| dc.description.abstract | Sri Lanka's garment industry is crucial,
contributing significantly to the country's export market.
However, current fabric handling methods in Sri Lankan
companies are primarily reliant on manual labor, creating
a compelling potential for research and development in the
field of automated fabric handling. Fabrics present distinct
challenges due to their dynamic and static character,
needing novel solutions to overcome these limitations.
Furthermore, human fabric problem detection achieves
just 60% accuracy, emphasizing the importance of
automation in this vital sector. Significant benefits can be
obtained by automating these processes in textile
manufacturers.The fundamental goal of this project is to
design and build an innovative system capable of
automatically separating and classifying cloth cut pieces
based on the presence of defects. Our suggested device
includes a cylindrical manipulator outfitted with cutting edge pinch-like grippers designed exclusively for effective
ply separation. To improve defect detection accuracy, we
use a custom-trained convolutional neural network (CNN)
with a validation accuracy of 80%. We have also created a
simple platform for remote control and real-time
monitoring of the entire system by using IoT
technology.This complete project not only meets the
critical demand for fabric handling automation, but it also
has the potential to change the garment manufacturing
process in Sri Lanka. | en_US |
