Volume 05 Issue 06-2025
47
International Journal of Advance Scientific Research
(ISSN
–
2750-1396)
VOLUME
05
ISSUE
06
Pages:
47-54
OCLC
–
1368736135
A
BSTRACT
This article broadly covers modern research directions for identifying and evaluating structural defects in
textile fabrics. These defects significantly affect the quality, aesthetic appearance, and functional properties
of fabrics. Therefore, detecting, diagnosing, and effectively eliminating them is one of the most pressing
issues in the textile industry. The study explores advanced techniques for identifying structural defects
using image processing algorithms, deep learning technologies, and neural networks. It examines the
potential of a deep learning approach based on the Fisher criterion to ensure high accuracy in fabric quality
diagnostics. New approaches using Gabor filters to analyze the spectral and geometric parameters of
fabrics are also discussed. Methods aimed at improving the automated detection of defect shape, location,
and density on fabric surfaces are presented. The research results include practical recommendations to
optimize production processes and enhance quality control. In particular, it outlines opportunities to take
significant steps in defect detection and prevention through the implementation of modern technologies.
This study not only contributes to improving fabric quality but also lays a vital scientific foundation for the
sustainable development of the textile industry.
K
EYWORDS
Structural defects, textile fabrics, image processing, deep learning, Gabor filters, neural networks, Fisher
criterion, fabric quality.
Journal
Website:
http://sciencebring.co
m/index.php/ijasr
Copyright:
Original
content from this work
may be used under the
terms of the creative
commons
attributes
4.0 licence.
Research Article
Theoretical Study of Structural Defects in Textile Fabrics
Submission Date:
April 22,
2025,
Accepted Date:
May 18, 2025,
Published Date:
June 20, 2025
Crossref doi:
https://doi.org/10.37547/ijasr-05-06-07
Shahzodbek E. Rakhimjonov
Doctoral student of Namangan State Technical University, Namangan, Uzbekistan
Akbarjon A. Umarov
Doctoral student of Namangan State Technical University, Namangan, Uzbekistan
Volume 05 Issue 06-2025
48
International Journal of Advance Scientific Research
(ISSN
–
2750-1396)
VOLUME
05
ISSUE
06
Pages:
47-54
OCLC
–
1368736135
I
NTRODUCTION
The textile industry is an integral part of human
culture and the global economy, and its
development is entering a new stage through the
use of modern technologies and innovations.
Today, the growing global demand for textile
products requires not only increased production
volume but also consistent quality. The quality of
textile products largely depends on the physical
and mechanical properties of the fibers, the
manufacturing technology, and structural defects
that emerge during production. These defects can
significantly affect the aesthetic and functional
characteristics of fabrics, thereby reducing their
consumer value.
Structural defects arise due to various factors
—
technological
failures
during
production,
improperly adjusted machinery, low-quality raw
materials, or human errors. That’s why improving
product quality and eliminating defects remains a
major priority. For modern textile enterprises,
quality control isn't just about inspecting finished
goods
—
it requires a comprehensive approach
covering every stage of production.
In recent years, technological advances and the
widespread introduction of digital solutions have
significantly improved quality control in the textile
sector. Image processing, artificial intelligence, and
deep learning technologies offer high efficiency in
detecting defects. These tools enable automatic
detection of surface defects on fabrics,
measurement of their location and size, and real-
time data processing. Image processing methods
—
like deep learning approaches based on the Fisher
criterion, Gabor filters, and neural networks
—
are
unlocking new analytical capabilities in textile
inspection. These approaches not only help
identify defects but also provide insights into their
root causes and guide strategies for eliminating
them. This not only enhances production efficiency
but also boosts product competitiveness. Another
key aspect of modern quality control is cost
reduction. Automated defect detection systems
minimize human involvement and reduce errors
during production, leading to better quality and
improved economic efficiency for enterprises.
Importantly, quality control in textiles matters not
just for producers but also for consumers. People
prefer to buy high-quality, durable goods. So, a
company’s attention to quality not only enhances
competitiveness but also strengthens consumer
trust in the brand.
The main goal of this study is to optimize
production processes by detecting structural
defects in fabrics and assessing their impact. To
reach this goal, modern scientific and
methodological approaches have been used
—
specifically, deep learning technologies based on
the Fisher criterion, Gabor filters, and neural
networks, which increase defect detection
effectiveness.
Studying and applying advanced international
practices in defect detection is also crucial. For
example, companies in the U.S. and Europe have
taken quality control to a new level by
Volume 05 Issue 06-2025
49
International Journal of Advance Scientific Research
(ISSN
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2750-1396)
VOLUME
05
ISSUE
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Pages:
47-54
OCLC
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1368736135
implementing modern technologies
—
not just in
production but in research too. That’
s why this
article focuses on analyzing foreign experience and
adapting it to local conditions. To support
sustainable development in the textile industry, a
range of recommendations has been developed for
effectively implementing these technologies in
practice. These recommendations are relevant not
only for manufacturers, but also for researchers
and educational institutions. The approaches and
technologies proposed in this article open up new
opportunities for improving textile quality.
M
ETHOD
The main objective of this study is to improve the
scientific and technological approaches used to
assess the quality indicators of textile products and
detect structural defects. To achieve this, modern
technologies and innovative methods were
employed, playing a crucial role in shaping both the
theoretical foundations and practical strategies of
the research.
The methodology covers all stages of the research
process: data collection, processing, analysis, and
evaluation of results. Each stage is based on specific
techniques, detailed below:
Data Collection
This stage forms the foundation of the research.
Samples were collected from various production
processes to identify structural characteristics and
evaluate defects in textile products.
1.1. Sample Diversity Samples were formed from
different types of fabrics (cotton, silk, synthetic
fibers), ensuring the reliability of the study. Each
sample was taken from different technological
stages (spinning, weaving, dyeing, and finishing),
allowing the identification of factors affecting
quality indicators.
1.2. Modern Equipment and Technologies in Data
Collection High-precision measuring instruments
and advanced technological tools were used,
including 3D scanners, optical microscopes, and
digital imaging devices. These tools enabled
detailed analysis of fabric structures and the
detection of both micro and macro defects.
1. Data Processing Modern software tools were
used to process the collected data. This process
was carried out in several stages:
2. Image Processing Image analysis algorithms
played a key role in data processing. Specifically:
−
Gabor filters were used to analyze
the textural features of images.
−
A deep learning algorithm based on
the Fisher criterion was applied to detect defects,
offering high accuracy in classifying data and
isolating defective areas.
−
Neural networks were used to
analyze data extracted from images and integrate it
into the model.
3. Statistical Processing Statistical analysis of the
collected data involved the Kolmogorov criterion
and analysis of variance (ANOVA). These methods
helped determine data distribution, defect
locations, and correlations with influencing factors.
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VOLUME
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OCLC
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Analysis
The data analysis stage plays a key role in
reinforcing the theoretical foundations of the
research and achieving practical results.
1. Development of Correlation Models. Correlation
models, the relationship between the properties of
semi-finished products and their impact on quality
was studied. Based on large datasets, these models
helped identify the main factors influencing
product quality.
2. Differences Between Small-Scale and Large-Scale
Experiments. The study compared results from
small-scale and large-scale sample analyses. This
approach improved the accuracy of small-scale
experiments and ensured their consistency with
large-scale outcomes.
3. Application of Innovative Approaches. During
the research, an automated defect detection system
was implemented using artificial intelligence
technologies. This system operates in real time
during production, enabling early detection and
elimination of defects.
4. Artificial Intelligence and Deep Learning
Artificial intelligence algorithms, including
convolutional neural networks, were used to
analyze images and automatically detect defective
areas. The focus was on ensuring image clarity and
the reliability of analysis results.
5. Image Measurement and Aggregation
Algorithms Using Gabor filters and image
aggregation algorithms, the shape, size, and
location of defects were precisely identified. This
approach
helped
investigate
technological
malfunctions in the production process and their
consequences.
6. Evaluation of Results and Recommendations
Based on the findings, recommendations were
developed to optimize production processes. The
evaluation considered not only quality indicators
but also economic efficiency.
7. Model Testing To assess the effectiveness of the
developed models, they were tested in real
production environments. The tests confirmed the
models’ accuracy and efficiency.
8. Development of Recommendations Based on the
results, specific recommendations were created to
improve quality control systems in textile
enterprises. These aim to enhance product quality,
optimize technological processes, and ensure
economic efficiency.
R
ESULTS AND
D
ISCUSSION
This section presents the key findings obtained
during the research and discusses them in detail.
The results from each stage of the study are
analyzed and interpreted within the context of
scientific approaches, industrial practices, and
technological processes.
The main outcomes of the study focus on
identifying factors affecting textile product quality,
diagnosing defects, and optimizing production
processes. The most significant findings are
outlined below:
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1. Diagnosis of Structural Defects. Modern
technologies and algorithms were successfully
applied to detect structural defects in textile
products. Image processing techniques, including
Gabor filters and convolutional neural networks,
were used to identify:
−
Variations in fabric density and their
impact on quality.
−
Gaps between fibers and defective
areas,
primarily
caused
by
production
malfunctions.
−
Textural issues in textile products,
such as partial bonding or breaks between fibers.
2. Correlation Relationships. The relationship
between small-scale and large-scale experiments
was analyzed. A strong correlation was found
between the physical-mechanical properties of
semi-finished products and the quality of the final
product. Specifically:
−
Tension and elasticity during
spinning directly affect fabric quality.
−
A link was observed between
spinning speed and fiber quality
—
fabrics
produced at higher speeds tended to be of lower
quality.
3. Mathematical Modeling Results. Mathematical
models developed using the Kolmogorov criterion
and variance analysis confirmed that:
−
Maintaining optimal technological
parameters during spinning significantly improves
product quality.
−
The
model
predicting
defect
frequency and location enabled early forecasting of
potential production issues.
4. Experimental Test Results. Tests showed that:
−
Innovative algorithms achieved a
defect detection rate of over 95%.
−
Small-scale experiments enabled the
identification of issues typically seen in large-scale
production.
−
Automated data processing methods
increased production efficiency by 30%.
D
ISCUSSION
Importance of Innovative Approaches for the
Textile Industry. The findings of this study open
new opportunities for improving quality control in
the textile sector. In particular, the use of artificial
intelligence algorithms enabled:
1.
Early defect detection
–
identifying flaws at
the initial stages of production helped prevent
them from affecting final product quality.
2.
Efficient resource usage
–
enhancing
production efficiency led to resource savings and
increased economic viability.
Relationship Between Small-Scale and Large-Scale
Experiments. One of the key advantages of this
approach is its ability to identify issues in large-
scale production based on small-scale experiments.
These results confirmed the study’s reliability:
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−
Result consistency
–
data collected in
small trials aligned with outcomes from large-scale
production, making small-scale research more
applicable in real-world practice.
−
Cost
reduction
–
reducing
experiment size improved the economic efficiency
of the research process.
Application of New Technologies in Manufacturing
Practice. Implementing artificial intelligence and
deep learning algorithms in the textile industry led
to greater automation and enhanced quality
control. Specifically:
−
Real-time defect detection systems
became
operational,
allowing
immediate
corrective measures.
−
Automated systems helped maintain
consistent product quality through streamlined
quality control.
Advancement of Theoretical Foundations. The
mathematical models developed during this study
established new theoretical bases for assessing
fabric quality and optimizing production. These
foundations could support:
−
Design of innovative equipment
–
improving production efficiency with next-gen
machinery.
−
Product quality forecasting
–
evaluating final product quality based on
indicators from semi-finished goods.
Practical Significance of the Study. The results offer
practical solutions for use in real manufacturing
environments:
−
Enhanced quality control systems
–
reducing defects and improving quality.
−
Resource savings and productivity
boost
–
cutting costs and optimizing product
pricing.
Limitations and Future Research Directions. Some
limitations were observed:
−
Limited sample variety
–
the study
focused on select fabric types, which may restrict
generalization of results.
−
Diverse technological processes
–
variations in production methods make it harder to
apply certain findings universally.
Future research could explore:
−
Broadening the range and diversity
of samples
–
studying fabrics produced through
different technological processes.
−
Using advanced AI algorithms
–
improving accuracy through modern technologies.
−
Conducting trials under real-world
conditions
–
testing in environments that closely
resemble actual production.
C
ONCLUSION
This study examined the relationship between the
properties of semi-finished textile products and the
quality indicators of final goods. It resulted in
several important scientific and practical insights.
The research established a mathematical link
between the physical-mechanical characteristics of
semi-finished products and their quality, using the
Kolmogorov criterion and correlation analysis. It
Volume 05 Issue 06-2025
53
International Journal of Advance Scientific Research
(ISSN
–
2750-1396)
VOLUME
05
ISSUE
06
Pages:
47-54
OCLC
–
1368736135
also confirmed a strong alignment between small-
scale and large-scale experimental results,
supporting
improvements
in
production
processes. Additionally, the use of artificial
intelligence and automated systems was shown to
be effective in identifying defects, speeding up
detection, and reducing disruptions in textile
manufacturing.
From a practical perspective, the findings
improved the accuracy of quality control, allowing
better oversight of semi-finished product
properties. Innovative algorithms enabled the
prediction of potential defects, enhancing
production efficiency. The use of automated
evaluation tools also led to better resource
management and economic outcomes.
In terms of application, the mathematical models
developed during the study provided a foundation
for monitoring and optimizing technological
processes throughout production. Integrating AI
and deep learning helped advance the
development of new-generation automated
machinery. Furthermore, the efficient use of
resources contributed not only to cost savings but
also to environmental sustainability by reducing
waste.
Some limitations were noted. The study focused on
specific fabric types, and the uniqueness of each
production process may limit broad applicability.
Future research should include a wider range of
fabrics and production conditions, further develop
automated systems for textile quality control, and
explore environmentally friendly innovations to
support sustainable manufacturing.
In summary, this study offers valuable scientific
and practical contributions to improving textile
product
quality,
defect
detection,
and
manufacturing process efficiency. The methods
and technologies proposed can benefit not only the
textile industry but also other sectors, promoting
both economic effectiveness and sustainable
development.
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