Post curing optimization for tensile strength of hybrid ramie-carbon fiber reinforced polymer

Abstract

The optimization of post-curing processes is crucial for enhancing the performance of epoxy-based fiber-reinforced polymers (FRP) by ensuring adequate cross-linking. This study focuses on optimizing the post-curing parameters for hybrid Ramie and Carbon fiber composites with the primary objective of improving tensile strength. Variations in post-curing temperature, post-curing time, and the number of synthetic fiber layers were systematically investigated across three levels using Taguchi design of experiments. The ultimate tensile stress was employed as the response parameter. Results indicate that post-curing temperature exerts a greater influence on tensile strength compared to post-curing time. A failure pattern of natural fiber followed by synthetic fibers was seen to happen progressively. A precise multivariable regression model was developed to predict the response for different combinations of post-curing parameters. Furthermore, employing particle swarm optimization revealed an optimal post-curing time of 12 h and an optimal temperature of 60°C. These findings contribute to the optimization of post-curing processes in hybrid fiber composites, thereby enhancing their mechanical properties.

Introduction

The use of fiber-reinforced polymers (FRP) in structural applications has majorly been in the domain of structural retrofitting as they provide excellent tensile strength and confinement in case of columns. FRPs pose several benefits like light weight, high strength to weight ratio, ease of application, and reduction in retrofitting time. ¹ Synthetic fibers like carbon and glass fibers are often used in FRPs employed for civil engineering works. ² They are extremely strong, rigid, and resistant to chemical deterioration. ³ Additionally, synthetic fibers offer better thermal stability, ⁴ better toughness, and fatigue resistance as compared to the cellulose-based fibers counterparts. Although having good mechanical performance, synthetic fibers are not a sustainable approach towards using it on large scale. In applications where sustainability, weight reduction and cost effectiveness are of priority, natural fibers form a good alternative for the synthetic fibers. Natural fibers also reduce the wear on equipment processing them. ⁴

The mechanical performance of FRPs depends on the performance of fibers as well as the polymers. The epoxy serves as a host and matrix to the fibers which are the load carrying members in the composite. The function of epoxy is to protect the fiber and evenly disperse the load to the fibers. Epoxy polymers are widely used for manufacturing composites as epoxies provide a good resistance to most of chemical attacks, ⁵ and are also structurally good at transferring the loads to the fibers. Some epoxies gain strength in different stages. After 24 h of curing at room temperature, some thermosetting epoxies require a post curing process. Post-curing of epoxies is a process that involves subjecting epoxy materials to elevated temperatures after the initial curing process. This additional heat treatment enhances the mechanical, thermal, and chemical properties of the epoxy, ensuring it reaches its full potential. The specifics of post-curing, including temperature and duration, can vary based on the epoxy formulation and intended application. Post curing process improves strength, elevates the glass transition temperature, and reduces the residual stresses . ⁶ The post curing process needs to be optimized for achieving the best performance from the epoxy as under-curing may not achieve the complete cross linking of the polymers resulting in lower mechanical performance ⁷ and over curing may charr the composite disfiguring its aesthetic properties as well as degrading the mechanical performance. By under curing or over curing the polymerisation does not take place effectively, thus offering a lower mechanical strength of the composite. The post curing schedule for a certain epoxy system are generally provided by the supplier or manufacturer of the epoxy system. This schedule is however optimised for certain type of a synthetic fiber and may not be applicable to the fiber that we intend to work with, hence there is a need for determining the optimum post cure schedule for other fibers which are intended to be used in the structural application. Diego et al. ⁸ studied the post curing process of di glycidyl ether bisphenol acetate (DGEBA) and tri ethylene tetra amine (TETA) hardener and found that epoxy cured at moderate temperatures around 80^o C to 90^oC, which reduced the gel formation time, and a strong polymerisation was seen in the resin. Khalifa et al. ⁹ fabricated flax fiber composites and studied the effect of post curing temperature and time and the density of cellulose on the mechanical properties of the composite, it was ascertained that post curing is essential in increasing the cross linking and forming a strong polymer network. Seretis et al. ¹⁰ varied the post curing temperature and post curing time and developed a regression model for graphene nano platelet reinforced glass epoxy composite keeping the tensile strength and strain as response factor. This regression model was used to find the optimum temperature and time for post curing. Shubham et al. ¹¹ studied the effect of post curing parameters of temperature and time on the inter laminar shear strength, aesthetics of the laminates was also given consideration and found that a post curing temperature of 140^oC and post curing time of 6 h give a fairly good inter-laminar shear strength while retaining aesthetics. Andrew et al. ¹² studied the effect of post cure temperature on bending response of repaired glass epoxy frp, it was seen that the post curing temperature significantly affected the response of the laminate and a optimum temperature of 50° determined. Sivasankaraiah et al. ¹³ investigated the effect of post curing temperature ranging from 40^oC to 220^oC for a fixed period of 6 h on the flexural performance of glass frp, the temperature played a significant role and was seen to increase the flexural strength from 40^oC to 140^oC and degrade from 140 to 220^oC. Fernando et al. ¹⁴ developed a time-temperature-transformation (TTT) cure diagram which were crucial in determining the unreacted (48^oC) and reacted system temperature (70^oC).

Two important factors in post curing process are the post curing temperature and post curing time. The natural fibers depending on the lignin content are seen to mechanically degrade at higher temperatures, ¹⁵ hence the extent of temperature for post curing as suggested by the manufacturer of epoxy system may not be suitable for the natural fibers. It is also seen that higher the exposure time of natural fibers to high temperature more is the mechanical degradation. ¹⁶ The study initially focuses on the post curing of Ramie-Carbon hybrid composite at various combinations of temperatures and post curing time to achieve a maximum tensile strength and a trend is identified. Further a bio inspired based Particle swarm optimization is performed to identify the optimum in the range used for experimental purpose.

Methodology

The post curing optimization was performed by varying the post curing temperature (T_c) and the post curing time (t_c). Also, the effect of number of synthetic layers in the composite on the post curing parameters was taken into consideration by varying the number of synthetic layers. The Ramie fiber fabric layers were sandwiched between varying number of layers of Carbon fiber fabric, keeping the number of Ramie fabric layers constant throughout. The variation of parameters is shown in Table 1.

Table 1.

Temperature-Time schematic.

Post curing temperature (Tc)(oC)	Post curing time (t) (h)	Number of carbon layers (N)
60	3	0 (no synthetic layer) – C0
90	6	2 (1 top +1 bottom) – C2
120	9	4 (2 top +2 bottom) – C4
150	12	6 (3top +3 bottom) – C6

Material selection

A bi-directional Carbon fiber fabric of twill weave pattern was used as synthetic layer part in the hybrid composite. The Ramie fiber was sourced in the woven uni-directional fabric format. The fabric was woven with fibers, pre-treated with sodium hydroxide during the retting process for separating the individual fibers. Table 2 shows the determined mechanical properties of the Ramie and Carbon fabric properties.

Table 2.

Mechanical properties of Ramie and Carbon fabric.

Parameters	Ramie fiber fabric	Carbon fiber fabric
Fiber density (kg/m-3)	1490 ± 40 17	1750 – 1930 18
Fiber diameter (m)	0.8*10−6	5*10−6
Thickness (m)	0.73*10−3	0.3*10−3
Weaving pattern	Unidirectional	Bi-directional (plane weave)
Areal density (kg/sqm)	316*10−3	200*10−3
Warps (picks per meter)	10*102	600 tows
Wefts (ends per meter)	10*102	600 tows

The epoxy used was ether based DGEBA (Di glycidyl ether bisphenol acetate). Epoxy resins are typically stronger than the vinyl esters or polyester resins, hence a DGEBA epoxy was a better choice for the fabrication of composites. Table 3 shows the properties of the epoxy resin used.

Table 3.

Epoxy resin properties. ¹⁹

Parameter	Resin	Hardener
Commercial name	Lapox L12	K6
Density (kg/m-3)	1200	1100
Mixing ratio	100 parts by weight	10 parts by weight
Tensile strength of neatly cured system	55 – 70 MPa
Elongation at break of neatly cured system	1.5 – 3 %
Bulk density of neatly cured system (kg/m-3)	1200

Taguchi design of experiments

The Taguchi design of experiments is a powerful statistical technique that aims to optimize the performance of a process or system while minimizing variability and improving robustness. It offers several salient features that make it a valuable tool in engineering and industrial applications. One key feature of the Taguchi design is its use of orthogonal arrays to efficiently plan and conduct experiments. ²⁰ Orthogonal arrays ensure that all factor combinations are evenly distributed, reducing the experiment trials required to determine the optimised settings. The L-orthogonal arrays, such as L9 or L16, are commonly used in Taguchi designs. ²⁰ Signal to noise ratio (SNR) is another important aspect of the Taguchi method, it is used to calculate the quality of the output and its sensitivity to the noise factors. The SNR allows for the quantification of the effects of different factors on the response variable and helps identify the most significant factors for optimization. ²¹ There are different types of SNR measures, such as the smaller-the-better, the larger-the-better, and the nominal-the-best. ²² In this study the signal to noise ratio for tensile strength was kept as the-larger-the-better, as we expect the composites to have best performance in the tensile behaviour.

The Taguchi method also emphasizes the importance of conducting experiments in a controlled and systematic manner. It involves breaking down the factors into controllable factors, uncontrollable factors (noise factors), and interactions between factors. ²³ This approach enables the identification of the optimal factor settings giving a robust performance even in the presence of variations in noise factors. ²⁴ It employs analysis of variance (ANOVA) to determine the contribution of each factor and their interactions to the variability in the response variable. This allows for the identification of the most influential factors and the estimation of their optimal levels. ²⁴ The features of Taguchi design of experiments contribute to its effectiveness in optimizing processes and systems while considering the effects of noise factors. Table 4 shows the nomenclature followed for different variations, the prefix number denotes the post curing temperature, followed by post curing time duration, the suffix number after the alphabet ‘C’ denotes the number of synthetic layers used in the composite, example 60-3-C-4 denotes a hybrid laminate having four layers of carbon post cured at 60^oC for 3 h. The minimum post curing temperature as specified by the epoxy manufacturer is 80^oC for 1 h. Hence, the lower range of post curing temperature for this experiment was kept sufficiently close (60^oC) but not exact to 80^oC as this was specified for glass fiber roving, thus, it was intended to check from a lower temperature for natural fibers. ²⁵ performed thermal analysis characterization of Ramie fiber and found that it is thermally stable till 200°, as the post curing time was kept till 12 h, it was finalised to keep a margin of safety and thus 150^oC was kept as upper range.

Table 4.

Laminate nomenclature.

Sample code	Temperature (celsius)	Time (h)	Synthetic layers
60-3-C-0	60	3	0
60-6-C-2	60	6	2
60-9-C-4	60	9	4
60-12-C-6	60	12	6
90-3-C-2	90	3	2
90-6-C-0	90	6	0
90-9-C-6	90	9	6
90-12-C-4	90	12	4
120-3-C-4	120	3	4
120-6-C-6	120	6	6
120-9-C-0	120	9	0
120-12-C-2	120	12	2
150-3-C-6	150	3	6
150-6-C-4	150	6	4
150-9-C-2	150	9	2
150-12-C-0	150	12	0

Composite fabrication

The composites were prepared using a hand layup method, compacted using a hand roller and pressed under vice grip to consolidate the layers. A 5-layer Ramie fabric laminate is reinforced with four configurations of carbon layers (C0, C2, C4 and C6) viz. 0 layer (no carbon fabric used), 2, four and six layers respectively as described in Table 1. Figure 1(a)–(d) shows the variations of laminates that were fabricated. The laminate is thus composed of two cores, inner core consisting of natural fibers and outer core consisting of synthetic fibers.

Figure 1.

(a) 0 Carbon layer configuration (b) 2 Carbon layer configuration (c) 4 Carbon layer configuration (d) 6 Carbon layer configuration (e) Schematic of composite fabrication.

The fabric was first cut to a dimension of 200 x 270 mm, in the form of a layer sheet. A known weight of epoxy resin was prepared according to the ratio specified by the epoxy datasheet. A base coat was laid on the Teflon sheet resting on a firm base. The base coat is required to evenly coat the fabric from the bottom side. The carbon and natural fibers were sequentially layered, and hand compacted by the roller. The remaining weight of epoxy was noted. The composite was then subjected to a compacting pressure by vice grips as shown in Figure 2(b) and left for air curing for 7 days to complete the first stage of curing. The transition to post curing stage was accomplished by subjecting the epoxy to different post curing temperatures and post curing durations.

Figure 2.

Laminate fabrication (a) Laminate geometry (b) Consolidation of laminates (c) Marking for machining (d) CNC machining process (e) Testing of natural fiber laminates (C-0 configuration) (f) Testing of hybrid laminate.

Machining

The composite was wrapped in a masking-tape as shown in Figure 2(c), so as to prevent chipping of the edges while machining with a CNC machine. The laminate was machined to the required dimensions as specified by ASTM D 3039. The test coupon dimensions and the preparation for machining are as shown in the Figure 2(a).

After the machining process, machined laminates were numbered sequentially and kept in the hot air oven for the duration as specified in Table 6. The weight of each machined laminate was taken precisely up to 0.01 g before testing. The thickness of the laminate was measured to a precision of 0.01 mm using a digital thickness gauge. Table 5 shows the calculation process used for determining the fiber volume fraction of laminates.

Table 5.

Fiber volume fraction calculation.

Fiber volume fraction (f)	$=\frac{V_f}{Total\hspace{0.17em}Laminate\hspace{0.17em}Volume}$
Where V f = fiber volume (m3)	$V_f=Total\hspace{0.17em}Laminate\hspace{0.17em}Volume-V_r$
Where V r = volume of resin (m3)	$V_r=\frac{w_r}{Epoxy\hspace{0.17em}density=1200\hspace{0.17em}kg/m^3}$
Where w r = weight of resin (kg)	$w-\left(areal\hspace{0.17em}weight\hspace{0.17em}of\hspace{0.17em}fabric*area\hspace{0.17em}of\hspace{0.17em}fabric\right)$
Where w = Total weight of laminate (kg)

Table 6 shows the calculated parameters for each laminate.

Table 6.

Laminate properties.

Sample code	Avg. Thickness. X10−3 (m)	Avg. Wt.x10−3 (kg)	Wt. of resin x10−3 (kg)	Laminate vol. X10−6 (m3)	Resin vol. X10−6 (m3)	Fiber vol. X10−6 (m3)	Fiber volume fraction
60-3-C-0	3.045	15.055	9.130	11.419	7.608	3.810	0.329
60-6-C-2	3.495	14.275	8.350	13.106	6.958	6.148	0.469
60-9-C-4	3.255	13.415	7.490	12.206	6.242	5.965	0.489
60-12-C-6	3	12.965	7.040	11.250	5.867	5.383	0.479
`90-3-C-2	3.67	17.28	9.855	13.763	8.213	5.550	0.402
90-6-C-0	4.245	20.115	12.690	19.103	10.575	8.528	0.446
90-9-C-6	4.03	19.78	12.355	19.143	10.296	8.847	0.462
90-12-C-4	3.745	16.515	9.090	14.980	7.575	7.405	0.494
120-3-C-4	3.74	19.36	10.435	14.025	8.696	5.329	0.381
120-6-C-6	4.45	19.925	11.000	16.688	9.167	7.521	0.451
120-9-C-0	4.015	18.61	9.685	15.056	8.071	6.985	0.464
120-12-C-2	3.695	17.105	8.180	13.856	6.817	7.040	0.508
150-3-C-6	4.255	21.185	10.760	15.956	8.967	6.990	0.440
150-6-C-4	4.96	21.545	11.120	18.600	9.267	9.333	0.502
150-9-C-2	4.45	20.845	10.420	16.688	8.683	8.004	0.479
150-12-C-0	3.99	19.84	9.415	14.963	7.846	7.117	0.476

Tensile testing of laminates

The test coupons were subjected to a tensile loading on a 50 kN capacity universal testing machine as shown in Figure 2(e)–(f). The grip length on either side of the test coupons was kept as 50 mm and the gauge length was 150 mm. The test was carried at a strain rate of 2 mm per minute. The test was carried until the fracture of the composite. A piece of emery cloth was required to wrap the grips of the machine in cases where slipping was inevitable. In case where emery cloth was insufficient to create the necessary grip, tabs made of glass frp were bonded to the gripping length of the laminates on both sides.

Results and discussion

From the tensile testing of composites, it was observed that, as the ratio of synthetic to natural fiber increased, the tension capacity of the composite increased proportionally. This is due to the high specific strength of carbon fibers. The sequence of failure was from inner to outer, as inner core was made of natural fibers and outer core of synthetic. Initially the load is taken by both (natural + synthetic) as a system. Subsequently delamination is seen between inner and outer core and the entire load is transferred to the outer core. This delamination can be attributed to local defects in the composite which develop during the hand layup process. The local defects like entrapped air while fabrication of laminate can lead to variation in the average weight and average thickness of laminate. This, in turn, affects the fiber volume fraction, requiring a normalization of fiber volume fraction to be performed, to compare the individual results. Table 6 shows the actual fiber volume fraction of all the laminates which are close to the value of 0.5, hence the fiber volume fraction of all laminates was normalised to a value of 0.5 in order to not deviate too far from the median. Since the stress of the laminate is directly proportional to the fiber volume fraction, the normalised stress is given by the equation (1).

${\sigma }_{normalised}={\sigma }_{actual}*\frac{0.5}{f_{actual}}$

(1)

Where f_actual is the actual fiber volume fraction of the laminate. Figure 3 shows the variation of actual stress, normalised stress and strain for different layer configurations and shows comparison of their performance at different temperatures and timings.

Figure 3.

Strain and normalised stress values for (a) C0 configuration (b) C2 configuration (c) C4 configuration (d) C6 configuration.

The tensile stress for all laminates cured at 90 deg. post curing temperature showed a lower value as compared to other temperatures. This could be attributed to the over curing of natural laminate and under curing of synthetic laminates at 90^o. At lower temperatures, the laminates with natural fiber showed a better performance in tension, which can be attributed to the degradation of natural fibers at higher temperatures as evident from the Figure 3(a). The trend showed that with increase in number of synthetic fiber layers, the post curing time for same temperature increased subsequently. As the curing temperature increased, the synthetic laminate was able to cure to a higher degree as seen in Figure 3(b)–(d), thus giving a better performance. For natural laminate, it was seen that the strength degraded as the temperature was increased from 60^o to 150^o due to degradation of natural fibers at higher temperatures. Maximum strength for natural fiber was achieved at 60^o for 3 h.

Taguchi DOE results

The Taguchi DOE was analysed for normalised values of stress. The main effects plot ranked the significance of parameters on the tensile strength of the composite laminate as shown in Figure 4(a). It was seen that the number of layers was the most significant factor, followed by the temperature and followed by post curing time.

Figure 4.

(a) Main effect plot for Taguchi DOE (b) Multilinear equation fit (c) - (f) Time-Temperature variation for 0, 2, four and six layers of carbon fiber respectively.

A multilinear regression analysis was performed to fit the curve and predict the tensile strength, it yielded the following equation.

$\mathbf{T}\mathbf{e}\mathbf{n}\mathbf{s}\mathbf{i}\mathbf{l}\mathbf{e}\hspace{0.17em}\mathbf{s}\mathbf{t}\mathbf{r}\mathbf{e}\mathbf{n}\mathbf{g}\mathbf{t}\mathbf{h}\hspace{0.17em}\left(\mathbf{M}\mathbf{P}\mathbf{a}\right)=1198.54\hspace{0.17em}-\hspace{0.17em}30.87*A\hspace{0.17em}-\hspace{0.17em}29.63*B\hspace{0.17em}-\hspace{0.17em}45.47*C+0.29*A^2+5.25*B^2+24.39*C^2\hspace{0.17em}-\hspace{0.17em}8.37*{10}^{-4} *A^3\hspace{0.17em}-\hspace{0.17em}0.192*B^3\hspace{0.17em}-\hspace{0.17em}2.61*C^3\hspace{0.17em}-0.14*A*B+7.23*{10}^{-2}\hspace{0.17em} *A*C+1.06*B*C$

(2)

Where A = Temperature in degree Celsius, B = Time in hours, C = Number of Carbon layers.

Figure 4(c)–(f) shows that the strength of laminate is maximum and almost similar at two extreme ends of the temperature range taken for this experiment. However, the time for post curing is maximum (12 h) for the lower range of the temperature (60^oC) and the post curing time is the least (3 h) for the higher range of the temperature (150^oC).

Particle swarm optimization

A bio-inspired algorithm of particle swarm optimization was used for finding the optimum temperature and time in the range provided. The different combinations of temperature and time may produce local maximum for a particular number of carbon layers, however we intend to find the global maximum which shall give the optimum temperature and time.

Particle swarm optimization (PSO) is a computational optimization iterative technique that simulates the collective behaviour of a swarm of particles to solve optimization problems. PSO offers several salient features that make it a popular and effective technique in various fields of study.

The PSO is efficiently able to search large solution spaces. It achieves this through the collaboration and exploration of a swarm of particles. The potential solution is represented by individual particle and moves through the solution space by adjusting its position based on its own best-known position and the global best-known position of the swarm. The movement of particles is governed by mathematical formulas that balance exploration and exploitation. The velocity update is given by Equation (3) and the position update equation is given by Equation (4).

$v_{ij}\left(t+1\right)={w*v}_{ij}\left(t\right)+c_1{r}_1\left[x_{ij}^L-x_{ij}\left(t\right)\right]+c_2{r}_2\left[x_j^G-x_{ij}\left(t\right)\right]$

(3)

$x_{ij}\left(t+1\right)=x_{ij}\left(t\right)+v_{ij}\left(t+1\right)$

(4)

where $v_{ij}\left(t+1\right)$ is the updated velocity of the ith particle in the jth dimension at time (t + 1), ‘w’ is the inertia weight, cognitive weight c₁ and social weight c₂ are the acceleration coefficients, r₁ and r₂ are random values between 0 and 1, $x_{ij}^L$ is the local best position of the ith particle in the jth dimension, x_ij(t) is the current position of the ith particle in the jth dimension at time ‘t’, and $x_j^G$ is the best position of the swarm in the jth dimension. ²⁶ PSO is easier to implement as compared to other optimization algorithms, since PSO has fewer control parameters and does not require gradient information. This makes it relatively straightforward to implement and apply to a wide range of optimization problems. A solution near to optimum is found and the particle convergence can be observed in real time. The collaborative nature of the swarm allows for the exchange of information and learning from the best solutions found by individual particles. This enables PSO to effectively explore the solution space and converge towards promising regions that may contain optimal solutions. Table 7 shows the control parameters selected for PSO algorithm.

Table 7.

PSO Control parameters.

Inertial weight (w)	0.7
Cognitive weight (c1)	1.5
Social weight (c2)	1.5
Acceleration coefficient r1	Random between 0 & 1
Acceleration coefficient r2	Random between 0 & 1
Initial global best	−1*infinity
Number of particles	50
Number of maximum iterations	50

The PSO was applied, and solution was checked for 0, 2, four and six layers of synthetic fibers. It was seen that the final solution was similar for all, the results for six layers are shown in Figure 5. The standard deviation and the swarm density curve show the convergence of particles towards the optimum value around the end of 16 iterations. The standard deviation at the beginning of iteration was as high as 40, as the particles were scattered around to search the potential solution. As the iterations increased, the particles tend towards the possible solution, the standard deviation decreased, and the swarm density increased. The particle trajectories show the swarming of particles towards the solution, the convergence curve shows the tensile strength obtained.

Figure 5.

(a) Particle trajectories (b) Swarm diversity (c) Convergence curve (d) Standard deviation curve.

The optimum value by particle swarm optimization was found to be at a temperature of 60^oC for a curing period of 12 h, this is evident from the graphs shown in Figure 4(c)–(f).

Conclusions

The investigation focused on optimizing the post curing parameters, namely temperature and time, for Ramie-Carbon hybrid laminate fabrication. The L16 Taguchi design of experiment facilitated the selection of experimental combinations for laminate fabrication, followed by uniaxial tensile testing to evaluate performance. Conclusions drawn from the study, incorporating both uniaxial tensile testing outcomes and bio-inspired optimization techniques, are summarized as follows:

1. The primary influential factor affecting the tensile strength of the laminate, subsequent to the number of layers, is the post curing temperature, followed by the post curing time.

2. Notably, pure natural laminate exhibited maximum strength at lower temperatures (60°C for 3 h). Conversely, the hybrid laminate demonstrated comparable performance at lower temperatures (60°C) with extended curing times (12 h), as well as at higher temperatures (150°C) with shorter curing times (3 h).

3. An observed trend indicates that as the number of Carbon fabric layers increased, the laminate exhibited enhanced performance at higher temperature ranges.

4. The imposition of a narrow temperature range (60 to 150°C) aimed to mitigate the degradation of Ramie fiber at elevated temperatures. Considering future research endeavours, broader exploration encompassing a wider range of temperature, time, and diverse combinations of synthetic fabrics is warranted.

In conclusion, the findings underscore the critical role of post curing parameters in governing the mechanical properties of Ramie-Carbon hybrid laminates. Insights gleaned from this study contribute to advancing the understanding of laminate optimization techniques and provide valuable directions for future investigations in composite material science.