In this research, artificial neural networks, one of the most common branches of artificial
intelligence, were used to evaluate the diameter of the cotton yarn. The necessary data
were collected and practical tests were carried out. Then, an algori
thm for the artificial
neural network was established, which provides the possibility of determining the yarn
diameter from the input variables, represented by the count and twist yarn. Where after
the creation of many networks, one was selected which gave the lowest error rate.
The majority of known polymer materials are electrically insulating.
But there are some of polymers are intrinsically conducting in
nature, However, it is lacking in process ability, and the scope for
manipulation of electrical and mechanical prop
erties is limited. The
method which used to solve this problem is an addition of a
conductive metal filament into a polymer filament yarn. Which can
be directly integrated into a textile, or can be knitted. So we made
a device able to produce this kind of conductive yarns which
depends on melt spinning technique but with the elimination of
necessary pressure for extrusion. And we were able to produce
samples of copper filament coated with polymeric material (low
density polyethylene).
