| dc.description.abstract | Nowadays, garment manufacturing companies face increased worldwide competitiveness
and unpredictable demand variations. These demands push companies to continually
enhance the effectiveness of their manufacturing processes to provide the final product
in the shortest possible time and at the lowest possible cost. Traditional manual
approaches, on the other hand, confront limitations in terms of subjectivity, time limits,
and scalability, driving the study to propose ideal AI-based methods for garment quality
inspection. This systematic study looks into the integration of artificial intelligence (AI)
technologies such as Convolutional Neural Networks (CNNs), Artificial Neural Networks
(ANNs), and many more AI technologies for quality control and defect detection in
the clothing industry’s sewing segment. This focuses on innovations such as CNNs
for identifying damaged stitches and the influence of ANN on the fashion supply
chain. Future work recommendations include broadening AI-powered defect detection,
incorporating AI into Industry 4.0, resolving ethical problems, and developing adaptive
AI systems to handle dynamic changes in garment patterns. Overall, this analysis sheds
light on the revolutionary potential of CNNs and ANNs in improving quality control in
the clothing industry’s sewing division. | en_US |
