Study the Impact of Drilling Process Parameters on Natural Fiber Reinforced Herringbone Epoxy Composites

In recent times, composite materials have gained a lot of recognition because of their superior properties due to which they find their application in a number of fields [1, 2]. They are additionally utilized in hardware, building development, furniture, control industry, oil industry, restorative industry and in numerous other mechanical items, for example, oxygen tanks, control transmission shafts and so on [3, 4]. But with these applications, the composites need to be properly selected to avoid burning and delamination can be overcome chemical treatment [5, 6].

There were many advantages in composites such as good strength to weight ratio, high creep resistance, high wear resistance, corrosion-resistant, anisotropic and many more that is why it has strong future potential and prospects. It also depends on the composites being used, whether matrix or fiber, chemical treatments [7]. In addition to that, natural fiber-reinforced composite is eco-friendly [8-10]. There were various drawbacks in using of composites such as material costs, manufacturing difficulties, difficult to repair, inspection and testing more complex and also difficult to achieve required property in required direction [11, 12]. There were various machining processes such as drilling, milling, trimming, grinding, etc. which were used to reduce the raw materials to the desired shape and while machining, it was possible that the properties of the composite may get impaired. Among the different machining operations, drilling was one of the most prominent operations for the joining of composites to structures [13]. It is defined as an operation in which a drill bit is used to make a circular hole in a workpiece. While drilling, the fiber particles come in contact with the drill bit in an intermittent fashion, thus causing delamination of the composite material. Factors such as material thickness, drill bit diameter, spindle speed, feed rate, thrust force, torque and several other factors play a pivotal role in the drilling of reinforced composites. [14, 15]

The epoxy resin was most demanding matrix material, which is used during the preparation of composites. Epoxy was a thermosetting polymer that possesses unique mechanical and resistance properties. Epoxy resins were first commercialized in 1946 and are widely used in industry as protective coatings and for structural applications, such as laminates and composites, tooling, molding, casting, bonding and adhesives, and others [16-18]. Nagamadhu et al. also worked on dynamic mechanical analysis of composites and found that bonding strength between epoxy to sisal fabric was better even at higher temperature [12]. Saba et al. worked on thermal and dynamic mechanical properties of cellulose nanofibers reinforced epoxy composites and observed that reinforcement enhanced the thermal and dynamic mechanical properties [19]. Mohan et al. observed the water barrier properties of nano clay filled sisal fiber reinforced epoxy composites and found that mechanical properties are enhanced by adding nano clay [20]. Fiore et al. observed that mechanical properties enhanced with adding Arundo Donax fillers in epoxy [21]. It was found from the literatures that mechanical and thermo-mechanical properties were enhanced by reinforcement. But machining of natural fiber epoxy composite was major limitation due to its processing parameters. Karthikeyan et al. studied the effect of drilling process parameters of natural fiber chaired epoxy composite with polyurethane foam as core material [22]. These literatures revels that need to understand the effect of drilling process parameters on sisal fabric reinforced composites.

Taguchi technique method was implemented for the design of systems with improved quality [23, 24]. With the help of Taguchi technique, the performance characteristics can be optimized for the sensitivity of the source of the design parameters [25]. This technique can be used to improve the quality of the hole produced. The drilling processes are analyzed using statistical techniques in order to determine the effect of thrust force and torque on the geometry of the bit. Ranking and finding the most optimal solution for the problem is done using this technique.

Grey relation finds its use in different fields. With the help of these method operations with multiple performance characteristics were solved effectively. This method was used to solve problems having limited information. The model made using GRA is able to handle incomplete as well as inaccurate information [26]. It was widely used in real-world applications due to its ability to find the most optimum solution for the designed problem. Management, Engineering and many other domains make use of this method. After careful analysis, it can be said that the quality of the drilled hole depends on parameters such as delamination, torque, thrust force and many more of the drilling process. Since there has been limited research done multi responses using grey relation analysis, hence this paper aims to improve the process parameters like spindle speed, bit diameter and feed rate on drilling of herringbone epoxy composites for the output characteristics like thrust force, torque and delamination.

The prepared herringbone epoxy composite is machined using VMC drilling machine by varying process parameters shown in Figure 1(c). To minimize the defects due to vibrations during machining the composite should be properly clamped using suitable fixtures. All precautions must be taken such as proper clamping, suitable coolant etc. to avoid the loss of properties of the composite. The VMC is programmed to follow a particular path by considering drilling bit diameter. A high-speed steel twist drill of a specific diameter is used and the composite material is subjected to drilling. Parameters such as drill diameter, spindle speed and feed rate are varied during its operation and it is tabulated in the Table 2 [27, 28].

Table 2. Levels of operational parameters

F_d =D_max/D_o                (1)

where,

D_max = Affected zone of maximum diameter;

D_o= Drilled hole diameter of Actual size.

The parameters such as bit diameter, spindle speed and feed rate are varied. Five different values are considered for the study. The factors with their corresponding values for different variations is tabulated. The torque and thrust force acting on the drill bit is evaluated. The process is then optimized using Taguchi’s technique using Minitab 18.

In composite materials the input parameters should be chosen optimally since it affects the machining and geometrical characteristics of drilling operation. Grey relation theory has an easier computing technique and less processing time compared to other optimizing techniques. Using grey relation technique, the responses can be understood effectively in a problem. It provides significant data about the relationship among the sequences. With minimum data the relationship with the sequences can be studied. The results of the experiments based on Taguchi’s L₂₅ orthogonal array is shown in Table 3.

To optimize several parameters using Grey relation technique, the following steps are stated below [28].

1. The sequence of performance characteristics is preprocessed.
2. From the preprocessed data deviation sequences are evaluated.
3. Grey relation co-efficient is obtained from the deviational sequence data.
4. The average Grey relation co-efficient.
5. ANOVA analysis for GRG values.

During the Grey relation technique, the variation in multiple output characteristics are preprocessed to a comparable data within 0-1. After the analysis the output parameters such as thrust force, delamination and torque are standardized by using given formula shown in Eq. (2).

Table 3. Taguchi L₂₅ orthogonal array

where,

y^o_i(p) = Sequence of original value;

y^*_i(p) = the sequence value after data-preprocessing;

maxy^*_i(p) = the maximum value of y_i^o(p);

miny^*_i(p) = the maximum value of y_i^o(p);

y^o = the desired value.

Eq. (2) shows the original responses for “lesser-the better" characteristics are preprocessed and tabulated in Table 4. The variation of sequence data can be calculated from the preprocessed data to a comparable value within 0-1. Δ_max is considered as 1 and Δ_min is considered as 0. The Grey coefficient ξ(p) can be determined by the below mentioned equation:

$\xi(\mathrm{p})=\frac{\Delta_{\min }+\xi \Delta_{\max }}{\Delta \operatorname{Oi}(p)+\xi \Delta_{\max }}$             (3)

where,

$\Delta_{\mathrm{oi}}=$ The absolute difference between y_i⁰(p) and y_i^*(p), in other words called as deviation sequence. $\xi$ coefficient used, if the $\xi$ has a larger value. But in common the value of $\xi$ is considered as 0.5.

$\Delta_{\mathrm{oi}}=\left\|\mathrm{y}_{0}^{*}(\mathrm{p})-\mathrm{y}_{\mathrm{i}}^{0}(\mathrm{p})\right\|$                 (4)

$\Delta_{\min }=\left(\begin{array}{c}\left.\min _{\forall j \in i}\right)\left(\min _{\forall p}\right)\left\|\mathrm{y}_{0}^{*}(\mathrm{p})-\mathrm{y}_{\mathrm{j}}^{*}(\mathrm{p})\right\|\end{array}\right.$            (5)

$\Delta_{\max }=\left(\begin{array}{l}\left.\max _{\forall j \in i}\right)\left(\max _{\forall p}\right)\left\|\mathrm{y}_{0}^{*}(\mathrm{p})-\mathrm{y}_{i}^{*}(\mathrm{p})\right\|\end{array}\right.$         (6)

Grey relation grade is the average of grey relational coefficient of each sequence. It is calculated from Eq. (7) as shown below:

$\gamma_{\mathrm{i}}=\frac{1}{n} \sum_{p=1}^{n} \xi i(p)$             (7)

It is quite possible that the significant factors in the real world may change according to their circumstance of unequal weight for various factors. Hence the GRG value can be calculated using Eq. (8):

$\gamma_{i}=\frac{1}{n} \sum_{p=1}^{n} w_{p} \xi_{i}(p)$             (8)

where, w_p = normalized weight of factor p, when, w_p is considered as equal for all the factors in the problem. The grey coefficient was determined using the Eq. (3) and GRG value by Eq. (8) and is tabulated in Table 5.