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Abstract

The building industry has recently experienced a surge in the use of wood plastic composite (WPC) materials, which are lightweight, easy to work with, and visually appealing. This study aims to analyze and optimize drilling parameters to minimize delamination at the entrance and exit of the hole, leading to defect-free products. To analyze delamination, three input parameters for the drilling process are selected to measure the delamination factor (F_d) at both the entrance and exit of the hole. The experimental data are validated using the developed regression and fuzzy models. Results show that feed rate (FR) and spindle speed (SS) are the primary factors in reducing delamination. Desirability-based optimization suggests an optimal SS of 3000 rpm, a FR of 75 mm/min, and a drill diameter of 6 mm, with output responses of F_d, 1.04454 (entry), and 1.42694 (exit). Additionally, metaheuristic algorithms such as particle swarm and hippopotamus optimization algorithms are employed to determine the optimal drilling conditions. This paper highlights the importance of optimizing drilling parameters when creating holes in WPC panels for various construction uses.

Keywords

wood plastic composite delamination Taguchi’s DoE response surface methodology particle swarm optimization hippopotamus optimization

Introduction

The diverse sizes, shapes and characteristics of composite materials, such as strength, strength-to-weight ratio, resistance to corrosion, and design flexibility, represent a remarkable group of engineering materials.¹ The range of applications utilizing composite materials continues to expand, including sporting goods, shipping, automobiles, aerospace, and civil engineering.² The design flexibility made possible by the ability to thermally form wood polymer composites (WPC) into intricate shapes is a tremendous advantage in industries that require made-to-order parts.³ The innovative combination of phases in wood composites within a single material is remarkable. By fusing the distinct elements, each of which has its unique properties and crystal structure, new material characteristics can be achieved.

As a result, the composite exhibits an enhanced set of attributes not found in the individual components.⁴ Achieving strength effectiveness, environmental sustainability, and resource sustainability are the results of the combination of the reinforcement and the matrix while reducing ecological impact.⁵ For wood composites, design ease and flexibility with respect to the required sizes, shapes, and proportions facilitate the achievement of specified performance and quality criteria.⁶ In the reduction of ecological footprint in the construction sector, wood composites constitute one of the materials for eco-friendly construction.⁷ This composite construction material, which consists of lignocellulose, lignin, a mineral thermoplastic, mineral powder, plasticizer, impact-modifier, and several processing additives, is innovative in the sense that it offers waste utilization and ecological preservation in construction.⁸ In construction engineering, wood composites are becoming more and more popular because of their many advantages, including lower heat dissipation, higher dielectric stability, durability, corrosion resistance, strength and stiffness, low weight, and a significant reduction in CO₂ emissions and the environmental footprint.⁹ As previously stressed, construction engineers are increasingly using particle natural composites in various energy-saving building materials. The use of hybrid wood-based material structures, which combine several materials in construction, is growing in popularity because of their strength, affordability, and environmental benefits. As established^10,11,12 materials represent one of the bases for the energy conservation of a building.

Wood–plastic composite (WPC) materials are comprised of palm, bamboo, and other wood plant fibers along with thermoplastics, polyethylene, polypropylene, and polyvinyl chloride (PVC) to name a few. WPC merged with lignocellulosic fibers and thermoplastics has positioned WPC as a key prefabricated architecture building material due to their recyclable and environmentally sustainable building, antibacterial, and water-resistant characteristics.¹³ The WPC materials and bio-circular green economy (BCG) policy¹⁴ has sparked extreme interest. WPC has been progressively used as a partial replacement to engineered wood products and conventional items of MDF (Medium Density Fiberboard), plywood (PW), particleboard (PB), oriented strand board (OSB), wood veneer (WV), and laminated veneer lumber (LVL).¹⁵ WPC materials are used for non-bearing construction and perform as MDF and particleboard material replacements for high value applications in the automotive sector. Fiber-reinforced composite materials do not corrode, are light in weight, and have high strength, extreme fracture toughness and desirable properties as compared to monolithic construction materials.¹⁶ Ultimately, composite construction is to develop these integrated elements.

Due to the difficulties in welding fiber-reinforced composites, which include WPC, construction using these materials is predominantly through mechanized fastening. The type and properties of reinforcing fibers in the composite influence the machinability of the fiber-reinforced plastics.¹⁷ Drilling is an important part of this process as it allows for screw or bolt insertion, facilitating assembly.¹⁸ For assembly, numerous holes must be created, and mechanical drilling is a particularly efficient technique for this activity.¹⁹

Wood plastic composites are lightweight materials with specific mechanical properties, including high stiffness-to-weight ratios, excellent durability, impact resistance, and ease of drilling. However, drilling also causes specific problems such as delamination, fiber pullout, microcracking, and burning.²⁰ Of the several potential problems, research on drilled composites focuses on the delamination of wood composite material. This is due to its critical influence on the composite’s structural integrity and failure mechanisms. Delamination is the failure of a composite material in which internal layers separate, and is of particular interest in composite or laminated materials. Delamination significantly impacts the quality of drilled wood-based panels by causing defects around the hole edges. This can compromise the structural integrity and spoil the final appearance. It mainly occurs during machining and is affected by factors such as feed rate, cutting speed, drill tip angle, and tool condition. Studies show that minimizing delamination is critical for improving surface quality and avoiding flaws that could weaken the structure or harm aesthetics, especially in furniture and construction. Employing smaller drill tip angles and optimizing feed rates can reduce delamination.²¹ The various reason for delamination includes technological, free edge connection, percussion loads, cyclic loading, temperature changes, high radiation levels, hygroscopic effect, and environmental factors. Defects caused by drilling erode the holes and enlarge them while assembly,²² weakening the structural reliability and lowering the service lifespan.²³ These flaws cause anisotropy and heterogeneity, as well as changes in the fiber’s hardness and abrasiveness.²⁴ The poor-quality holes are considered the reason for up to 60% of the parts rejection.²⁵ Several researchers have directed their investigation towards this matter. Prakash et al.²⁶ investigated the delamination factor (F_d) of MDF using a desirability-based approach. In this work, spindle speeds (1000, 2000, 3000 rpm), feed rates (FR)(100, 300, 500 mm/min), and drill diameters (DD) (4, 8, 12 mm) have been used. The experimental outcomes established that the F_d drops with rising SS and FR. The best settings (2937 rpm, 101 mm/min, and 4 mm) yield the lowest F_d. Valarmathi et al²⁷ found that the best delamination results are obtained with lowering f and d when using HSS drills for drilling of pre-laminated MDF. Shirzaei et al.²⁸ studied the effects of depth of cuts (4, 8, 12 mm), feed speeds (5, 13 mm/s), and a constant SS of 8000 rpm on the drilling of wooden-sandwich panels. The test results demonstrate that the F_ddecreases as the DD and FR decrease, and surges as DD and FR tend to rise. The best quality hole was obtained with a DD of 4 mm and a FR of 5 mm/s.

To lessen the number of flaws and evaluate the performance of drilling equipment with the help of response surface methodology (RSM), Elhadi and others¹⁸ studied the drilling of hybrid jute palm composite and optimized the drilling parameters with three types of drills: High-Speed Steel (HSS), HSS-Co5 with 5% cobalt, and cemented carbide (carbide). The results show that the HSS drill, at 0.04 mm/rev and 1592 r/min, minimizes the delamination factor (F_d), circularity, and cylindricity of the hole. Jaiprakash et al²⁹ examined the drilling of Neem wood veneer epoxy composite and used HSS drills of various diameters: 4, 6, 8, 10, and 12 mm. Cutting conditions are encompassed by FR per revolution values of 30, 40, 50, 60, and 70 mm/rev and 450, 852, 1260, 1860, and 2700 rpm of SS. Results showed that while the F_d lowers with higher SS, it rises with higher FR. The optimal conditions are 2700 rpm (SS), 50 mm/min (FR), 6 mm (DD). These principles mitigate the impacts of torque, thrust, and F_d, thus enhancing the quality of drilled holes. With respect to drilling MDF panels, Bedelean et al.³⁰ examined how drilling parameters pertaining to the cutting action influence the thrust force, torque, and F_d, concentrating on systems with several drills. Prediction of the results under different conditions and experimental validation were performed using Response Surface Methodology (RSM), Analysis of Variance (ANOVA), and Artificial Neural Networks (ANN). The results show that the ANN model outperforms the RSM model, with drill type having a significant impact on tooth bite and drill tip angle. This research may lower torque, thrust force, and F_d. Prakash and Ramamoorthy³¹ investigated surface roughness and F_d caused by carbide spade drills used to drill PB panels. A 12 mm carbide spade drill is used for drilling, along with SSs of 1000, 2000, and 3000 r/min and FRs of 100, 300, and 500 mm/min. The compliance testing using RSM software findings confirms the results, which show that high SS (5000 r/min) at low FR (100 mm/min) are the best options for lowering F_d and surface roughness.

Among the wood composite materials, the usage of WPC materials could be a current research topic due to their enhanced properties like durability, weather resistance, and appearance in various industrial applications, especially in construction engineering. Studying the drilling of WPC can yield significant contributions in machining, structural analysis, and sustainability due to its hybrid nature and industrial relevance. Compared to other wood composite materials, wood plastic composites have environmental protection and sustainable development. The primary characteristics of quality drilled holes are accuracy of hole size, divergence from circularity, delamination, heat-affected region, and excellent surface polish. Quality criteria for drilled holes in construction engineering include accurate hole diameter and depth and alignment, as well as the absence of damages and defects like delamination. Since interconnected materials possess different severities of machinability, real-time cutting parameter optimization is displayed within machining operations.³² For materials science and engineering, advanced cutting parameters for hybrid composite drilling are particularly beneficial.³³

For the optimization of drilling performance and the attainment of component quality, tool geometry, cutting parameters, and material properties must be considered.³⁴ This study aimed at reducing delamination, the most influential defect in drilling wood composite materials, by optimizing the drilling parameters. The drilling experiment on WPC using carbon steel with an aluminum finish was conducted, and the determination of cutting parameters was based on comprehensive research and evaluation of available research articles on similar cutting conditions.

While many studies target the drilling of composite materials, most are focused on the traditional fibre-reinforced plastics and are constrained in terms of process settings. Research on WPC remains scarce, especially regarding the use of statistical and advanced AI-based optimization methods. This study optimizes SS, FR, and DD concerning the F_d at both the hole entry and exit, by combining RSM, a Fuzzy Inference System (FIS), and metaheuristic algorithms viz., Particle Swarm Optimization (PSO) and Hippopotamus Optimization (HO) algorithms to identify improved optimal solutions. The findings provide useful guidance for industrial WPC drilling, aiming to improve hole quality, reduce tool wear, and conserve energy across industries such as furniture, automotive, and construction. Overall, this research offers a validated approach for practical machining optimization and deepens academic insight into WPC machinability.

Material and methods

Drilling of WPC is mainly for fastening components together, forming joints, or creating decorative features. The quality of the holes is influenced by the drilling settings and the materials being drilled. SS, FR, and DD are the three key cutting variables in the drilling process. This project aims to investigate how different cutting settings impact the current investigation. The planned experiment is structured according to the Taguchi L₂₇ orthogonal array to examine variables with as few experiments as possible and to optimize the drilling parameters that could reduce undesirable outcomes.^35,36 The attributes of the WPC composite under examination are presented in Table 1.

Table 1.

WPC wood composites’ constituents.

WPC material composition
Wood Fibers (40%)	Thermoplastics (54%)	Additives: Colour pigments, UV stabilizers, and antifungal agents (6%)
Tensile strength: 35–40 MPa	Hardness (Shore D): 65–70	Density: 900 kg/m³

In drilling wood–plastic composites, delamination is primarily influenced by thrust force, heat generation, and chip evacuation. Introducing cooling or lubrication (compressed air, MQL, or mist cooling) may improve the quality. However, lubrication or cooling effects can cause swelling or moisture absorption in wood-based fibers, which are hydrophobic in nature, potentially affecting dimensional stability.

The drilling operation is carried out on 17 mm thick laminated WPC by IS16359:2015 on a CNC vertical machining Centre (SIEMENS 802D Make) with the spindle power of 10 kW, with the SS range of 60-6000 rpm under dry conditions, as per L₂₇ orthogonal array. The drill bits chosen are brad point carbon steel of aluminium finish with 6, 8, and 10 mm as shown in Figure 1 with a point angle of 118°, spiral flute, with 2 flute, and rake angle of 25°. The cutting parameters are carefully picked after thoroughly examining the literature.^26,27,37 Table 2 lists the cutting ranges and characteristics used in the experiment. Three 100 × 100 mm WPC panels had a total of 27 holes drilled in them.

Figure 1.

Drill bits of diameters 6,8 and 10 mm.

Table 2.

Input process variables.

Factor	Notation	unit	Levels
Factor	Notation	unit	1	2	3
Spindle speed	SS	rpm	1000	2000	3000
Feed rate	FR	mm/min	75	150	225
Drill diameter	DD	mm	6	8	10

Delamination Measurement

When assessing delamination in drilled WPC, using an ARCS SVP 2010 video measuring instrument (range: X axis - 200 mm/Y axis - 100 mm), which guarantees precise and consistent determination of the F_d. Unlike traditional photographic methods that often encounter issues like angle distortion, poor lighting, and operator variability, the SVP 2010 offers orthogonal, high-resolution images under controlled lighting conditions. This allows for clear visualization of the borehole edges and damaged zones around them. The SVP 2010 stores borehole images in a traceable digital format with embedded scale references, ensuring a reproducible dataset for future use. To avoid uncertainty and measuring errors, the video measuring instrument ARCS make, model SVP 2010 was calibrated before the measurement, which avoids uncertainty. Each measurement was made thrice, and the repeatability is ensured. The 2D top-view images of the hole were captured through the 2D measurement mode (Figure 2). The damaged area is measured by drawing two concentric circles, with a smaller diameter circle indicating the actual hole diameter (D) and the larger diameter (D_max) used to measure the region of delamination after drilling to determine F_d as shown in equation (1).³⁸ In terms of modeling, 3D RSM curves are created using Design Expert software. The input parameters include DD, FR, and SS, while the output parameters include entry F_d (F_{d_entry}) and exit F_d (F_{d_exit}). The methodology used in this study is presented in Figure 3.

F_{d} = \frac{D_{\max}}{D}

(1)

Figure 2.

Delamination zone measurement.

Figure 3.

Methodology of the research used in this study.

Results and discussion

The upward force applied by the drill flutes to the cut layer causes delamination at the hole’s entrance, separating it from the uncut layer. Because, in drilling, the thrust force produced is greater than the debonding of the two adjacent composite layers, delamination occurs at the hole end. This step involves delamination among plies, adhesive layer debonding/fracture as a result of drilling. The outcomes of the trials reveal the impact of machining variables on the delamination of WPC. Figure 4 visualizes the post-drilling of WPC with 27 holes, with a hole at entry (Figure 4(a-c)) and exit (Figure 4(d-f)) under prescribed input parameters, demonstrating that delamination is the output response in assessing the quality of the drilled hole. The experiments are performed thrice, and a total of 81 results are obtained. The average of the three trial conditions is used for the subsequent analysis. Table 3 lists the experimental conditions and results used at various trials. A rise in FR from 75 to 225 mm/min causes more F_{d_entry} and F_{d_exit} at lower SS (1000 rpm), suggesting that larger thrust forces at higher FRs worsen delamination.³⁹ The effect is particularly pronounced for larger DDs (8 mm and 10 mm), which consistently show higher F_d compared to the smaller 6 mm drill. Increasing the SS to 2000 r/min reduces delamination at lower FRs due to reduced thrust and better cutting efficiency; however, delamination increases again at higher FRs and larger DDs.⁴⁰ Particularly with smaller drills (6–8 mm), F_{d_entry} is reduced at the lowest FR (75 mm/min) at the maximum SS (3000 rpm), indicating ideal cutting conditions. However, the F_{d_entry} and F_{d_exit} upsurges with DD and FR, confirming that these two factors are the main causes of delamination.

Figure 4.

Drilled panel (a,b,c) Hole at entry, (d,e,f) Hole at exit.

Table 3.

Experimental conditions and results.

Trial no.	SS (rpm)	FR (mm/min)	DD (mm)	F_{d_entry}	F_{d_exit}
1	1000	75	6	1.2	1.42
2	1000	75	8	1.26	1.45
3	1000	75	10	1.21	1.54
4	1000	150	6	1.19	1.52
5	1000	150	8	1.22	1.54
6	1000	150	10	1.32	1.64
7	1000	225	6	1.36	1.66
8	1000	225	8	1.58	1.67
9	1000	225	10	1.56	1.65
10	2000	75	6	1.05	1.41
11	2000	75	8	1.12	1.39
12	2000	75	10	1.21	1.41
13	2000	150	6	1.16	1.46
14	2000	150	8	1.26	1.45
15	2000	150	10	1.41	1.58
16	2000	225	6	1.35	1.63
17	2000	225	8	1.4	1.69
18	2000	225	10	1.48	1.75
19	3000	75	6	1.03	1.42
20	3000	75	8	1.02	1.43
21	3000	75	10	1.04	1.43
22	3000	150	6	1.17	1.48
23	3000	150	8	1.31	1.43
24	3000	150	10	1.33	1.51
25	3000	225	6	1.32	1.59
26	3000	225	8	1.47	1.72
27	3000	225	10	1.51	1.75

The F_d is the most substantial output response when determining the quality of a hole; FR and SS have an important effect on reducing F_d in WPC. Figure 5 shows a bar chart with the mean response for different DDs (6, 8, and 10 mm) and F_{d_entry} and F_{d_exit} at varied FR and SS. The optimal input parameters for various SS and FR are underlined in bold, indicating the optimal configuration for achieving the lowest F_d and thereby improving the drilling performance of the WPC panel. The plot of mean factor effects for DD, FR, and SS is shown in Figure 6, demonstrating that the most important element in raising the F_d_{_entry} and F_{d_exit} of the hole in the WPC panel is the FR. Figure 5 and 6 indicate that the SS of 3000 rpm, 75 mm/min FR, and 6 mm DD are the ideal values for lowering the F_d_{_entry} and F_{d_exit}. The most important of the three input parameters for WPC drilling is FR, trailed by SS and DD. The optimum F_d for wood composites like MDF, Plywood, PB panels, and Neem wood/Veneer epoxy composite is displayed in Table 4, and the results obtained are consistent with the other previous research, as depicted.

Figure 5.

Response for means for different DDs (6, 8 and 10 mm).

Figure 6.

Factor effect on the performance metrics.

Table 4.

Comparative studies on F_d in wood composite drilling.

Drill material	Wood composite	Cutting parameters			$F_{d}$	Reference
Drill material	Wood composite	SS (rpm)	FR (mm/min)	DD (mm)	$F_{d}$	Reference
Carbide	MDF	2950	100	4	1.02 (Entry) 1.36 (Exit)	[22,39]
Carbide	PB	3000	300	10	1.00 (Entry) 1.08 (Exit)	[31]
	MDF				1.03 (Entry) 1.13 (Exit)
	Ply wood				1.05 (Entry) 1.00 (Exit)
HSS	Neem wood/Veneer epoxy	27000	50	6	1.00 (Entry) 1.002 (Exit)	[29,23]
Carbide	PB panels	5000	100	12	1.01	[27,40]

Figure 7 displays the SEM picture of the WPC composite panel at low FRs and higher SSs. The image shows the unevenness and roughness after the drilling process, which indicates the fiber pull-out and non-uniform textures and irregular layers, indicating that the material has undergone mechanical stress and fracture⁴¹ Areas with clustered wood sawdust significantly weaken the mechanical properties of the WPC composite because they are full of porosity and voids, making them more susceptible to crack initiation and propagation under lower stress levels. The SEM image highlights micro-defects and morphological changes induced by drilling, such as delamination between the matrix and wood sawdust, with micro-gaps that can reduce mechanical strength. In Trial 6, F_d_{_entry} = 1.32 and F_d_{_exit} = 1.64, the wall exhibits a thick matrix smear, crushed chips, wide cavities from fiber/particle pull-out, stepped edges at the margin, and micro-crack networks. These features indicate high thrust and unstable breakthrough, enlarging the damaged region and increasing F_d, especially at the exit.

Figure 7.

SEM of WPC drilled hole (Trial 6).

Figure 8 shows the sectional view of WPC drilled hole taken at magnification of 3.46 kx, the protruded fiber while drilling the WPC panels with carbon steel aluminium finish drill, due to stability and cutting action of the drill bit during drilling may not proper due to this heat generated during process could not disspited properly in the WPC panels and this leads to degradation of wood fibres and it also weaken the bond between wood fiber and polymer matrix.⁴² Trial 24 (F_{d_entry} = 1.33, F_{d_exit} = 1.51) the texture shifts to finer debris, shallow pull-out pits, and a more continuous, sheared edge; the reduced smear and fewer debonded interfaces reflect lower effective thrust at higher speed, hence a smaller damaged ring and lower F_d. Practically, F_d increases with (i) pull-out pit density/size, (ii) exit-lip height and cusp step frequency, (iii) smear layer thickness and interfacial debonding length; and F_d decreases as the kerf wall becomes uniformly sheared with minimal pits/smear.

Figure 8.

SEM image of drilled WPC (Trial 24).

Modeling of drilling parameters using response surface methodology

To optimize the process parameters and provide the intended results, the RSM strategy combines statistical and mathematical methodologies.⁴³ The output response is predicted by optimizing the input variables through this procedure. Equation (2) provides a second-order polynomial RSM prediction model for predicting the parametric impact on the WPC panel, which consists of both linear, quadratic, and vector terms, because the correlation among the response (y) and variables is nonlinear. In this case, $β$ ₀ is the constant, $x_{i}$ is an input variable (SS, FR, DD) and $β_{i}$ , $β_{i i}$ , $β_{i j}$ represents the vectors that contain the linear, the quadratic, and the cross-product terms, respectively. The residual $ε$ measures experimental error at the observation

y = β_{0 +} \sum_{i = 1}^{k} β_{i} x_{i} + \sum_{i = 1}^{k} β_{i i} x_{i}^{2} + \sum_{i < j} β_{i j} x_{i} x_{j} + ε

(2)

The $F_{d}$ , represented by the second-order response, can be written with drilling variables viz., SS, FR, and DD. Equation (3) describes the connection between drilling parameters and the F_d.

F_{d} = β 0 + β_{1} (S S) + β_{2} (F R) + β_{3} (D D) + β_{4} (S S^{2}) + β_{5} (F R^{2}) + β_{6} (D D^{2}) + β_{7} (S S * F R) + β_{8} (S S * D D) + β_{9} (F R * D D)

(3)

The established quadratic model is more applicable for the two responses, F_{d_entry} and F_{d_exit}, in the drilling WPC panels, which are represented by regression equations given as coded terms (equation (4) and (5)). The application of coded equations enables the correlation between the F_d and process parameters.

F_{d_entry} = + 0.923704 - 0.000158 * S S - 0.001489 * F R + 0.088889 * D D + 4.22222 E - 07 * S S * F R + 8.33333 E - 07 * S S * D D + 0.000189 * F R * D D + 1.22222 E - 08 * S S^{2} + 4.24691 E - 06 * F R^{2} - 0.005278 * D D^{2}

(4)

F_{d_exit} = + 1.79481 - 0.000112 * S S - 0.001793 * F R - 0.059444 * D D + 2.33333 E - 07 * S S * F R - 1.25000 E - 06 * S S * D D + 0.000078 * F R * D D + 1.72222 E - 08 * S S^{2} + 7.80247 E - 06 * F R^{2} + 0.004306 * D D^{2}

(5)

Analysis of Variance

ANOVA is a statistical tool for studying the variation that exists between observed quantities. ANOVA is applied to determine the contribution of given factors or variables to the variance. To determine which effects are significant and to estimate the effects, it examines how independent factors affect a dependent variable.⁴⁴ Dependent variables are the output measures that are conceptualized to be affected by the independent variables. ANOVA techniques are used to find the most significant parameters of

F_{d}

³⁴. An ANOVA analysis was done for the quadratic model of F_{d_entry} and F_{d_exit} to evaluate the importance of the input variables and their interactions on the outputs. The ANOVA values for the F_d_{_entry} are provided in Table 5. With SS, FR, and DD identified as critical factors, the model is significant, as directed by the F-value for F_d_{_entry} of 18.25 and p < .05. None of the model terms make an impact, the most important variable is the FR, which is trailed by DD.

Table 5.

Analysis of F_{d_entry} and F_{d_exit} using ANOVA in drilling WPC.

	Sum of squares			Mean square		F-value		p-value		P (%)
Source	$F_{d_e n t r y}$	$F_{d_e x i t}$	df	$F_{d_e n t r y}$	$F_{d_e x i t}$	$F_{d_e n t r y}$	$F_{d_e x i t}$	$F_{d_e n t r y}$	$F_{d_e x i t}$	$F_{d_e n t r y}$	$F_{d_e x i t}$
Model	0.6053	0.3228	9	0.0673	0.0359	18.25	17.57	<0.0001	<0.0001			significant
N	0.0272	0.0061	1	0.0272	0.0061	7.39	2.96	0.0146	0.1033	4.49	1.88
F	0.4640	0.2713	1	0.4640	0.2713	125.93	132.88	<0.0001	<0.0001	76.65	84.04
d	0.0854	0.0249	1	0.0854	0.0249	23.18	12.21	0.0002	0.1974	14.10	7.71
Nf	0.0120	0.0037	1	0.0120	0.0037	3.27	1.80	0.0885	0.8503	1.98	1.14
Nd	0.0000	0.0001	1	0.0000	0.0001	0.0090	0.0367	0.9253	0.3836	0	0.030
Fd	0.0096	0.0016	1	0.0096	0.0016	2.61	0.7999	0.1243	0.3636	1.58	0.495
N ²	0.0009	0.0018	1	0.0009	0.0018	0.2432	0.8715	0.6282	0.0293	0.148	0.557
f ²	0.0034	0.0116	1	0.0034	0.0116	0.9293	5.66	0.3486	0.3636	0.561	0.359
d ²	0.0027	0.0018	1	0.0027	0.0018	0.7257	0.8715	0.4061	0.1974	0.446	0.557
Residual	0.0626	0.0347	17	0.0037	0.0020					10.34	10.74
Cor total	0.6680	0.3575	26
Std.Dev	0.0607	0.0452

R² for $F_{d_e n t r y}$ = 0.9062; R² for $F_{d_e x i t}$ = 0.9029.

Adjusted R² for $F_{d_e n t r y}$ = 0.856;adjusted R² for $F_{d_e x i t}$ = 0.851.

RMSE for $F_{d_e n t r y}$ = 0.060, RMSE for $F_{d_e x i t}$ = 0.045.

A decent model fit is indicated by the forecasted R² of 0.7830 and the adjusted R² of 0.8566, which are reasonably in accord. The signal-to-noise is evaluated by the appropriate precision value, and has an optimal ratio of 4. The F_{d_exit}model’s F-value, as shown in Table 5, is 17.57, suggesting that the model is noteworthy. FR, DD, and DD² are the important model terms in this model. In contrast, the model is considered unimportant if the p-value exceeds 0.1000. The adjusted and forecasted R² of 0.8515 and 0.7083 are reasonably in agreement because the difference is lower by 0.2. The signal-to-noise proportion is 12.969, indicating appropriate precision.

Figure 9 displays the analysis of the F_d_{_entry} model through a normal probability plot of residual, actual against the predicted plot, perturbation plot, and 3D responses. The normal probability of the obtained residual follows the straight line; no data processing is required during the initial stage.⁴⁵ In Figure 9(a), the computed residual demonstrates that all the data points connected to the F_d_{_entry} were plotted on the predicted straight line falling inside the range −2 and +2. The 45° line evenly divided the datasets in the graph between actual and expected response to identify whether the developed model can accurately predict.⁴⁶ Figure 9(b) shows that the actual $F_{d_e n t r y}$ and predicted $F_{d_e n t r y}$ data points are equally lined up on the 45°. This shows a good relationship in the response model. Figure 9(c) presents the joint influence of FR and SS on the F_{d_entry,} indicating that increasing the FR leads to higher F_{d_entry}, and vice versa, whereas SS does not exhibit significant variation. Figure 9(d) demonstrates the impact of DD and SS, where a major part is played by DD in increasing F_d_{_entry} the DD increases, F_{d_entry} also increases. SS again shows minimal influence. Figure 9(e) represents the combined influence of DD and FR, showing that F_d_{_entry} rises with the increase of both variables. The minimum F_{d_entry} was observed at maximum SS, minimum FR, and smallest DD.⁴⁷

Figure 9.

Diagnostics plot and 3D surface for F_{d_entry}.

The diagnostic plot and 3D responseF_{d_exit} is illustrated in Figure 10. The normal probability graph of residuals is distributed normally fitted around the straight line. In the observed versus predicted graph, the 45° line seems to split the data points evenly, demonstrating that the model developed could accurately predict. The 3D surface plots show that the F_{d_exit} increases with higher DD and FR and vice versa. The analysis shows that the minimum F_{d_exit} could be attained at maximum and minimum FR and DD.⁴⁸

Figure 10.

Diagnostics plot and 3D surface for F_{d_exit}.

The interaction between parameters is minimal if the effects between the parameter lines are parallel. If the lines are apart from each other, there will be an remarkable interaction.⁴⁹ Figure 11(a) shows that a higher SS of (3000 rpm), FR of (75 mm/min) shows the reduction of the F_{d_entry}, whereas at a higher FR (225 mm/min), the SS has little influence and the F_d remains high. Figure 11(b) shows that at a small diameter of 6 mm, the F_{d_entry} of the hole reduces at higher speed (3000 rpm), whereas at a larger diameter of 10 mm, SS has a limited effect, thereby increasing the F_d. Figure 11(c) shows that a higher FR results in an increase in F_{d_entry}, and the effect is magnified with a larger diameter. The F_{d_exit} exhibits similar interaction behaviour (Figure 11(d) and (e)), with increasing SS mostly reducing delamination at lower FRs and smaller DDs.

Figure 11.

Interaction plot for delamination in the entry and exit of the hole.

One of the multi-objective optimization methods applied for enhancing the drilling procedure is Desirability Function Analysis (DFA). It is capable of concurrently optimizing many output parameters.⁵⁰ It is used to investigate the optimal operating parameters that optimize different response values. The colored dot on each ramp represents the component value for the proposed solution’s response estimation.⁵¹ The optimal input parameters are shown in red, while the WPC drilling response is shown in blue. Minimizing the F_{d_entry} and F_d_{_exit} is the aim of this optimization strategy. Figure 12 shows the ramp plot with optimal SS (3000 rpm), FR (75 mm/min), and DD (6 mm) with predicted F_{d_entry} and F_d_{_exit} of 1.04454 and 1.42694. The desirability value obtained from the ramp plot is 0.963, closer to the optimal value of 1.

Figure 12.

Ramp plot of optimized condition.

Fuzzy Intelligent System

Fuzzy logic (FL) is an AI technique that can be used in the machining parameters modelling to forecast the response of the machining. Fuzzy systems (FS) are constructed on a fuzzy set theory that performs well by two theoretical contributions: (1) imprecise reasoning, (2) compositional rule. FL can perform well like human capability, which includes conversing, reasoning, making rational decisions, and it can perform various perform mental and physical duties deprived of any measurement and computation.⁵² Fuzzy system works commonly on four systems.

(i) Fuzzier process- It converts crisp (or) translating fuzzy sets into a language that makes sense with different membership functions (MFs). For each input variable, the MF will assign a value from 0 to 1.

(ii) Fuzzy systems rely on a collection of linguistic assertions called fuzzy rules to define the relationship between their output and input systems. The fuzzy rules are expressed in the IF-THEN rule

(iii) A desired approach may be achieved via the use of an inference engine’s approximation reasoning.

(iv) Lastly, a defuzzification is employed to get a clear result or a conclusion that is not fuzzy using an approach such finding the area’s centroid or maximum size⁵³

In this experiment, FL employs fuzzy rules to establish relationships between input and output variables, utilizing element viz, rule framer, fuzzifier, implication, and output processor. In this study, a fuzzy rule base with 27 IF-THEN rules is used, incorporating three inputs: SS, FR, and DD, and two outputs, Delamination factor ( $F_{d_e n t r y}$ ) and ( $F_{d_e x i t}$ ). Fuzzy logic enhances traditional statistical methods by converting complex numeric problems into fuzzy linguistic terms through continuous membership transitions. The membership function (MF) of fuzzy networks is leveraged to develop rules and make decisions based on existing rules. As the fuzzy inference system (FIS) evolves and more precise data and rules are utilized, the forecast model’s accuracy can improve. Generally, it is decided by the type of interference system, either Sugeno or Mamdani. In Mandani interference system, the output MF is a fuzzy set; in the case of the Sugeno type, the output membership function is either linear or stable. Mamdani inference is used in this experimental work to convert the experimental output into a fuzzy set. Through the formulation of linguistic variables, the kinds of MFs (e.g., Trapezoidal, Triangular, and Bell or Gaussian MFs), and the fuzzifier, it is possible to convert data from crisp or fuzzy sets into appropriate linguistic values. Figure 13 represents the functional element of FIS to convert input into a suitable output using a rule base. The MFs and FIS used in this study, which integrates three inputs; SS, FR, and DD and outputs F_{d_entry} and F_{d_exit}.

Figure 13.

(a) FIS, (b) Triangular MF with L, M, H for $F_{d}$ Input SS; (c) Triangular MF with L, M, H for $F_{d}$ Input FR; (d) Triangular MF with L, M, H for $F_{d}$ Input DD; (e) Triangular MFs for $F_{d}$

The study employs triangular MFs due to their simplicity, with each input having three MFs: high (H), medium (M), and low (L) as in Figure 13(b)–(d) and the outputs modeled with nine MFs: Ultra High (UH), Very High (VH), High (H), Slightly High (SH), Medium (M), Slightly Low (SL), Low (L), Very Low (VL), and Ultra Low (UL) as shown in Figure 13(e). The rule editor, guided y expert system knowledge, utilizes IF-THEN rules to forecast F_{d_entry} and F_{d_exit}. Based on specific input values.⁵⁴

Figures 14 and 15 illustrate that the F_{d_entry} and F_{d_exit} are reduced when drilling with carbon steel and aluminum finish drills at 3000 rpm (SS), 75 mm/min (FR), and 6 mm (DD). In the 3D plot, blue represents the minimum F_d, green indicates uniform F_d, and yellow signifies the highest delamination facto. Figure 16 shows the output response of the (F_{d_entry}) and (F_{d_exit}) with comparison of experimental, RSM, and fuzzy projected values. Table 6 shows the percentage differences between the experimental, RSM, and fuzzy projected delamination factors at the hole’s entry and exit. The FL model achieved an MAE of 0.01852 and RMSE of 0.02143 for F_{d_entry}, and 0.01074 and 0.01291 for F_{d_exit}, while the RSM model recorded an MAE of 0.03889 and RMSE of 0.04811 for F_{d_entry}, and 0.02926 and 0.03564 for F_{d_exit}. The overall error for the fuzzy model is 0.01463 with an RMSE of 0.017691 across both entry and exit points, whereas the RSM model has an MAE of 0.034074 and RMSE of 0.042339. These findings demonstrate that the fuzzy model has lower errors than the RSM model.

Figure 14.

3D plot of F_{d_entry}.

Figure 15.

3D plot of F_{d_exit}.

Figure 16.

Comparison of experimental and predicted values of F_{d_entry} and F_{d_exit}.

Table 6.

Comparison of experimental outputs with RSM and fuzzy logic.

Experimental		RSM		Fuzzy logic		% Deviation experimental vs RSM		% Deviation experimental vs FL
F_{d_entry}	F_{d_exit}	F_{d_entry}	F_{d_exit}	F_{d_entry}	F_{d_exit}	F_{d_entry} %	F_{d_exit} %	F_{d_entry} %	F_{d_exit} %
1.2	1.42	1.16	1.45	1.23	1.43	3.3	−2.11	−2.50	−0.704
1.26	1.45	1.22	1.46	1.23	1.43	3.17	−0.69	2.381	1.379
1.21	1.54	1.23	1.51	1.23	1.52	−1.65	1.95	−1.653	1.299
1.19	1.52	1.23	1.5	1.16	1.52	−3.36	1.32	2.521	0.000
1.22	1.54	1.32	1.52	1.23	1.52	−8.20	1.30	−0.820	1.299
1.32	1.64	1.37	1.58	1.3	1.66	−3.79	3.66	1.515	−1.220
1.36	1.66	1.36	1.64	1.37	1.66	0.00	1.20	−0.735	0.000
1.58	1.67	1.47	1.67	1.56	1.66	6.96	0.00	1.266	0.599
1.56	1.65	1.55	1.74	1.56	1.66	0.64	−5.45	0.000	−0.606
1.05	1.41	1.07	1.4	1.04	1.4	−1.90	0.71	0.952	0.70
1.12	1.39	1.13	1.41	1.09	1.4	−0.89	−1.44	2.679	−0.719
1.21	1.41	1.15	1.45	1.23	1.4	4.96	−2.84	−1.653	0.709
1.16	1.46	1.18	1.47	1.16	1.48	−1.72	−0.68	0.000	−1.370
1.26	1.45	1.27	1.49	1.23	1.43	−0.79	−2.76	2.381	1.379
1.41	1.58	1.32	1.54	1.44	1.57	6.38	2.53	−2.128	0.633
1.35	1.63	1.34	1.62	1.37	1.62	0.74	0.61	−1.481	0.613
1.4	1.69	1.45	1.66	1.37	1.71	−3.57	1.78	2.143	−1.183
1.48	1.75	1.53	1.72	1.51	1.74	−3.38	1.71	−2.027	0.571
1.03	1.42	1.01	1.39	1.04	1.43	1.94	2.11	−0.971	−0.704
1.02	1.43	1.07	1.39	1.04	1.43	−4.90	2.80	−1.961	0.000
1.04	1.43	1.1	1.43	1.04	1.43	−5.77	0.00	0.000	0.000
1.17	1.48	1.15	1.47	1.16	1.48	1.71	0.68	0.855	0.000
1.31	1.43	1.24	1.49	1.3	1.43	5.34%	−4.20	0.763	0.000
1.33	1.51	1.29	1.54	1.3	1.52	3.01%	−1.99	2.256	−0.662
1.32	1.59	1.34	1.64	1.3	1.57	−1.52%	−3.14	1.515	1.258
1.47	1.72	1.46	1.67	1.44	1.71	0.68%	2.91	2.041	0.581
1.51	1.75	1.54	1.74	1.51	1.74	−1.99%	0.57	0.000	0.571

Particle swarm optimization algorithm

The current optimization method seeks to minimize the F_{d_entry} and F_{d_exit} by optimizing the multi-objective function of the drilling parameters.^55,56 Kennedy and Elberhart developed the particle swarm optimization (PSO) algorithm in 1995 as a metaheuristic algorithm that draws motivation from the societal performance of fish schools and/or flocks of birds.⁵⁶ It can be applied to optimization issues that are discrete or continuous. A particle is the solution to each of a bird’s search spaces in a multidimensional space. Particles are thought to possess two properties: position and velocity. Every iteration updates the position of every particle in the swarm according to its velocity, which is impacted by both its global best (g_best) and personal best (p_best) solutions. The following formula updates the particle positions and velocities.

V_{j + 1} = {wV}_{j} + c_{1} r_{1} (P_{bestj} - X_{j}) + c_{2} r_{2} (G_{best} - X_{j})

(6)

X_{j + 1} = X_{j} + V_{j + 1}

(7)

V_j+1 denotes newer velocities of the particle based on the preceding position V_j X_j+1 denotes the particle built on the earlier position X_j, and j denotes iterations and total particles. P_{best j} is the ideal fitness value at the j^th particle. Gbest signifies the best fitness value for all of the particles. c₁, c₂ denote the learning factor, r₁, r₂ denote numbers 1 and 0, and w denotes the inertia weight.⁵⁷

Hippopotamus Optimization Algorithm (HOA)

The hippopotamus optimization algorithm (HOA)enhances the ability to reach global convergence by drawing inspiration from hippopotamus behaviour, including its location update, defense behaviour against predators, and avoidance of predators. As seen in equation (8), HOA is a population-centered technique where a search agent stands in for a hippopotamus. It creates a vector by updating the positions of each hippopotamus in the design region that corresponds to the decision variable values.⁵⁸

X_{i} : x_{ij} = L_{j} + r . (U_{j} - L_{j}), i \in N, j \in M

(8)

where L_j and U_j specify the j^th decision variable lower and upper bounds, and X_i indicates the location of the i^th candidate solution. A random number within 0 and 1 is denoted by r. M is the total number of decision factors, and N is the hippopotamus population size.

Position updating

It updates the hippopotamus’ position as per the objective function with the dominant male, and it must attract a female or a male to minimize the function by improving the position of the hippopotamus as described in equation (9).

X_{i}^{Mhippo} = X_{ij} + y_{1} (D_{hippo} - I_{1} X_{ij})

(9)

{\begin{cases} \begin{array}{l} I_{2} X \vec{r_{1}} (Q_{1}) \\ 2 \times \vec{r_{2}} - 1 \\ \vec{r_{3}} \end{array} \\ I_{1} + \vec{r_{4}} + (Q_{2}) \\ r_{5} \end{cases}

(10)

Where X_i^Mhipporepresents the male hippopotamus position, D_hippo denotes the dominant hippopotamus position,

\vec{r_{1 \dots 4}}

is a random vector among 0 and 1 (equation (11)), I₁ and I₂ a integers between 1 and 2 (equation (9) and equation (10)), Q₁ and Q₂ are random numbers within 1 and 0, y₁ and y₂are random numbers between 0 and 1.

Defense against predators

In situations where a hippopotamus is threatened by a predator, it strikes back. This behaviour is indicative of the pattern of behaviour for a person’s move. Equation (11) depicts the hippo’s defense distance from the predator, while equation (12) shows how the hippo returns to the herd after escaping from the hunter.

\vec{D} = | {P r e d a t o r}_{j} - X_{i j} |

(11)

X_{i}^{R h i p p o} = {\begin{cases} \vec{R L} + {P r e d a t o r}_{j} + (\frac{ϑ}{c - d x \cos (2 π g)}) (\frac{1}{\vec{D}}) F_{{P r e d a t o r}_{j}} < F_{i} \\ \vec{R L} + {P r e d a t o r}_{j} + (\frac{ϑ}{c - d x \cos (2 π g)}) (\frac{1}{2 x \vec{D} + \vec{r_{9}}}) F_{{P r e d a t o r}_{j}} \geq F_{I} \end{cases}

(12)

Where

X_{i}^{R h i p p o}

lies the hippos with relation to its predator C and d are also random integers between 1 and 3, whereas

ϑ

is a number between 2 and 4. The value of g may be any integer between −1 and 1, where

\vec{r_{9}}

it is a random vector.

Evasion from predators

When a hippo faces a threat from predators may retreat to a safe position. This augments the local hunt exploration in the HOA model as described in equation (13) and equation (14).

{L L}_{j}^{l o c a l} = \frac{{L L}_{J}}{i t e r}, {U L}_{j}^{l o c a l} = \frac{{U L}_{J}}{i t e r} i t e r = 1, 2.3 \dots \dots \dots ., {i n t e r}_{\max}

(13)

X_{I}^{H i p p o} = X_{ij} + r_{10} . ({L L}_{j}^{l o c a l} + α ({U L}_{j}^{l o c a l} - {L L}_{j}^{l o c a l})

(14)

Where

X_{I}^{H i p p o}

is the position of a hippo, which searches for the closest safe place,

i t e r

denotes current iteration,

α

represent the random vector or variable, r₁₀denotes a random number within 0 and 1.⁵⁹

The PSO and HOA are executed by considering the parameters listed in Table 7 and utilizing the MATLAB R2021 code to identify the optimal WPC drilling variables aimed at minimizing F_{d_entry} and F_{d_exit}. A second-order regression model is employed to develop the objective function for optimization using PSO and HOA.⁴³ These restrictions apply to the range of parameters: 1000 < SS <3000; 75 < FR <225; and 6 < DD <10.

Table 7.

PSO and HO algorithm-specific parameters.

Parameters	Values
PSO specific parameters⁵⁷
Size of swarm	50
Variables	3
Creation function	PSW creation uniform
Min. Neighbour fraction	0.25
Self adjustment	1.49
No.of generations	100
HOA-specific parameters
Population size	50
Maximum iteration	100
Water update constant	0.7
Hunger coefficient	0.5

Figure 17 shows the combined objective function obtained during minimization of the F_d, which comes to the required objective at the 3^rd and 8^th iterations. The results show that the PSO and HOA algorithms are giving almost the same results. The objective function obtained the required minimal value at the particular iteration in the present investigation, and convergence is reached within the 10^th iteration. This shows that the PSO and HOA algorithms are suitable for optimizing the F_d in WPC drilling. The outcomes show that the PSO and HOA are giving similar trends after a particular iteration.

Figure 17.

Combined objective function during minimizing F_d.

The optimized solution obtained during minimizing the F_{d_entry} and F_{d_exit} is analyzed further and presented in Figures 18–20. Figure 18 shows the optimum SS obtained for minimizing the F_d of WPC composite panels by using PSO and HOA algorithms. The results clearly show that the optimal SS obtained is 3000 rpm. The convergence started from 0 and was obtained within the 10^th iteration. This shows that high SS is preferred for minimizing the F_d. The reason is that at elevated SS, the cutting takes place at a high transverse rate, improving the cutting action and minimizing the F_{d_entry} and F_{d_exit}.

Figure 18.

Optimum SS concerning variation of SS.

Figure 19.

OptimumFR concerning variation of FR.

Figure 20.

Optimum DD concerning variation of DD.

Figure 19 shows the optimum FR obtained for minimizing the F_d of WPC composite panels by using PSO and the HOA algorithms. The results indicate that the optimal results start from 155 mm/min and reach the optimal value of 75 mm/min for the PSO algorithm, whereas the optimal solution for the HOA is around 75 mm/min after the 10^th iteration. The convergence takes place, and both optimization techniques reach 75 mm/min consistently up to 100 iterations, and the iterations are stopped at the 100^th iteration. Figure 20 shows the optimal results obtained for DD in the drilling of WPC composite panels for studying F_d. The results from the PSO algorithm indicate that there is a variation in trends from approximately 6.3 mm to 6 mm. After it reaches 6 mm, there is a stabilization that occurs, and the optimal value obtained is 6 mm, whereas by using the HOA, the results fluctuate from 6.9 mm to 6 mm within the 10^th iteration. The results of the PSO and HOA algorithm shows similar trends after 15^th iterations. From the graph, the optimal value of DD obtained is 6 mm for drilling of WPC composite panel for minimizing the F_d.

PSO is highly sensitive to parameter tuning, where swarm size, inertia weight, and acceleration coefficients must be carefully balanced; otherwise, the algorithm risks stagnation, divergence, or premature convergence. In contrast, HOA is less parameter-dependent, with its main controls (population size, water update constant, and hunger coefficient) working effectively over broad ranges, making it more robust and stable across different problem types. Thus, while PSO can perform very well when finely tuned, HOA generally offers more consistent results with less sensitivity to parameter adjustments. Both PSO and HOA provided the same results (combined objective function value, SS, FR, and DD). But the convergence of HOA is quicker than the PSO and reduces the likelihood of entrapment in local optima. As a result, HOA generally achieves higher solution accuracy and robustness across multiple runs with less sensitivity to parameter tuning. Table 8 shows the comparative performance of PSO and HOA in terms of performance and highlights the key takeaways from the optimization algorithms.

Table 8.

Comparative performance of PSO and HOA.

Aspect	PSO (size = 50, 100 iterations)	HOA (pop = 50, 100 iterations)	Take-away
Best objective reached	Same as HOA	Same as PSO	No superiority in final fitness
Convergence speed (to ∼1% of final)	Fast; reaches plateau by ∼ 8–10 iters; noticeable early zig-zags	Fast; plateau by ∼ 9–12 iters; trajectories are smoother	Speed is comparable; HOA gives smoother paths
Stability of decision variables after convergence (SS, FR, DD)	Minor oscillations before settling (see FR curve)	Practically flat after settling	HOA is slightly more stable near the optimum
Sensitivity to hyperparameters	Higher (w, c1, c2, swarm size) typically need tuning	Moderate (water-update, hunger coeff.)	HOA can be a bit less tuning-sensitive in practice
Exploration vs. exploitation	Strong early exploration; occasional overshoot	More tempered updates; fewer overshoots	HOA may reduce chattering near the optimum
Feasibility retention under penalties	Similarly, brief boundary touches are visible	Similar or marginally better post-settling	Tie, slight edge to HOA for steadiness
Computational cost per run	Similar	Similar	No runtime advantage at this scale
When to prefer	Baseline: Well-known, easy to reproduce	When smoother control trajectories or slightly lower tuning sensitivity matter	Methodological diversity + stability argument for HOA

The desirability analysis using RSM, PSO, and HOA algorithms identified the optimal drilling parameters, which are highlighted, indicating the best parameters to enhance drilling performance, as shown in Table 9. The predictive models suggest that a combination of maximum SS, minimum FR, and minimum DD minimizes the F_{d_entry} and F_{d_exit}.⁶⁰ Confirmation experiments were conducted using the optimized variables to validate the models. Table 9 presents the authentication experiments carried out at the ideal level (exp. nos 1 & 2) and under three different conditions. The percentage error between the experimental and forecasted values is displayed in Figure 21. The drilled holes under optimal conditions exhibit reduced delamination and fewer uncut fibers, as shown in Figure 22.

Table 9.

Verification test results.

SS (rpm)	FR (mm/min)	DD (mm)	$F_{d_entry}$					$F_{d_exit}$
SS (rpm)	FR (mm/min)	DD (mm)	Exp	RM	$ε$ (%)	FL	$ε$ (%)	Exp	RM	$ε$ (%)	FL	$ε$ (%)
Optimal results
3000	75	6	1.03	1.01	1.94	1.04	−0.97	1.42	1.39	2.11	1.43	−0.70
3000	75	6	1.02	1.01	0.98	1.04	−1.96	‘1.41	1.39	1.41	1.43	−1.41
Selected verification test
1000	75	6	1.2	1.16	3.33	1.23	−2.5	1.42	1.45	−2.11	1.43	−0.70
1000	225	8	1.58	1.47	‘6.9	1.56	1.26	1.67	1.67	0	1.66	0.59
2000	75	10	1.21	1.15	4.95	1.23	−1.65	1.39	1.45	−4.31	1.4	−0.71

Figure 21.

Verification test results (a) Hole entry; (b) Hole exit.

Figure 22.

Optimal drilling parameters images of holes with less delamination and small uncut fibre at hole (a) Entry (b) Exit.

The drilling conditions of 3000 rpm, 75 mm/min feed, and a 6 mm aluminium finish carbon-steel drill are feasible for wood–plastic composite, giving clean holes with low thrust and minimal delamination. However, with carbon steel’s poor hot-hardness, tool life is moderate, especially if the composite contains fillers. But with an aluminium finish, the tool life may be extended. For industrial practice, slightly higher feeds and upgraded tools such as HSS or carbide brad-point drills are recommended to achieve better productivity and longer tool life without compromising hole quality.

Conclusion

This investigation on drilling WPC panels focuses on the drilled hole, F_{d_entry}, and F_{d_exit} for different DD, FR, and SS. Holes are made by using a carbon drill bit with an aluminium finish, and the subsequent conclusions were drawn.

The F_d increases with higher FR, and simultaneously reduces with a lower FR, particularly at 75 mm/min. Variation in F_dwas obtained with a rising SS, but no clear trend emerged. The quadratic model developed using RSM for F_{d_entry} and F_{d_exit} predicts the output response as the fuzzy model does, and the data are closer to each other, showing the supremacy of the fuzzy model. The optimal cutting parameters to reduce the F_din WPC are 3000 rpm (SS),75 mm/min (FR), and 6 mm (DD). ANOVA analysis confirmed that all the factors are significant on the F_{d_entry} and F_{d_exit}, with accuracy and precision. Desirability analysis validates with a value of 0.963, closer to the perfect value of 1, indicating that all the input parameters have a substantial influence on the F_{d_entry} and F_{d_exit}. The predicted output responses F_{d_entry} and F_{d_exit} are 1.04454 and 1.42694. The multi-objective optimization by PSO and HOA has indicated the optimal SS of 3000 rpm, FR of 75 mm/min, and DD of 6 mm, with the lowest objective function of 2.666, which are the best parameters for reducing the delamination factor.

This study employed the RSM, PSO, and HOA algorithms to analyze how SS, FR, and DD influence delamination at the entry and exit points during drilling of WPC. Combining these methods provided valuable insights into process behaviour and parameter choices. However, there are certain limitations: (i) only a single drill tool geometry is considered; (ii) the focus is limited to delamination at the hole’s entrance and exit, excluding factors like thrust force, torque, surface roughness, and hole accuracy.

Future research focus include (i) investigate different tool geometries and materials, (ii) incorporate additional quality control techniques, and examine various WPC types. (iii) the use of hybrid AI models and advanced sensor-based monitoring enhance the sustainable drilling and enable real-time optimization.

Footnotes

Author Contribution

Sundarapandiyan: Conceptualization, Methodology, Investigation, Data curation, Palanikumar, Supervision, and writing original draft; and Senthilkumar methodology, Data interpretation, Investigation, Formal analysis and writing original draft; and Latha Data interpretation, and Resources; Writing and editing original draft, and Visualization.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Ethical considerations

The authors declare that this work is original and is not under consideration elsewhere.

Consent for publication

The authors declare our consent for publication upon acceptance.

ORCID iDs

Sundarapandiyan Murugesan

Palanikumar Kayaroganam

Senthilkumar Natarajan

Latha Balasubramanian

Data Availability Statement

All the data’s related to this study is available within this manuscript.*

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Analysis and optimization of the delamination factor in the drilling of wood-thermo plastic composites: An approach based on artificial intelligence

Abstract

Keywords

Introduction

Material and methods

Delamination Measurement

Results and discussion

Modeling of drilling parameters using response surface methodology

Analysis of Variance

Fuzzy Intelligent System

Particle swarm optimization algorithm

Hippopotamus Optimization Algorithm (HOA)

Position updating

Defense against predators

Evasion from predators

Conclusion

Footnotes

Author Contribution

Declaration of conflicting interests

Funding

Ethical considerations

Consent for publication

ORCID iDs

Data Availability Statement

References