their levels [29, 30].

Box–Behnken design method was employed for selecting the independent level of factors. The interaction effects with selected factors and measured responses were developed by RSM [31]. A 27 run of experiments was developed to optimize the delamination during drilling on fiber-reinforced plastic composites using three process parameters with three levels by Taguchi analysis [32]. However, in the present work, 25 run of experiments was conducted effectively for four factors with three levels, and interrelation properties between the level of factors were examined by RSM. The combinations of different factors and their levels are presented in Table 4.

The milling operations are conducted by computer numerical control vertical machining center (HAAS Automation Inc. USA) with a maximum spindle speed of 4,000 rpm under the specified process parameters, and surface roughness on machined composite specimens (90 × 40 × 10 mm³) was measured by Mitutoyo talysurf tester [33]. The experiment results are described in Table 5. In addition to RSM, S/N ratio analysis are carried out for each experiment which is described in Table 5 by Minitab 17 tool and its one of the logarithmic function of optimization [34]. “Lower the better” approach suitable for minimizing surface roughness in S/N analysis was adopted and expressed as follows:where n is the number of observations and y is the observed data.

In surface roughness, a simple and efficient mathematical model was built-in by RSM by Design Expert 11.0 software employing four independent factors with three levels. It can be used to predict the surface roughness of composite specimens and the process parameters successfully correlated by each experiment result.where β₀, β_i, β_ii, β_ij, X_i, X_j are constant, liner coefficient, quadratic coefficient, interaction coefficient interaction independent variables, respectively, and y—response variables of fabricated composites.

The developed mathematical model was checked its fitness. If ANOVA results showed that the value of R² (adjusted) is approximately 95%, implying that the regression equation provides a better correlation of control factors with measured responses. If the value of P on developed model ≪0.05 (95% of confidence), the developed model can be considered to adequate in predicting the surface roughness [35].

3. Results and Discussion

Surface roughness of machined fiber-reinforced polymer composites was evaluated through 25 experiments as per the design matrix, and the machining of composites was completed by selected levels; the obtained results are presented in Table 5.

In general, surface roughness increased with an increment in spindle speed [23, 24]. However, from the experimental results, it can be noticed that three different response results were obtained for the same levels of feed rate (0.2 mm/rev.) and depth of cut (1.0 mm) with the variation of nanoclay content and spindle speed in the run order of 5, 9, and 10. The surface roughness was minimized (2.18–2.08 µm) because of increasing nanoclay particles and even when spindle speed was increased from 16 to 24 rpm, as can be observed from 9th to 5th run order experiments. Hence, with the addition of nanoclay, the surface roughness of composite can be controlled. It can also be noticed that further addition of nanoclay leads to increasing surface roughness, as seen from the 10th run order, since overload nanoclay filler materials developed poor bonding with the matrix materials and stress concentration [36].

ANOVA tool used to develop model under the different levels of parameters was significant. Table 6 presents the results of ANOVA analysis result on surface roughness. The obtained result shows that probability value ≪0.05, which represents that the developed mathematical model is significant since most of the influential factors have values less than 0.0001. Hence, it can be concluded that the developed model is well significant in a given level of independent factors. The Model F-value of 46.75 also confirms that the model is significant [18, 37].

The mathematical model of the surface roughness of composites is a function of independent factors, and it is represented by the following equation [38]:

The previous mathematical models were used to predict interaction effects on surface roughness. The adequacy of response models was confirmed by coefficients of determinant R². The value of R² is very near to unity indicating a high degree of correlation between the predicted and experimented data. In this investigation, the R² value is 0.985 and adjusted R² value is 0.964. In consequently, it is concluded that the experimental results and predicted data were well correlated. Hence, the developed regression model could be effectively used to predict the surface roughness of machined composites [39].

Figure 1(a) shows the relation between the experimental results and predicted data for the surface roughness of machined fiber-reinforced polymer composites. The graph indicating a very close relationship between the predicted and experimental data, and an adequate level of 98% confidence of mathematical model can be obtained for machining behavior during the milling of fiber composite structure [40, 41].

(a)

(b)

From Figure 1(b), it can be clearly observed that the residuals and run orders were well correlated and evenly distributed on both sides for the entire run of experiments in the range of ±5, described in design matrix [42].

The interactions between the given factors for surface roughness are shown in 3D surface graphs in Figure 2(a)–2(f). The interaction effects of nanoclay with feed rate (Figure 2(a)), spindle speed (Figure 2(b)), and depth of cut (Figure 2(c)) clearly show that at low level of nanoclay (3 vol%) and high level of nanoclay (9 vol%) resulted in considerable surface roughness as compared to mean level of nanoclay (6 vol%). This indicates that the addition of optimum level of nanoclay particles to fiber composite produces minimum surface roughness, whereas the addition of nanoclay above and below these levels leads to increasing surface roughness [43]. Due to excess clay of materials causes debonding with matrix and reinforcement materials in natural fiber-reinforced hybrid polymer composite [29]. The effect of feed rate with spindle speed and depth of cut on surface roughness on composite are shown in Figures 2(d) and 2(e)). With an increase in the feed rate from 0.1 to 0.3 mm/rev., the surface roughness also increased gradually with other process parameters. However, no such effects produced on spindle speed with depth of cut are shown in Figure 2(f). Minimum effect was observed at low depth of cut (1.0 mm) and it increased with increase in depth of cut [44–46].

(a)

(b)

(c)

(d)

(e)

(f)

From the analysis of all interaction graphs on machining parameters, it can be concluded that feed rate is one of the most significant factors compared to other process parameters; meanwhile, filler content act as a major role for improving surface roughness on machining of fiber-reinforced hybrid polymer composite. Results confirm that for obtaining quality of machining on composites, minimum feed rate with moderate nanoclay content is preferred.

The surface roughness experimental results on fiber-reinforced hybrid polymer composite are presented in Table 5. The 8th column of Table 5 shows the S/N ratio of response. The purpose of using the S/N ratio is to measure the performance of developed model [47]. The main effects of the surface roughness are graphically shown in Figure 3 for mean values of S/N ratio. Figure 3 shows the result of process parameters, namely nanoclay, feed rate, spindle speed, and depth of cut on surface roughness of machined composite. The slope of each factor under the levels shows influences of the response of machining operation [17, 48]. From Figure 3, it can be concluded that the feed rate is one of the most influential factors with the other process parameters.

As compared to other process parameters, feed rate is the most significant factor for required response (surface roughness), as concluded in Table 7. It simply indicates the combination of independent factors A2B1C4D3 for obtaining minimum surface roughness in the fiber-reinforced hybrid polymer composite.

4. Conclusion

Important conclusions from the present study are as follows:(i)It can be concluded that the feed rate is one of the most significant factors (A2B1C4D3), followed by nanoclay, depth of cut, and spindle speed from the previous investigation on FRP composites.(ii)The experimental results indicating that the nanoclay content has a significant influence on the surface roughness of hybrid composites.(iii)Optimum addition of clay content minimized the surface roughness (2.18–2.08 µm), while further addition with fiber materials leads to increasing of response (2.42 µm) due to excessive clay content at the same levels of other parameters.(iv)The optimum effect produced with combination levels of independent factors is A2B1C4D3 on machining composite by S/N ratio.(v)Mathematical model developed can be successfully used to calculate surface roughness and correlates well with investigational results (R²—0.985).(vi)The proper selection of machining parameters is essential to get better quality of machined surface on fiber composites.

Data Availability

The data used to support the findings of this study are included in the article.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

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Copyright © 2023 S. Ragunath et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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