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  • KnitVision
    Intelligent and Real-time Detection
    of Fabric Defects

Description

 

Reducing waste is always one of the major concerns in every phase of fabric manufacturing including the knitting process. Smart and online detection of fabric defects in early phases is highly valuable to increase production quality. In addition, to saving production costs, it also reduces the loss of raw materials and energy and consequently makes less damage to natural resources and the environment.

KnitVision Device

Intelligent and Real-Time Detection of Fabric Defects

EAGLE EYE ON KNITTING

 

How it works

 

Joula KnitVision consists of two main parts; the imaging unit and the touchscreen control panel. The imaging unit takes continuous online fabric images and transfers them to the control panel. In case of any defect in the produced fabric, the control panel detects that and stops the machine. If the machine is also equipped with Joula KnitNet, all defects are also recorded in the KnitNet database for each roll which then can be used for quality control reports. KnitVision categorizes defects as vertical, horizontal, and randomly shaped. Any needle or sinker fault usually results in a vertical defect, Spandex or yarn mismatch causes a horizontal defect, and any other mechanical or operational errors can make a hole in different shapes and sizes. Also, the user can set the sensitivity for each kind of defect, and the KnitVision will smartly complete the subsequent settings that are required.

Catalogue

Read the Catalogue for more information

Introduction to KnitVision

 

product introduction: video teaser of KnitVision

Advantages

 

Precise fabric faults detection

Can be used for colored or dark fabrics

High accuracy in fault detection

Automated fabric quality control report by connection to the Joula KnitNet

Quick return on investment due to noticeable prevention of raw material waste

Easy install, easy use based on spider fixture and illustrative interface

Great customer support by Joula’s professional team