| 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 labour, 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 |
