Spunbond Nonwoven Fiber Diameter Analysis & Optimization

Received: 26 June 2024
Revised: 2 September 2024
Accepted: 3 September 2024
DOI: 10.1002/pls2.10163
RESEARCH ARTICLE
Experimental analysis on fiber diameter of spunbond
nonwoven fabrics through Plackett–Burman and
Box–Behnken designs and its impact
on mechanical properties
Kaoutar Abdel-Mouttalib 1,2
| Ayoub Nadi 2
Abdelowahed Hajjaji 1
| Omar Cherkaoui 2
| Samir Tetouani 3
| Samira Touhtouh 1
|
1
Engineering Sciences for Energy
Laboratory (LabSIPE), National School of
Applied Sciences (ENSA), Chouaib
Doukkali University, El Jadida, Morocco
2
Laboratory for Research in Textile
Materials (REMTEX), Higher School of
Textile and Clothing Industries (ESITH),
Casablanca, Morocco
3
Laboratory of Advanced Numerical
Engineering (LINA), Higher School of
Textile and Clothing Industries (ESITH),
Casablanca, Morocco
Correspondence
Ayoub Nadi, Laboratory for Research in
Textile Materials (REMTEX), Higher
School of Textile and Clothing Industries
(ESITH), Casablanca, Morocco.
Email: [email protected]
Abstract
The COVID-19 pandemic sparked a surge in demand for nonwoven protective
materials, prompting a significant increase in nonwoven fabric production. To
advance understanding, particularly in the Spunbond process, we conducted
experiments to analyze the effects of various input parameters on fiber diameter,
and mechanical tests to study how fiber diameter influences the mechanical
properties of spunbond nonwoven fabrics. Employing Plackett–Burman design
and Box–Behnken design with Minitab 18, we have examined different process
parameters and study the cause-and-effect relationships between input parameters and the response. A set of experiments were carried out with nine varying
parameters, including polymer melt index, initial polymer temperature, and air
velocity. By regression modeling, we assessed the effects and interactions of
these factors on fiber diameter. The Box–Behnken approach revealed that only
three factors significantly influenced fiber diameter. Our analysis unveiled valuable insights for optimizing process parameters to achieve a target fiber diameter
of 31.0 μm, crucial for enhancing nonwoven fabric production. Moreover, the
results of the tensile property tests show that fiber diameter influence mechanical properties of nonwoven fabrics. As the fiber diameters increase, the mechanical properties of the nonwoven fabric are increased.
Highlights
• Market research of textile industry, particularly of nonwoven sector.
• Identification of the parameters of the Spunbond process.
• Selection of Placket–Burman and Box–Behnken designs to analyze, optimize, and predict fiber diameter.
• Evaluation of the designs results (analysis of variance, Pareto, Regression
model …), determination of the optimal conditions and the factors that has
most effect on fiber diameter.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided
the original work is properly cited.
© 2024 The Author(s). SPE Polymers published by Wiley Periodicals LLC on behalf of Society of Plastics Engineers.
SPE Polymers. 2025;6:e10163.
https://doi.org/10.1002/pls2.10163
wileyonlinelibrary.com/journal/pls2
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ABDEL-MOUTTALIB ET AL.
• Investigation of the effect of fiber diameter on mechanical properties.
KEYWORDS
Box–Behnken design, mechanical properties, nonwoven protective materials, optimizing
process, Plackett–Burman design, Spunbond process, tensile property tests
1 | INTRODUCTION
In 2020, global demand for textile materials experienced
a significant decline due to the spread of COVID-19, leading to the collapse of several national markets.1,2 Despite
these challenging circumstances, the nonwoven industry
managed to overcome these obstacles due to the high
demand for personal protective equipment (PPE) made
from nonwoven materials, in order to mitigate the devastating impact of the virus.3 According to persistence market research (PMR), the market has witnessed significant
growth since 2015, with its value increasing from an estimated $32.76 billion to $45.2 billion in 2022.3 PMR's forecasts indicate that the market is expected to continue
growing and reach $86.2 billion by the end of 2033.4 The
use of products made from nonwovens has never been
higher than it is currently. This increase is reinforced by
the World Health Organization (WHO), which advocates
for the mandatory use of protective masks to prevent the
transmission of respiratory diseases, including COVID-19.
Among the different types of masks available, three are
found, woven, knitted, and nonwoven masks. Nonwoven
masks are the most commonly used as they provide high filtration efficiency and are less expensive than woven or knitted masks.5 Nonwoven masks are typically manufactured
using a technique called Spun laid, which involves two
industrial methods: Spunbond and Meltblown. These masks
consist of three layers, with a middle layer of Meltblown
nonwoven and two outer layers of Spunbond nonwoven.6
Meltblown nonwovens are often used in conjunction with
Spunbond rather than being used alone.5 The Spunbond
technology dates back to the 1950s–1960s when the German group Freudenberg and the American company
DuPont began commercializing it. Since then, it has
become the most commonly used method.7
Spunbond is an industrial manufacturing process that
involves integrated, continuous, and direct steps to produce
a random or oriented nonwoven fabric, which is thermally
bonded.8 The Spunbond process involves three primary
techniques: extrusion, spinning, and web formation unit.
Without interruption, the process primarily utilizes polypropylene (PP) thermoplastic polymer, which is one of the
most adaptable and widely used polymer,9 due to its outstanding physical properties, such as rigidity and chemical
resistance.10 The PP pellets are melted within an extruder
and subsequently forced through special dies with numerous openings using pumps. The filaments emerging the
die are cooled and solidified using conditioned air. The
entangled filaments are then randomly deposited onto a
moving belt to undergo thermal calendaring.11
Spunbond nonwovens are used in various applications, among others, medical applications (masks, gowns,
etc.), textiles (lining, protective clothing, insulation blankets, etc.), automotive (sound absorption, tapes, cover
parts, insulation tapes, cables, etc.), and agriculture (crop
covers, mulch fabrics, etc.).12 To meet the various application areas, the machine is adjusted based on the “trial and
error” principle, like the majority of industrial installations.13 Furthermore, due to its inherent stochastic nature,
the Spunbond process presents complexities in adjusting
various parameters of the production line. This complexity
often results in increased energy consumption and material
wastage.6 Industries aim to optimize resource utilization
while balancing cost, quality, and lead time. However, relying solely on experimental approaches and trial-and-error
methods is insufficient for continuously improving production capacity and optimizing various parameters.
During the transformation process, multiple parameters interact and impact the quality of the final product,
which is determined by several characteristic properties.
These properties include fabric weight, which provides
insights into material density,14 fiber uniformity, which
indicates material homogeneity,15 and mechanical properties, which offer information about material strength
under different stresses during usage.14
Another important parameter for assessing the quality of nonwovens Spunbond is fiber diameter (FD).16 The
alignment of the aforementioned properties is closely
interconnected with FD, as it serves as a fundamental
quality factor. In this regard, the appropriate selection of
FD depends on the specific requirements of the application and the expertise of the manufacturer.
Due to the complexity of the process and the variety
of factors influencing FD, many studies have initially
focused on considering only a limited number of factors
in order to investigate FD and have not examined their
impact on it. Zhao,17 who analyzed the FD of Spunbond
nonwovens, with a specific focus on the air drawing process, which has a significant impact on FD. The study
was limited to three factors: PFR, ASS, and QP, analyzed
ABDEL-MOUTTALIB ET AL.
by three models to predict the FD: a physical model, a
statistical model, and an artificial neural network (ANN)
model. The results showed a correspondence between the
experimental and predicted values of FD.
Chen et al.,18 have taken five factors into account,
which sets it apart from Zhao17 where only three factors
were chosen, including PFR (g/s). The additional four
factors considered are IAT ( C), IPT ( C), PMI
(g/10 min), and IAV (m/s). The interactions between
these five factors, as well as their relative importance,
were analyzed using a statistical model and an ANN
model to determine their effect on FD. Zhao,19 have
extended the study to incorporate seven factors: IPT ( C),
IAT ( C), PFR (g/s), ASS (rpm), QP (Pa), VG (mm), and
PCAS (rpm). A study was conducted using a multiple
regression model and an ANN model to predict the
FD. However, a description of the relationship between
these seven factors and their respective impacts on the
FD was not provided.
The prior studies have investigated a range of factors
without verifying their relevance influence in determining the quality of FD, potentially compromising the accuracy of the developed models. However, it is crucial to
recognize that the quality of FD cannot be exclusively
determined by the mentioned factors alone, as it is subject to the influence of various additional processing
parameters that affect the quality, structure, and properties of Spunbond nonwovens.
Ensuring the quality of FD necessitates the comprehensive consideration of all factors that have the potential to impact it, and effectively managing and controlling
those factors. To achieve this, we decide to work with all
the factors used by Zhao17,19 and Chen et al.18 for describing and making informed decisions regarding their
effects and predictions. By using PBD and BBD during
the development stage, we can design more efficient and
comprehensive experiments that are less prone to errors.
This approach increases the likelihood of product success
while optimizing process performance.
This study investigates the influence of nine distinct
parameters on FD using empirically derived values. The
parameters include PFR, IAT, ASS, IPT, QP, VG, PCAS,
PMI, and IAV. By employing PBD, the most significant factors were identified, and these were further refined through
BBD to define optimal conditions for enhancing FD quality.
2 | MATERIALS AND METHODS
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grade used for Spunbond process is SABIC® PP 511A,
which is a homopolymer PP resin specifically designed
for extrusion applications with a melt flow index of
25 g/10 min. The PP pellets was extruded through
Spunbond process to produce nonwoven fabrics with
different FD.
2.2 | Experimental procedures of
nonwoven fabric production
The nonwoven fabrics were produced in a single step
using the industrial spunbond technique implemented by
Micagricole company, specialized in plastic films and
nonwoven materials, located in Casablanca, Morocco.
The process involves converting PP pellets into filaments
and subsequently into a nonwoven web through several
key phases, as illustrated in Figure 1. Initially, the PP pellets are melted in a single-screw extruder at high temperatures. The elevated temperature, typically ranging
between 200 and 260 C, ensures complete melting of the
pellets. This molten polymer is then transferred to a
metering pump for precise control over the flow rate,
a critical parameter for the consistency and uniformity of
the produced filaments. The controlled flow is conducted
to a spinneret with thousands of orifices, where it is
extruded into fine filaments. The design of the spinneret
plays a crucial role in determining the final properties of
the filaments, such as their diameter. Once extruded,
these filaments pass through a cooling chamber where
they undergo rapid cooling, a critical step to stabilize the
filament structure.
Cooling is followed by a stretching phase, where
the filaments are elongated under mechanical forces,
enhancing their mechanical properties, particularly
tensile strength. These stretched filaments are then laid
onto a moving conveyor belt, forming a cohesive
nonwoven web.
Finally, this web undergoes additional treatments,
such as thermal bonding, to improve its characteristics,
for the final nonwoven fabric. The adjustment of key process parameters, including polymer temperature, polymer
flow rate, and cooling conditions, is crucial to achieving
the required specifications of the final nonwoven fabric
are detailed in the following sections.
2.3 | Design of experiments:
Plackett–Burman design
2.1 | Materials
The raw material used in this study “PP pellets” was purchased from SABIC based in Riyadh, Saudi Arabia. The
To evaluate the impact of the nine factors on the FD, specifically PFR (g/s), IAT ( C), ASS (rpm), IPT ( C), QP
(Pa), VG (mm), PCAS (rpm), PMI (g/10 min), and IAV
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ABDEL-MOUTTALIB ET AL.
F I G U R E 1 (A) Schematic
representation of the Spunbond
process. (B) Thermal bonding of
the nonwoven web and winding
of the fabric into rolls.
(m/s), we employed PBD. A statistical method used to
assess the effect of several factors on a process without
the need to perform an extensive number of tests.20
PBD is often used for factor screening that does not
differentiate between main effects and interaction effects.
However, it proves valuable in determining factors with
significant impacts by examining the differences between
the two levels of each factor, thus facilitating the screening process effectively. To ensure accuracy, the design
encompassed nine factors and 12 sets of tests, all
designed using Minitab18 software. In our study, PBD
was favored due to its broader scope, enabling resource
conservation since data collection was conducted on an
industrial scale. Each factor was set at two levels: high
level (+1) and low level (1). The high level (+1) represents the maximum value of the parameter, while the
low level (1) represents the parameter's minimum
value, as shown in Table 1. The statistical modeling was
carried out using a linear model represented by a firstorder equation.21
2.4 | RSM: Box–Behnken design
Response surface methodology (RSM) is a statistical and
mathematical technique, commonly valuable in textile
research, as it enables the systematic investigation of
complex interactions among multiple variables, facilitating a deeper understanding of the relationships between
factors and responses.22 BBD is a response surface
analysis method leads to optimize and identify the
ideal process conditions while reducing the number of
ABDEL-MOUTTALIB ET AL.
TABLE 1
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Factors levels, notation and coded values used in Plackett-Burman.
Code
Factors
Notation
X1
Polymer melt index (g/10 min)
PMI
16.3
113
X2
Initial polymer temperature (C )
IPT
212
314
X3
Polymer flow rate (g/min)
PFR
0.282
2.94
X4
Initial air temperature (C )
IAT
8
46
X5
Initial air velocity (m/s)
IAV
57.06
175.56
X6
Air suction speed (rpm)
ASS
2150
2650
X7
Quench pressure (Pa)
QP
175
285
X8
Venturi gap (mm)
VG
15
33
X9
Primary cool air speed (rpm)
PCAS
1000
1600
experiments23 considering the possible interactions
between the factors studied and their effects on the
response based on the PBD results. The significant factors
resulted from PBD, was examined at three levels (1,
0,1) Table 2. The BBD was evaluated by the second-order
equation to compare the association of each factor using
the multiple regression.24
TABLE 2
Low Level (+1)
High Level (1)
Factors and their levels in Box-Behnken Design.
Levels
Code
Factors
Low (1)
Medium (0)
High (1)
X3
PFR
0.282
1.631
2.98
X7
QP
175
230
285
X8
VG
15
24
33
2.5 | Statistical analysis
Variability and uncertainty are inherent in the spunbond
process, as in any manufacturing process.25 Consequently, the objective of statistical analysis is to identify
and examine the factors that significantly influence this
variability. Minitab Version 18 was used to design experiments and perform regression analysis on the experimental data, providing a statistical summary of the various
combinations of experimental variables.
2.6 | Nonwoven FD analysis
The FDs of the nonwoven fabrics were determined using
a GT-B17B optical microscope. The measurements
obtained Figure 2 were used to study the impact of these
variations on the mechanical properties of the Spunbond
nonwoven fabric. By retaining the six factors values
Table 11, we modified the following three factors: PFR,
QP, and VG Table 3. This approach allowed us to obtain
different nonwoven fabric with three different FDs. The
average FDs reported in Table 3.
2.7 | Tensile strength
The tensile test was carried out to study the mechanical
behavior of Spunbond nonwoven fabric using an Instron
5967 tensile tester machine according to ISO 13934-1
standards. The traverse speed was set at 40 mm/min. The
rectangular samples measured 50 mm by 300 mm and
were tested in the machine direction (MD).
3 | RESULTS A ND DISCUSSIONS
3.1 | Screening of parameters using
Plackett–Burman design
Model adequacy verification: By adopting PBD, we tried
to develop an efficient model for studying the factors that
have the most significant impact on FD Table 4. Additionally, the experimental design will enhance the efficiency of our industrial production by minimizing the
utilization of various resources,13 thereby leading to a
substantial reduction in costs associated with the process.
We began with evaluating and validating the model.
The correlation coefficients R-Squared represent the
most important index for showing the validity of the
model. The coefficient of determination R2 (0.99) indicates that the model explains most of the total variations, as only 1% of the variations remain unexplained.
The adjusted coefficient of determination R2adj is used to
evaluate the model's quality. R2adj is also high with a
value of 0.96, indicating that the model is significant. R2
and R2adj are both close to 1, therefore, the above
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ABDEL-MOUTTALIB ET AL.
F I G U R E 2 (A) Microscopic
views of the nonwoven fiber
diameters. (B) Distribution of
fiber diameters for the three
samples.
Samples
Fiber diameter (μm)
PFR (g/min)
QP (Pa)
VG (mm)
PP1
48.62
2.980
230
33
PP2
34.84
2.980
285
24
PP3
29.73
10.752
175
20.8182
considerations reveal that the calculated model fits well
with the experimental data.
The difference between R2adj and R2pred (R2pred
= 0.78) was 0.18, which was a good fit, showing that the
actual and predicted values have a high correlation
Table 5.
Analysis of variance (ANOVA) was also employed to
investigate the adequacy and significance of the model
T A B L E 3 Effect of PFR, QP, VG on
fiber diameter of Spunbond nonwoven
fabrics.
and the factors. The F-value of the model (37.06) and pvalue of the model is (0.027) conforming that the model
is significant with the 95% confidence interval.
In this study, factors with confidence intervals
greater than 95% or p-values less than 0.05 ( p < 0.05)
were identified as significant. According to the ANOVA
results in Table 7, the p-values for PFR, QP, and VG
were all below the 0.05, indicating that these three
ABDEL-MOUTTALIB ET AL.
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T A B L E 4 Results of
Plackett–Burman design.
TABLE 5
Exp.
X1
X2
X3
X4
X5
X6
X7
X8
X9
Y
1
113
212
2.980
8
57.06
2150
285
33
1600
32.0004
2
113
314
0.282
46
57.06
2150
175
33
1600
26.1114
3
16
314
2.980
8
175.56
2150
175
15
1600
37.1486
4
113
212
2.980
46
57.06
2650
175
15
1000
29.1452
5
113
314
0.282
46
175.56
2150
285
15
1000
6.2141
6
113
314
2.980
8
175.56
2650
175
33
1000
37.2584
7
16.3
314
2.980
46
57.06
2650
285
15
1600
21.1453
8
16.3
212
2.980
46
175.56
2150
285
33
1000
32.9874
9
16.3
212
0.282
46
175.56
2650
175
33
1600
32.2001
10
113
212
0.282
8
175.56
2650
285
15
1600
6.1784
11
16.3
314
0.282
8
57.06
2650
285
33
1000
13.9852
12
16.3
212
0.282
8
57.06
2150
175
15
1000
17.0802
Fit summary of Plackett–Burman design.
TABLE 7
Results of Box–Behnken design.
S
R2
R2(adj)
R2(pred)
Exp.
X3
X7
X8
Y
2.02867
99.40%
96.72%
78.54%
1
0.282
175
24
28.5289
2
2.980
175
24
45.6795
3
0.282
285
24
21.0123
4
2.980
285
24
36.1024
5
0.282
230
15
23.1002
6
2.980
230
15
37.2140
7
0.282
230
33
24.0122
T A B L E 6 Analysis of variance (ANOVA) of parameters in
Plackett–Burman.
Source
DF
Adj SS
Adj MS
F-Value
p-Value
Model
9
1372.67
152.519
37.06
0.027
Linear
9
1372.67
152.519
37.06
0.027
8
2.980
230
33
46.2103
PMI
1
25.93
25.928
6.30
0.129
9
1.631
175
15
30.5489
IPT
1
4.98
4.978
1.21
0.386
10
1.631
285
15
22.0125
PFR
1
644.10
644.100
156.51
0.006
11
1.631
175
33
40.1247
IAT
1
1.44
1.437
0.35
0.614
12
1.631
285
33
30.5793
IAV
1
13.06
13.061
3.17
0.217
13
1.631
230
24
31.2417
ASS
1
11.27
11.270
2.74
0.240
14
1.631
230
24
31.2417
QP
1
367.78
367.780
89.36
0.011
15
1.631
230
24
31.2417
VG
1
276.78
276.779
67.25
0.015
PCAS
1
27.34
27.342
6.64
0.123
Error
2
8.23
4.115
factors are the most critical influencing FD. The importance of PFR, QP, and VG can be understood by considering their physical implications. PFR directly
influences the amount of polymer extruded, thereby
affecting the thickness of the resulting fibers. QP, on the
other hand, controls the cooling rate during the quenching process; higher pressures lead to faster solidification,
which reduces the time available for fiber stretching,
resulting in finer diameters. VG determines how easily
the material can be drawn into thin filaments. These
insights coordinate with the statistical results observed
in the ANOVA results Table 6.
The Pareto chart in Figure 3 provides a visual confirmation of these findings. The chart ranks the importance
of factors based on their absolute effect size, with longer
bars indicating a greater influence on FD. The fact that
PFR, QP, and VG exceed the limit line on the Pareto
chart underscores their significant impact on FD.
Consequently, the three principal factors, namely
PFR, QP, and VG, have a clear and significant impact on
FD. It becomes evident that PFR has the most substantial
impact on the modeled response, followed by QP, with
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ABDEL-MOUTTALIB ET AL.
interactions should not be entirely dismissed and could be
explored in future studies. Their influnece is ranked as follows PCAS> PMI> IAV> ASS> IPT> IAT Figure 3. Overall, the results demonstrate the suitability and
effectiveness of the PBD in identifying key factors that
significantly influence FD.
3.2 | Parameter optimization using
Box–Behnken design
FIGURE 3
design.
Individual Pareto effect in Plackett–Burman
VG having the least influence among the three. The statistical model, represented by the response function, facilitates a detailed analysis of how each factor affects
FD. Equation (1) quantifies the contribution of each factor, with coefficient showing their impact on
FD. Precisely, a positive coefficient signifies a synergistic
effect, where an increase in the factor leads to an increase
in FD, whereas a negative coefficient implies an antagonistic effect, where an increase in the factor results in a
decrease in FD.
FD ¼ 231:28 0:0304 PMI 0:0126 IPT þ 5:431 PFR
þ 0:0128 IAT þ 0:01761 IAV 0:00388 ASS
0:1007 QP þ 0:5336 VG þ 0:00503 PCAS:
ð1Þ
From this analysis, we can draw the following
conclusions:
1. PFR and VG are directly proportional to FD, meaning
that a decrease in PFR or VG will result in a decrease
in FD, and vice versa. This direct relationship highlights the critical role of polymer flow and venturi gap
in determining fiber thickness.
2. QP is inversely proportional to FD, implying that as
QP increases, the FD decreases. This inverse relationship underscores the importance of quench pressure
in controlling FD through its effect on the cooling
rate and solidification process.
The initial screening strategy, employing PBD, identified
notable effects of three main factors PFR, QP, and
VG. Subsequently, these influential factors underwent
further statistical optimization through the utilization of
BBD. Table 7 illustrates the result of BBD experiments
with measured response.
The coefficient of determination (R2) and the adjusted
coefficient of determination (R2adj) were 0.98 and 0.96,
respectively, indicating that the calculated model fits well
with the experimental data. Regarding the difference
between R2adj and R2pred (0.80) was 0.18, which means
the fit is good and the actual and predicted values have a
high correlation Table 8.
The ANOVA results presented in Table 9 relate each
input process variable to FD. The model's high
significance is validated by an F-value of 45.29 and a low
p-value (p < 0.001), confirming that the model is statistically significant within a 95% confidence interval.
The obtained results show that all input variables and
their interactions were considered, the three factors PFR,
QP, and VG emerged with a p-value lower than 0.001
(p-value < 0.001). this significance indicates that any
change in these factors strongly influence FD. The interaction between PFR and VG proved to be significant with a
p-value of 0.039 below (p < 0.05). This suggests that the
combined effect of PFR VG is adding a complex interaction, which validates the quadratic model used in this study.
To quantify these relationships, we have generated a
regression model using the factors classified based on
their influence of the coefficient on FD, which can be
expressed by the following equation. The Equation (2)
represents the empirical relationships between the FD
and specific values of PFR, QP, and VG. It provides a
mathematical representation as well of how variations in
the three factors affect the final FD.
TABLE 8
The other six factors didn't show statistically significant
p-values. Thereby, they have a less pronounced impact on
FD under the conditions tested. However, their potential
Fit summary of Box–Behnken design.
S
R2
R2(adj)
R2(pred)
1.46078
98.79%
96.61%
80.61%
ABDEL-MOUTTALIB ET AL.
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FD ¼ 29:3 þ 0:90 PFR 0:039 QP þ 0:420 VG þ 0:936 PFR
PFR 0:000038 QP QP 0:00384 VG VG
0:00694 PFR QP þ 0:1665 PFR VG 0:00051 Q
VG:
ð2Þ
Figure 4 illustrates the relationship between the
experimental and predicted FD values, demonstrating a
high degree of correlation between them. The strong
alignment between predicted and actual data across all
experimental sets confirms the model's stability and accuracy. This correlation indicates that the model is not only
statistically valid but also practically reliable for predicting FD under various process conditions. This level of
accuracy and reliability is crucial for optimizing the
spunbond process, as it provides a solid foundation for a
potential scale-up to industrial applications. This underscores the robustness of the model and its capacity to
accurately represent the process dynamics.
3.3 | Influences of factors and their
interaction on FD
The effects of the factors on FD can be estimated using
the second-order polynomial Equation (2). The sign of
the coefficient indicates whether a factor has a synergistic
T A B L E 9 Analysis of variance
(ANOVA) of parameters in Bken.
FIGURE 4
values.
The observed values plotted against the predicted
TABLE 10
Optimal values of each factor.
Factors
Optimal values
PFR
1.0752 g/min
QP
175 Pa
VG
20.8182 mm
Source
DF
Adj SS
Adj MS
F-value
p-value
Model
9
869.719
96.635
45.29
0.000
Linear
3
840.453
280.151
131.29
0.000
PFR
1
587.432
587.432
275.29
0.000
QP
1
154.664
154.664
72.48
0.000
VG
1
98.357
98.357
46.09
0.001
Square
3
11.611
3.870
1.81
0.261
PFR PFR
1
10.714
10.714
5.02
0.075
QP QP
1
0.048
0.048
0.02
0.886
VG VG
1
0.357
0.357
0.17
0.699
2-Way Interaction
3
17.655
5.885
2.76
0.151
PFR QP
1
1.061
1.061
0.50
0.512
PFR VG
1
16.339
16.339
7.66
0.039
QP VG
1
0.255
0.255
0.12
0.744
5
10.669
2.134
3
10.669
3.556
*
*
2
0.000
0.000
14
880.388
Error
Lack-of-Fit
Pure Error
Total
The “*” appear because the Pure Error is zero, preventing the calculation of the Lack-of-Fit, F-value and Pvalue.
10 of 15
ABDEL-MOUTTALIB ET AL.
(positive sign) or antagonistic (negative sign) effect. In
our study, PFR and VG both have a synergistic effect on
FD, leading to an increase in FD, whereas QP has an
antagonistic effect, resulting in a decrease in FD. This
behavior is consistent with previous findings as demonstrated by the PBD. The Pareto chart in Figure 5 highlights the significance of the main parameters. At lower
PFR values, the filaments tend to be finer, as the reduced
polymer flow facilitates the stretching. However, as PFR
FIGURE 5
Individual Pareto effect in Box–Behnken design.
FIGURE 6
Surface plot for fiber diameter.
increases, the filament stretching becomes more challenging, leading to thicker fibers. This is because a higher
PFR delivers more polymer to the spinneret, which
requires more energy and time to cool down and solidify
the filaments. This explains the directly proportional relationship between PFR and FD. Conversely, an increase in
QP tends to produce finer fibers, as the air pressure
in the quenching zone accelerates the cooling rate of the
filaments, leading to rapid solidification and a reduction
in FD, as PP is a highly-crystalline polymer.26
Therefore, QP presents an inversely proportional effect
on FD. The VG, representing the distance between the spinneret and the collector, also plays a critical role in determining the thickness of the FD. A smaller VG means that the
filaments undergo greater stretching due to the increased
draw-down force, leading to finer fibers. Consequently, VG,
like PFR, has a directly proportional effect on FD. Among
the factors studied, PFR is the most influential factor affecting FD. However, its effect cannot be considered alone, as it
works together with QP and VG to obtain the desired FD.
The contour plot and surface response plot, generated
using MINITAB 18, are clearly illustrating these relationships in Figures 6 and 7. FD increases linearly with PFR
and VG, while it decreases as QP increases slightly. Additionally, the interaction between PFR and QP has a
minor effect on FD, whereas the interaction between QP
and VG appears to have negligible influence on FD.
ABDEL-MOUTTALIB ET AL.
FIGURE 7
Contour plots of fiber diameter.
FIGURE 8
optimizer.
Response
The observed results align well with the
literature10,22,13,23 affirming the reliability of the findings.
This comprehensive analysis underscores the importance of optimizing the nonwoven production process.
For instance, adjusting PFR, QP, and VG allows a precise control over FD to achieve the desired nonwoven
fabric, thereby validating the practical implications of
the study.
3.4 | Optimal conditions for FD
The application of Minitab 18 as a statistical tool for optimizing the processing parameters proved effective. The
11 of 15
optimization plot generated by the software enabled
the precise determination of optimum values of the three
factors. These values were subsequently validated
through experimental testing, where the results showed
an average FD of 31.0 μm, a value that aligns with the
predicted outcomes Figure 8.
The critical factors were optimized at 1.08 g/min for
PFR, while QP was minimized at 175 Pa, and VG was set
20.82 mm Table 10. These settings were identified as the
most influential in achieving that final FD, as illustrated
in Figure 8.
Additionally, while PFR, QP, and VG were identified
as the primary factors, the optimization of the remaining
six parameters was also crucial. Although these factors
12 of 15
ABDEL-MOUTTALIB ET AL.
TABLE 11
FIGURE 9
fabrics.
Stress–strain curves of spunbond nonwoven
had a less significant impact on FD, their values as experimentally determined (PMI at 54.4 g/10 min, IPT at
230 C, IAT at 16 C, IAV at 115.84 m/s, ASS at 2378 rpm
and PCAS at 1295 rpm) Table 11.
In summary, it has been demonstrated that thin filaments outperform thicker diameter filaments in terms of
morphology, as well as physical and mechanical properties.15 Therefore, it is important to consider the description of the positive or negative effects of these nine
factors on the FD quality.
Ultimately, the results underscore the importance of
focusing on PFR, QP, and VG for FD optimization while
maintaining control over the other six factors to ensure
the quality of the final product. Furthermore, it was demonstrated that thinner filaments, produced under the
optimized conditions, show superior morphological,
physical, and mechanical properties compared to thicker
filaments.15 This finding highlights the significance of
effectively adjusting the process parameters to enhance
high quality nonwoven fabric with desirable properties.
3.5 | Influence of FD on mechanical
properties
Validating all the statistical results at an industrial scale
is undeniably intricate, yet crucial for ensuring the alignment of statistical data with the reality of the spunbond
process.
Strain at break, tensile strength and young's modulus
were measured in order to examine the effect of FD on
tensile properties of three samples of spunbond nonwoven fabrics.
Figure 9 illustrates the stress–strain behavior of spunbond nonwoven fabrics. The first sample PP1 shows the
Optimal values of the six factors.
Factors
Values
PMI
54.4 g/10 min
IPT
230 C
IAT
16 C
IAV
115.84 m/s
ASS
2378 rpm
PCAS
1295 rpm
highest stress value before failure, this sample resist more
load compared with the others. Additionally, it represents
a higher strain at break, as demonstrated by its greater
elongation before breaking. PP2 has intermediate properties, with lower maximum stress compared with PP1 but
higher than PP3, and with an intermediate strain at
break. PP3 shows the lowest maximum stress and strain
at break, highlighting its minimal strength and elongation capacity among the three samples.
Figure 10A,B illustrates respectively the tensile
strength and Young's modulus of spunbond nonwoven
fabrics. The first sample PP1, illustrates the highest tensile strength, demonstrating its ability to endure the most
significant amount of stress before failure, and shows the
highest Young's modulus, indicating that PP1 is the stiffest fabric and deforms the less under stress. The PP2 sample, while having slightly lower tensile strength and
Young's modulus compared with PP1, still maintains substantial load-bearing capacity and stiffness, remaining
significantly stiffer than PP1. In contrast, the PP3 sample
presents the lowest tensile strength and Young's modulus, making it the least capable of resisting high stress
and the most flexible among the three.
As seen from Figures 9 and 10 and Table 12, changes
in FDs have significant effects on mechanical properties
of spunbond nonwoven fabrics. According to Choi et al.
the mechanical properties of melt blown fabrics, which
are nearly produced the same as spunbond fabrics27 are
highly influenced by variations in FD.28 The FD has a
direct impact on individual strength,29 which in turn
influences the overall strength of the nonwoven fabric.
Consequently, As the FDs increase, the mechanical properties of the nonwoven fabric are increased. The ability to
control FD, combined with the structural advantage of
randomly oriented fibers,27 makes spunbond nonwovens
highly suitable for various applications requiring reliable
performance under various stress conditions. Fabrics
with larger FDs like PP1 and PP2 lead to fabrics with
higher tensile strength, greater stiffness (Young's modulus), and better overall load-bearing capacity. These properties make these nonwoven fabrics suitable for
applications
requiring
enhanced
mechanical
ABDEL-MOUTTALIB ET AL.
13 of 15
FIGURE 10
(A) Tensile strength of spunbond nonwoven fabrics, and (B) Young Modulus of spunbond nonwoven fabric.
TABLE 12
Physical and mechanical properties of spunbond nonwoven fabrics.
Samples
Linear
density (Tex)
Fiber
diameter (μm)
Tensile strength in machine
direction (MPa)
Young's
Modulus (GPa)
Breaking
extension (%)
PP1
7156.86
48.92
1446.06
39.87
105.39
PP2
4480.80
34.84
1368.47
34.86
105.50
PP3
4200
29.73
1149.80
28.28
71.05
performance like automotive (seat covers), civil engineering (roofing, erosion control), geotextiles …27 Conversely,
fabrics with smaller FDs like PP3 possess lower tensile
strength and stiffness but provide greater flexibility,
which might be advantageous for applications requiring
higher flexibility. Such as filtration applications,30 since
the decreased FD increase the surface area which
enhance the filter quality. They can be used as well for
thermal insulation applications and medical.31 Thinner
fibers also used to produce nonwovens with high packing
density.32
Since the FD is the important feature that condition
other properties,31 these findings highlight the importance of selecting correct FDs during the manufacturing
process of spunbond nonwoven fabrics to achieve the
appropriate mechanical properties for specific applications. By optimizing FD, manufacturers can adapt the
performance characteristics of their products to meet
diverse industry requirements.
4 | C ON C L U S I ON
In this study, the effects of the independent variables
were investigated using a combination of PBD and BBD
to model and optimize them, to enhance and ensure
resource-efficient production by evaluating their effects
on FD. This study provided a statistical model for predicting the effects of nine variables (PMI, IPT, PFR, IAT,
IAV, ASS, QP, VG, and PCAS) on FD. First, the factors
underwent a rigorous process of verification, validation
and have been screened to obtain the most significant
one, which are PFR, QP, and VG. Subsequently, the optimal values for these influential factors were determined
to achieve the optimal FD.
The experimental data adjustment to both designs led
to the creation of a reliable regression model, enabling
the evaluation of the influence of each factor and their
interactions on the measured response FD. According to
PBD, the FD is primarily influenced by PFI, QP, and VG
as well as by the interaction effect PFR VG according
to BBD. The predictions of the statistical model closely
align with the experimental values, showing minimal
deviations between the predicted and measured data.
Based on the regression model, it was possible to
highlight the cause-and-effect relationships between the
factors and the final response. PFR and VG were found
to have a directly proportional influence on FD, meaning
that when these factors decrease, FD also decreases. On
the other hand, QP showed an inversely proportional
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influence on FD, indicating that as QP increase, FD
decreases.
The optimal FD value is 30.9973 μm corresponds to
the following values of the factors: PMI (54.4 g/10 min),
IPAT (230 C), PFR (1.0752 g/min), IAT (16 C), IAV
(115.84 m/s), ASS (2378 rpm), QP (175 PA), VG
(20,8182 mm), and PCAS (1295 rpm).
Furthermore, the FD revealed its impact on the
mechanical properties, offering insights into the
mechanical behavior of the spunbond nonwoven fabrics. Thinner fibers as PP3 with a FD of 29.73 μm typically result in more flexible and softer nonwoven
fabrics, which are advantageous for applications such
as filtration textiles and medical products. Conversely,
thicker fibers such as PP1 (48.92 μm) and PP2
(34.84 μm) contribute to enhanced durability and are
thus preferred in applications requiring robust
mechanical strength, such as geotextiles and automotive substrates.
In conclusion, validating statistical results at an
industrial scale is undeniably intricate, yet crucial for
ensuring the alignment of statistical data with the reality
of the Spunbond process. To accomplish this, we are
embarking on a validation approach rooted in physical
principles, which promises to enhance the reliability of
our analytical findings.
Our strategy involves establishing a connection
between statistical results and the underlying physical
laws governing the dynamics of the Spunbond process,
with a focus on refining the model's precision. This
approach encompasses ongoing laboratory experiments,
empirical data collection, and the implementation of simulations to represent process behavior more accurately.
We are actively collaborating with esteemed experts in
the field to achieve this precision.
In parallel, we are actively developing a neural network model with the goal of highly accurately predicting FD, harnessing the potential of machine learning
to discern complex data patterns. Simultaneously,
we are initiating other studies aimed at optimizing
Spunbond process parameters, contributing to improved process performance as part of a shift toward
more ecologically and socio-economically sustainable
practices.
A C K N O WL E D G M E N T S
The authors would like to thank the supervisory Board of
Higher School of Textile and Clothing Industries (ESITH)
for technical assistance and support and Chouaib Doukkali University.
CONFLICT OF INTEREST STATEMENT
The authors declare no conflicts of interest.
ABDEL-MOUTTALIB ET AL.
DA TA AVAI LA BI LI TY S T ATE ME NT
The authors confirm that all data generated or used during the study are available in the article.
ORCID
Kaoutar Abdel-Mouttalib https://orcid.org/0009-00035260-8069
Ayoub Nadi https://orcid.org/0000-0001-9829-8359
Samir Tetouani https://orcid.org/0000-0003-3493-3426
Abdelowahed Hajjaji https://orcid.org/0000-0002-48634753
Omar Cherkaoui https://orcid.org/0000-0002-67116433
Samira Touhtouh https://orcid.org/0000-0002-64466980
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How to cite this article: Abdel-Mouttalib K,
Nadi A, Tetouani S, Hajjaji A, Cherkaoui O,
Touhtouh S. Experimental analysis on fiber
diameter of spunbond nonwoven fabrics through
Plackett–Burman and Box–Behnken designs and
its impact on mechanical properties. SPE Polym.
2025;6(1):e10163. doi:10.1002/pls2.10163