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Abstract

This study focuses on the development of a novel Polyamide 6/Talc/Carbon Nanotube (PA6/Talc/CNT) thermoplastic nanocomposite manufactured via Fused Deposition Modeling (FDM). The primary objective is to optimize the material’s Young’s modulus, impact strength, electrical conductivity, and thermal stability. To identify the optimum processing parameters (CNT content, talc content, nozzle temperature, and feed rate), the Taguchi method combined with grey relational analysis was employed. The resulting samples were then analyzed via SEM, TGA, DSC, and four-probe tests. Findings showed that a significant enhancement in electrical conductivity was specifically obtained in the nanocomposite containing 1 wt% CNTs and 4 wt% talc nanoparticles. Moreover, incorporating CNTs and talc into PA6 improved the thermal stability of the resulting nanocomposites. According to the Taguchi analysis, the CNT and talc content exerted a greater influence on Young’s modulus and impact strength than the other process parameters. Grey relational grade optimization revealed that the optimal parameters to enhance these mechanical properties are a talc content of 4 wt%, a CNT content of 1 wt%, a nozzle temperature of 270°C, and a feed rate of 30 mm/s.

Keywords

Fused deposition modeling (FDM)PA6/talc/CNT nanocomposite Young’s modulus impact strength thermal stability Mining applications

Introduction

Polyamide 6 (PA6), or Nylon 6, is valued across industries like automotive, electrical, and textiles^1,2 for its strong mechanical properties, chemical resistance, and durability.^3,4 Its semi-crystalline structure consists of an amorphous phase and three crystalline phases (α, γ, and β), with the β-phase being an intermediate form. The formation of the dominant α or γ structure depends on the alignment of alkyl chains and the length of hydrogen bonds during crystallization.^5,6 Variations in PA6’s crystalline structure significantly impact on its mechanical properties, including strength and stiffness. This relationship enables the optimization of processing parameters to achieve a specific stiffness-to-flexibility ratio in the end product.^7,8 Advancements in the field now prominently feature the use of reinforcement agents such as carbon nanotubes,^9,10 graphene,¹¹ carbon fibers,¹² talc,¹³ etc. The incorporation of these additives significantly enhances key properties of PA6, including its stiffness, strength, thermal stability, and electrical conductivity.^14,15

Carbon nanotubes (CNTs) are widely used as nanofillers to improve the properties of polymer composites due to their exceptional features.^10,14 Their incorporation into PA6 matrices has been shown to significantly improve a range of properties, including mechanical strength,^15,16 Young’s modulus,¹⁷ impact resistance,¹⁸ and electrical/thermal conductivity.¹⁹ Consequently, CNTs have garnered significant interest across diverse fields such as electronics,^20,21 energy,²² sensors,²³ and biomedicine.^22,23 The enhancement in the properties of PA6 have been observed even at low CNT loadings, for instance, 1 wt% for electrical conductivity and impact strength¹⁸ and 0.2 wt% for thermal conductivity.¹⁹ Despite the numerous advantages of CNTs, the use of these nanoparticles is limited by high cost, potential environmental and health risks due to toxicity, and the economic infeasibility of excessive use. Furthermore, some studies indicate that CNT incorporation can detrimentally affect certain mechanical properties of PA6,^13,24,25 demonstrating that they do not universally enhance polymer nanocomposites. To mitigate the limitations of CNTs, alternative additives like talc nanoparticles (Mg₃Si₄O₁₀(OH)₂) are being considered due to their excellent nucleation ability, low cost, high stiffness, and good dimensional stability.²⁶ Talc is formed from layers of magnesium silicate bonded by weak van der Waals forces. This structure and its inherent softness facilitate the effective dispersion of talc particles within polymer composites.^26–29 Furthermore, the presence of silanol groups on the edges of talc particles enables the formation of covalent bonds with compatible chemical functional groups.²⁷ Guo et al.¹³ reported that addition of talc nanoparticles into PA6 improved Young’s modulus and tensile strength of PA6/talc nanocomposites. Long et al.³⁰ observed that a low loading of 1 wt% talc led to an enhancement of tensile strength and elongation at break. Domingo et al.³¹ found that the tensile and flexural strength of PA6/PA66/talc nanocomposite improved by adding talc nanoparticles, but its impact strength decreased. It was also reported by Yousfi et al.³² that the talc nanoparticles increased thermal stability, yield strength and Young’s modulus of PA6 but decreased its elongation at break.

Previous research works declared that single-filler systems are generally inadequate for enhancing the full spectrum of mechanical properties in polymer nanocomposites. This has led to the strategy of employing hybrid fillers to achieve synergistic improvements. For instance, Pisani et al.³³ enhanced the Young’s modulus of PA6 using a combination of graphene and carbon nanotubes. Similarly, Farhadpour et al.³⁴ improved electrical conductivity by adding both CNTs and conductive carbon black (CCB), while Mousavi et al.³⁵ boosted impact resistance and damping performance by incorporating thermoplastic polyurethane (TPU) with CNTs into PA6. Batakliev et al.³⁶ observed that incorporation of both carbon nanotubes (CNTs) and graphene (GPN) in the polylactic acid (PLA)/GPN/CNT composite, indicated better hardness and elastic modulus compared to the PLA/CNT composites loaded with a single carbon nanofiller. Ghasemi et al.³⁷ reported that by addition of 30 wt% talc and 0.7 wt% graphene into polypropylene (PP), the tensile strength, elastic modulus, impact strength were simultaneously improved. Prior research confirms that hybrid filler systems yield superior mechanical properties in polymer composites compared to single-filler composites. Based on these findings, the present study utilizes a combination of two common reinforcing agents: carbon nanotubes and talc nanoparticles.

The use of fused deposition modeling (FDM) for polymer nanocomposites has expanded considerably due to its unique advantages, including cost-effectiveness, design flexibility, environmental benefits, and effective nanoparticle dispersion.³⁸ Achieving optimal mechanical properties in these printed parts is highly dependent on identifying precise printing parameters. This is evidenced by studies where specific conditions were crucial. Numerous studies highlight the significant impact of FDM parameters and nanofiller content on the mechanical performance of polymer nanocomposites. Key findings include the positive effect of nozzle temperature on strength^38–40 and the identification of optimal nanofiller concentrations, such as 1.5 wt% graphene for PP⁴¹ and ∼2.9 wt% for CNTs in PLA.³⁹ Researchers have successfully optimized parameters like print speed and temperature to enhance Young’s modulus, impact strength, and thermal stability in various systems including PP/EPDM/TiO₂ and PA6/GPN.^42–44 The broader influence of these processing conditions on material behavior, including inter-layer adhesion and cooling dynamics, is also well-established.^45–50

Recent literature reviews indicate growing interest in ternary nanocomposites for improved mechanical properties. While ternary nanocomposites are gaining traction for their superior mechanical properties, PA6/Talc/CNT combinations represent an unexplored area of research. This study fills this critical gap by pioneering the development of this novel nanocomposite using an innovative 3D printing technique. The PA6/Talc/CNT nanocomposite leverages a synergistic filler system: talc acts as a reinforcing agent to increase crystallinity and stiffness, while CNTs enhance electrical conductivity, thermal stability, and fracture toughness. To efficiently optimize the process, the Taguchi method is employed to analyze the effects of talc content, CNT content, feed rate, and nozzle temperature on key properties including impact strength, Young’s modulus, thermal characteristics, and electrical conductivity, with a minimized number of experiments. Furthermore, gray relational analysis is introduced to identify the optimal parameters for the simultaneous enhancement of Young’s modulus and impact strength.

Materials and methods

Materials

The study utilized multi-walled carbon nanotubes (≥90% purity) from INP Corporation, featuring average dimensions of 1.5 μm in length and 9.5 nm in diameter, along with a specific surface area ranging from 250 to 300 m²/g. The matrix material consisted of commercial PA6 powder (SINTERLINE XP 1537/A, Solvay SA Group), characterized by near-spherical particles averaging 50 µm in diameter. Talc particles, with a mean particle size of 1.7 μm and commercially designated as Micro-talc I.T. EXTRA, were also utilized.

Fused deposition modeling (FDM)

This study employed fused deposition modeling (FDM) to fabricate PA6/Talc/CNT ternary nanocomposites. The PA6/Talc/CNT powder nanocompositions were first dried in an oven at 80°C. Then, a mechanical mixer was employed to blend PA6 powders with CNTs and talc nanoparticles at different loadings. Composite filaments (1.75 mm radius) were produced using a Filabot EX2 extruder (USA) operating at 210°C with a screw rotation speed of 3 rpm. The quality of the produced filaments was visually inspected to ensure the presence of holes or porosity. The FDM process was executed using a Wanhao D12-230 3D printer, with systematic variation of nozzle temperature and feed rate parameters. Constant process parameters are detailed in Table 1.

Table 1.

Values of constant parameters of FDM.

Parameters	Air gap	Bed temperature	Nozzle diameter	Layer thickness	Built orientation
Values	0.04 mm	80°C	0.4 mm	0.2 mm	0°

Taguchi design

To optimize the experimental process, this study employed the Taguchi design approach, which significantly reduces the required number of tests. The experimental layout was generated using Minitab® 17 software, with four input parameters of talc content, CNT content, nozzle temperature, and feed rate. The complete design matrix is provided in Table 2.

Table 2.

Measured values of elastic modulus and flexural strength based on L9 Taguchi design.

Run	Talc (wt%)	CNT (wt%)	Feed rate (mm/s)	Nozzle temperature (°C)	Young’s modulus (GPa)	Impact strength (kJ/m²)
1	0	0	15	250	2.24 ± 0.18	12.9 ± 2.6
2	0	1	30	260	2.78 ± 0.26	22.3 ± 3.3
3	0	2	45	270	2.54 ± 0.23	18.1 ± 2.2
4	4	0	30	270	3.12 ± 0.28	16.3 ± 2.1
5	4	1	45	250	2.88 ± 0.22	14.6 ± 1.7
6	4	2	15	260	2.75 ± 0.19	15.4 ± 2.3
7	8	0	45	260	2.4 ± 0.15	11.2 ± 1.4
8	8	1	15	270	3.03 ± 0.23	17.2 ± 1.8
9	8	2	30	250	2.63 ± 0.20	12 ± 1.3

Each parameter is considered at three levels. The output responses focused on Young’s modulus and impact strength. Following Taguchi’s orthogonal array principles, an L9 array was selected for the experimental design. To ensure reliability, each experimental condition was tested with three replicates. The selected parameters were determined based on prior studies^13,20–50 and preliminary tests, which revealed that talc content, CNT content, feed rate, and nozzle temperature significantly influence the composite’s properties.

Thermal experiments

The thermal properties of the fabricated samples were evaluated using differential scanning calorimetry (DSC; Netzsch 200 F3 Maia). The analysis was performed under nitrogen atmosphere with a constant heating/cooling rate of 10°C/min, following a three-stage temperature protocol (20–220°C) comprising heating, cooling, and reheating cycles. This procedure enabled determination of the melting (T_m) and crystallization (T_c) temperatures. Sample crystallinity (X_c) was subsequently calculated using equation (1).

X_{c} = (\frac{Δ H_{m} \begin{array}{l} - & Δ H_{c} \end{array}}{Δ H_{0} \begin{array}{l} \times & W_{p} \end{array}}) \times 100 %

(1)In this equation,

{Δ H}_{m}

is the melting enthalpy,

{Δ H}_{c}

is the crystallization enthalpy, W_p is the weight fraction of PA6 in the composite, and

{Δ H}_{0}

is the melting enthalpy for 100% crystalline PA6 (230 J/g).

Tensile experiment

Fused deposition modeling (FDM) was employed to manufacture specimens for tensile testing. Tensile specimens were fabricated in compliance with ASTM D-638 standard and evaluated using a Zwick/Roell-Z100 universal testing machine at a constant crosshead speed of 50 mm/min. Impact resistance was assessed through Izod impact testing following ASTM D-256 standard. Dimensions of tensile samples were given in Figure 1.

Figure 1.

Dimensions of tensile samples.

Electrical experiment

The electrical resistance of the printed samples was measured using a four-probe electrical conductivity meter (Suragus, Germany). The electrical resistivity of the samples was calculated using the equation ρ = RA/L, where L is the thickness of the sample, A is the area, and R is the resistance measured.⁵¹ The electrical tests of printed samples were performed at room temperature and 55% relative humidity.

Microstructure observation

Microstructural characterization was performed using a VEGA-TESCAN-XMU scanning electron microscope. Sample preparation involved cryogenic fracture in liquid nitrogen to expose pristine fracture surfaces, followed by gold coating using an Agar Scientific B7340 sputter coater (UK) to ensure adequate conductivity for SEM imaging. The samples were immersed in liquid nitrogen for around 70 min.

Results and discussion

Analysis of thermal properties

The thermal characteristics of the PA6/Talc/CNT nanocomposite were investigated through differential scanning calorimetry (DSC). The results of DSC analysis for PA6/Talc/CNT nanocomposite were shown in Figure 2. The values of melting temperature, crystallization temperature and percentage crystallinity of PA6/Talc/CNT nanocomposite were obtained from DSC test, as given in Table 3.

Figure 2.

Results of DSC test for PA6/Talc/CNT.

Table 3.

Thermal properties of PA6/Talc/CNT nanocomposite obtained by DSC test.

Thermal properties	PA6 + 0 wt% talc + 0 wt% CNT	PA6 + 4 wt% talc + 1 wt% CNT	PA6 + 8 wt% talc +2 wt% CNT
Crystallization temperature (°C)	195.9	198.8	201.5
Crystallinity (%)	21.8	24.5	26.9
Melting temperature (°C)	215.8	218.6	223.8

From Table 3, it can be seen that a rise of talc and CNT in the PA6 improved the crystallization and melting temperatures of PA6/Talc/CNT nanocomposite. Furthermore, the results of DSC in Table 3 show that the increase of the amount of talc and CNT nanoparticles improved the crystallinity percentage of the nanocomposite, because these nanoparticles act as nucleation agents in PA6.

For investigating the thermal properties of PA6/Talc/CNT nanocomposite, the thermogravimetric analysis (TGA) was conducted in the range of 0–600°C, as shown in Figure 3. As can be seen from TGA results in Figure 3, the decomposition temperature of PA6 increased by addition of talc and CNT. When the amount of nanoparticles is 0 wt%, the thermal degradation of PA6 starts at 305°C and its mass decreases continuously until 518°C. However, by adding nanoparticles, the decomposition temperature of PA6/Talc/CNT nanocomposite increases to 349°C.

Figure 3.

Thermogravimetric analysis of PA6/Talc/CNT nanocomposite.

The incorporation of nanoparticles in the PA6/Talc/CNT nanocomposite significantly constrains polymer chain mobility, thereby elevating its thermal decomposition temperature.^4–10 Thermal stability was quantitatively assessed through characteristic degradation temperatures (T10%, T50%, T90%) as presented in Table 4. Based on Table 4, the thermal stability of PA6 enhanced by increasing the talc and CNT contents.

Table 4.

Weight loss temperature at 10, 50 and 90%.

Temperature	PA6 + 0 wt% talc +0 wt% CNT	PA6 + 4 wt% talc + 1 wt% CNT	PA6 + 8 wt% talc + 2 wt% CNT
T₁₀ (°C)	336	351	374
T₅₀ (°C)	421	432	448
T₉₀ (°C)	462	487	501

Analysis of electrical properties

Figure 4 displays the volume conductivity of printed nanocomposite samples containing different nanoparticle concentrations. The results indicate that incorporating CNTs and talc nanoparticles into PA6 enhances the samples’ conductivity. Specifically, a mixture of 1 wt% CNT and 4 wt% talc increased conductivity to 49 s/m. However, further increasing the CNT to 2 wt% and talc to 8 wt% led to a decline in conductivity. This initial improvement is attributed to the high conductivity of CNTs, whereas the subsequent reduction occurs due to the insulating nature of talc particles at higher concentrations (8 wt%). Additionally, raising the CNT and talc content to 2 wt% and 8 wt%, respectively, promotes nanoparticle agglomeration, resulting in an uneven surface and microstructural defects, as detailed later. These defects act as insulating regions, further diminishing the volume conductivity of the nanocomposite.

Figure 4.

Volume conductivity of printed samples containing different nanoparticle contents.

Microstructure analysis

The microstructure of the printed samples is analyzed to assess the distribution of nanoparticles in the PA6/Talc/CNT nanocomposite and examine the bonding quality between filament layers. Figure 5 displays the fracture surface of tensile specimens with different amounts of talc and CNTs. A comparison of the fracture surfaces reveals a transition from the smooth, homogeneous morphology of pure PA6 (Figure 5(a)) to a rough texture upon the addition of nanoparticles (Figure 5(b) and (c)). Moreover, increasing the nanoparticle concentration changes their distribution within the microstructure. In Figure 5(b), with 4 wt% talc and 1 wt% CNT, the nanoparticles are well-dispersed within the nanocomposite. This improves the interfacial interaction between the nanoparticles and the PA6 matrix, enhancing the material’s mechanical properties. However, when the talc and CNT content rises to 8 wt% and 2 wt%, nanoparticle agglomeration becomes evident (Figure 5(c)). This uneven dispersion negatively impacts the mechanical performance of the nanocomposite.

Figure 5.

Fracture surface of nanocomposite for (a) Talc = 0 wt%, CNT = 0 wt%, (b) Talc = 4 wt%, CNT = 1 wt%, (c) Talc = 8 wt%, CNT = 2 wt%.

The dispersion of nanoparticles within the polymer matrix is also governed by printing parameters, specifically nozzle temperature and feed rate. These parameters influence the polymer’s viscosity and chain mobility. Typically, a higher nozzle temperature reduces viscosity and increases chain mobility, allowing nanoparticles to move more freely.⁵² This increased mobility, especially when combined with a slower feed rate, can lead to nanoparticle aggregation. This phenomenon results from a decline in nanoparticle-polymer matrix interactions, which in turn raises the probability of inter-nanoparticle collision and bonding. Conversely, a lower temperature or higher feed rate restricts chain mobility, promotes polymer-nanoparticle interaction, and can thus improve dispersion.

Microstructural analysis revealed that the morphology of the fracture surface depends not only on the nanoparticles but also on the printing parameters, specifically nozzle temperature and feed rate. Figure 6 illustrates the influence of feed rate on the microstructure of the PA6/Talc/CNT nanocomposite. As shown in Figure 6(a), at a print speed of 20 mm/s, the bonding between layers at the fracture surface is weak, leading to the formation of porosity and cracks and consequently a reduction in mechanical properties. However, when the feed rate increases to 30 mm/s (Figure 6(b)), the porosity percentage decreases, indicating stronger interlayer adhesion. Conversely, further increasing the feed rate to 40 mm/s (Figure 6(c)) reintroduces defects in the microstructure, as the higher feed rate reduces deposition time per layer, increases the cooling rate, and thereby reduces the chain interdiffusion.

Figure 6.

Fracture surface of nanocomposite for feed rate of (a) 20, (b) 30, (c) 40 mm/s.

The microstructural analysis was extended to include the fracture surface morphology at varying nozzle temperatures. Figure 7 demonstrates the effect of nozzle temperature on the microstructure of printed samples. As evident in Figure 7(a), a nozzle temperature of 250°C results in poor interlayer adhesion, which can be attributed to the high concentration of porosity and cracks in the fracture surface. This weak bonding is attributed to the higher viscosity of PA6 at this temperature, which hinders effective deposition by reducing filament fluidity and limiting the interpenetration of polymer chains.^23–26 When the nozzle temperature is raised to 260°C (Figure 7(b)), the density of porosity diminishes, giving rise to an enhancement of interlayer bonding as the polymer’s viscosity becomes more favorable for deposition. Further increasing the temperature to 270°C (Figure 7(c)) nearly eliminates cracks and porosity by creating optimal polymer flow and chain interdiffusion, leading to a significant improvement of layer adhesion.

Figure 7.

Fracture surface of nanocomposite for nozzle temperature of (a) 250, (b) 260, (c) 270°C.

To further examine the microstructural changes, the porosity percentage was measured by analyzing SEM images in ImageJ software, as shown in Figure 8. According to Figure 8, the lowest porosity percentage was obtained at a feed rate of 30 mm/s, which is due to greater adhesion between the filament layers. Furthermore, an increase in nozzle temperature to 270°C resulted in greater adhesion between filament layers and a significant reduction in porosity percentage. A reduction in the porosity percentage leads to an improvement in the mechanical properties of the samples.

Figure 8.

Effect of feed rate and nozzle temperature on porosity percentage.

Taguchi design analysis

To evaluate how process parameters affect the Young’s modulus and impact strength of fabricated samples, a signal-to-noise ratio (S/N) analysis was employed. The Taguchi experimental design accounts for both disturbing factors (noise variables that affect the process but are impractical to control) and controllable factors (adjustable process parameters). Rather than analyzing raw output responses directly, the Taguchi method utilizes S/N ratios as performance metrics. These ratios are categorized based on optimization objectives: “smaller is better,” “larger is better,” or “nominal is best.” The corresponding S/N ratios for the first two scenarios are computed using equations (2) and (3), respectively.

\frac{S}{N} = - 10 \log 10 (\frac{1}{n} \sum_{i = 0}^{n} ({y_{i}}^{2}))

(2)

\frac{S}{N} = - 10 \log 10 (\frac{1}{n} \sum_{i = 0}^{n} (\frac{1}{{y_{i}}^{2}}))

(3)In these equations, n represents the repetition of experimental tests (n = 3) and y_i represents the recorded response for the ith time. Since the maximum values of Young’s modulus and impact strength are expected, equation (3) is used to obtain the S/N ratio.

The Minitab software was used to determine the S/N ratios and identify the optimal parameter levels for each response, as presented in Table 5. This table also includes the corresponding Young’s modulus and impact strength values. Tables 6 and 7 detail the S/N ratios for various parameter levels affecting Young’s modulus and impact strength, respectively.

Table 5.

S/N ratios for Young’s modulus and impact strength.

Run	1	2	3	4	5	6	7	8	9
Young’s modulus (GPa)	2.24	2.78	2.54	3.12	2.88	2.75	2.4	3.03	2.63
S/N for young’s modulus	7.01	8.88	8.10	9.88	9.19	8.79	7.60	9.63	8.40
Impact strength (kJ/m²)	12.9	22.3	18.1	16.3	14.6	15.4	11.2	17.2	12
S/N for impact strength	22.21	26.97	25.15	24.24	23.29	23.75	20.98	24.71	21.58

Table 6.

Results of S/N analysis for Young’s modulus.

Level	Talc	CNT	Feed rate	Nozzle temperature
1	7.994	8.164	8.473	8.197
2	9.286	9.233	9.054	8.424
3	8.544	8.427	8.296	9.203
Delta	1.292	1.068	0.758	1.006
Rank	1	2	4	3

Table 7.

Results of S/N analysis for impact strength.

Level	Talc	CNT	Feed rate	Nozzle temperature
1	24.78	22.48	23.56	22.36
2	23.76	24.99	24.26	23.90
3	22.43	23.50	23.14	24.70
Delta	2.35	2.51	1.12	2.34
Rank	2	1	4	3

The delta values, calculated as the difference between the minimum and maximum S/N ratios, indicate the relative influence of each parameter on the responses. The parameters with higher delta values demonstrate greater impact on a response. The relationship between process parameters and responses is further illustrated in Figure 9, which plots the S/N ratios for both Young’s modulus and impact strength.

Figure 9.

Effect of parameters on (a) Young’s modulus, (b) impact strength.

Analysis of Tables 6 and 7 reveals that talc content exerts the most significant influence on Young’s modulus, while CNT content predominantly affects impact strength in the printed parts. As shown in Figure 9(a), optimal Young’s modulus occurs at 4 wt% talc and 1 wt% CNT. This enhancement correlates with the SEM observations in Figure 5(b), which demonstrate excellent nanoparticle dispersion within the PA6 matrix at these concentrations. Conversely, higher talc and CNT loading (8 wt% and 2 wt%, respectively) leads to nanoparticle agglomeration, impairing their distribution and consequently reducing Young’s modulus. This reduction of Young’s modulus may also stem from increased PA6 crystallinity at these higher filler concentrations. While crystallinity improves matrix stiffness, it can occasionally diminish overall specimen strength, as documented in prior studies.^{13,19–23,28–30}

Figure 9(b) demonstrates that the maximum impact strength is achieved with a 0 wt% talc and 1 wt% CNT. The detrimental effect of talc on impact strength likely arises from its larger particle size, which creates stress concentration sites within the PA6/Talc/CNT nanocomposite structure. Figure 9(a) and (b) demonstrate that increasing the feed rate to 30 mm/s enhances both Young’s modulus and impact strength in the PA6/Talc/CNT nanocomposite, while further elevation to 40 mm/s leads to deterioration of these mechanical properties. As evidenced by Figure 6(b), the observed improvement at 30 mm/s correlates with enhanced interlayer adhesion and reduced void formation between deposited filaments. These structural imperfections act as stress concentration points, significantly compromising the mechanical performance of material. These findings align with previous studies reported in references^{8,42,52–56,} which similarly established the critical relationship between processing parameters, structural integrity, and mechanical properties in polymer nanocomposites.

It can be observed from Figure 9(a) and (b) that an elevation of nozzle temperature to 270°C enhanced the Young’s modulus and impact strength of PA6/Talc/CNT nanocomposite, so that the highest Young’s modulus and impact strength were obtained at nozzle temperature of 270°C. As can be seen from Figure 7(c), the greater adhesion between the printed layers caused by the lower viscosity of the PA6 is responsible for the increased Young’s modulus and impact strength at nozzle temperature of 270°C. The robust bonding between printed layers at this temperature minimizes the formation of cavities and cracks (Figure 7(c)), leading to improved mechanical properties in terms of both Young’s modulus and impact strength. Additionally, as shown in the SEM image in Figure 7(a), defects in the microstructure caused by poor filament layer adhesion significantly degrade these properties at a nozzle temperature of 250°C. It is important to note that insufficient temperature during filament deposition results in weak interlayer bonds, primarily due to inadequate molecular chain fusion between the layers. These findings align well with the observations reported in other studies.^{35–41,53–56}

Grey relational analysis

To simultaneously improve Young’s modulus and impact strength, determining the ideal processing parameters is crucial. The Taguchi method results were subsequently optimized through grey relational analysis to establish these optimal parameter settings. This approach involves the normalization of S/N values, ensuring they fall within the range of 0–1.⁵⁷ The normalization process was carried out using equation (4).⁵⁷

Z_{i j} = \frac{Y_{i j} - \min (Y_{i j})}{\max (Y_{i j}) - \min (Y_{i j})}

(4)In equation (4), Z_ij denotes the normalized values, which fall within the range of 0–1, while Y_ij denotes the outcome of the i_th test for the j_th response.⁵⁷ Table 8 summarizes the normalized signal-to-noise (S/N) values corresponding to each response.

Table 8.

Normalized S/N ratios for Young’s modulus and impact strength.

Run	Young’s modulus	Impact strength
1	0.0000	0.1532
2	0.6136	1.0000
3	0.3409	0.6216
4	1.0000	0.4595
5	0.7273	0.3063
6	0.5795	0.3784
7	0.1818	0.0000
8	0.8977	0.5405
9	0.4432	0.0721

The gray relational coefficient is calculated using equation (5). This involves converting the normalized values into a deviation sequence (Δ_ij), which is obtained by calculating the difference between the normalized values and the ideal target value (i.e., 1).⁵⁷

Δ_{i j} = 1 - Z_{i j}

(5)

Equation (6) is used to compute the gray relational coefficient (GC_ij), which measures the correlation between the ideal and actual test results.⁵⁷

{G C}_{i j} = \frac{Δ_{\min} + λ Δ_{\max}}{Δ_{i j} + λ Δ_{\max}}

(6)In equation (6), Δ_min and Δ_max are equal to 0 and 1, respectively. The discrimination coefficient (λ), which varies from 0 to 1, is chosen according to the analysis requirements. In this study, λ was assigned a value of 0.5.⁵⁷ The gray relational coefficient (GC) for each response was then computed using equation (6), as the results were listed in Table 9.

Table 9.

Gray relational and G_i coefficients for each response.

Run	Young’s modulus	Impact strength	G_i coefficient
1	0.3333	0.3712	0.352285
2	0.5641	1.0000	0.782051
3	0.4314	0.5692	0.500302
4	1.0000	0.4805	0.74026
5	0.6471	0.4189	0.532963
6	0.5432	0.4458	0.494497
7	0.3793	0.3333	0.356322
8	0.8302	0.5211	0.675658
9	0.4731	0.3502	0.411638

Once the gray relational coefficients were calculated for all experiments, the final coefficient (G_i) was computed using equation (7). The experiment yielding the highest Gi value demonstrates the optimal parameter levels.⁵⁷

G_{i} = \frac{1}{m} \sum (α_{j} \times G C_{i j})

(7)

Equation (7) incorporates two key parameters: m represents the combined effect coefficient across all responses, while α_j indicates the weighting coefficient for each individual response. In this investigation, equal weighting coefficients were applied to all responses. The calculated G_i coefficients for each experimental run are presented in Table 9.

Analysis of these results reveals that experiment 4 yielded the maximum G_i coefficient for Young’s modulus, whereas experiment 2 produced the highest value for impact strength. This indicates that sample 4 demonstrated optimal stiffness performance, while sample 2 exhibited superior toughness characteristics. Significantly, experiment 2 emerged as the optimal condition for concurrently enhancing both Young’s modulus and impact strength. The corresponding parameters for this optimal condition were: 1 wt% CNT content, 0 wt% talc content, a feed rate of 30 mm/s, and a nozzle temperature of 260°C.

To confirm the reliability of the obtained results, several verification tests have been performed under optimal parameter conditions (Talc = 0 wt%, CNT = 1 wt%, feed rate = 30 mm/s, Nozzle temperature = 260°C). In addition, several tests were performed in the initial conditions of the parameters (Talc = 0 wt%, CNT = 0 wt%, feed rate = 20 mm/s, Nozzle temperature = 250°C) to compare with the optimal conditions. Results of experiments at initial and optimal conditions were shown in Table 10. As can be seen from Table 10, the Young’s modulus and impact strength values in the optimal conditions have improved by 24% and 72%, respectively, compared to the initial conditions.

Table 10.

Results of experiments at initial and optimal conditions.

Run	Initial conditions	Optimal conditions
Talc (wt%)	0	0
CNT (wt%)	0	1
Feed rate (mm/s)	20	30
Nozzle temperature (°C)	250	260
Young’s modulus (GPa)	2.23	2.74
Impact strength (kJ/m²)	12.8	22.1

Based on preliminary observations showing simultaneous improvement in both Young’s modulus and impact strength at nozzle temperature of 270°C, this parameter was adjusted at 270°C in the optimal conditions for further evaluation. In addition, the Young’s modulus improved at talc content of 4 wt% and the impact strength decreased slightly, so the talc content was also chosen at 4 wt% to examine the values of the responses obtained. Table 11 presents comparative results between the initial and modified optimal conditions. According to Table 11, the Young’s modulus and impact strength values in the new optimal conditions have improved by 49% and 58%, respectively, compared to the initial conditions. Notably, these refined conditions (incorporating 270°C nozzle temperature and 4 wt% talc content) yielded substantially greater Young’s modulus than previous optimal settings, with only a minimal compromise in impact strength performance.

Table 11.

Results of experiments at initial and new optimal conditions.

Run	Initial optimal conditions	New optimal conditions
Talc (wt%)	0	4
CNT (wt%)	1	1
Feed rate (mm/s)	30	30
Nozzle temperature (°C)	260	270
Young’s modulus (GPa)	2.74	3.34
Impact strength (kJ/m²)	22.1	20.3

Conclusion

In this research, the thermal, electrical and mechanical properties of the PA6/Talc/CNT nanocomposite enhanced using the Taguchi method and grey relational analysis. Thermal characterization revealed that incorporating talc and CNT nanoparticles elevated both crystallization and melting temperatures while improving thermal stability through enhanced heat absorption. Electrical conductivity measurements demonstrated optimal performance at 1 wt% CNT and 4 wt% talc loading. Microstructural examination showed excellent nanoparticle dispersion at 1 wt% CNT and 4 wt% talc concentrations, corresponding to improved Young’s modulus and impact strength. However, when amounts of talc and CNT increased up to 8 and 2 wt%, the aggregation of the nanoparticles was detected in the microstructure of the nanocomposite. The Taguchi optimization revealed that maximum Young’s modulus and impact strength were achieved at a feed rate of 30 mm/s combined with a nozzle temperature of 270°C. These optimal processing conditions facilitated superior interlayer adhesion in the nanocomposite, which effectively minimized void formation and microstructural defects. The enhanced interfacial bonding between filament layers directly contributed to the improved mechanical performance observed at these parameters. Through grey relational analysis, the ideal processing parameters for concurrently enhancing Young’s modulus and impact strength were identified as: 1 wt% CNT content, 4 wt% talc content, a feed rate of 30 mm/s, and a nozzle temperature of 270°C.⁵⁸

Footnotes

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 disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors extend their appreciation to the Deanship of Scientific Research at Northern Border University, Arar, KSA for funding this research work through the project number NBU-FFMRA-2025-2443-02.

ORCID iD

Mohamed Ayari

Data Availability Statement

The data supporting the findings of this study are available within the article.*

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Determining the ideal FDM printing parameters to optimize thermal,mechanical and electrical properties of a novel PA6/Talc/CNT thermoplastic nanocomposite

Abstract

Keywords

Introduction

Materials and methods

Materials

Fused deposition modeling (FDM)

Taguchi design

Thermal experiments

Tensile experiment

Electrical experiment

Microstructure observation

Results and discussion

Analysis of thermal properties

Analysis of electrical properties

Microstructure analysis

Taguchi design analysis

Grey relational analysis

Conclusion

Footnotes

Declaration of conflicting interests

Funding

ORCID iD

Data Availability Statement

References