Volume 14, Issue 16 (Forth Special Issue 2015)
Modares Mechanical Engineering 2015, 14(16): 339-348 |
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Moavenian M, Pazhoohiyani M, Momeni Heravi M E. Identification of broken needle in single jersey circular knitting machine using neural network on yarn fluctuations signals. Modares Mechanical Engineering 2015; 14 (16) :339-348
URL: http://mme.modares.ac.ir/article-15-12263-en.html
URL: http://mme.modares.ac.ir/article-15-12263-en.html
1- Academic member of Ferdowsi University of Mashhad Mech Eng. FDI
2- Msc Student, Mech. Eng., Ferdowsi University Of Mashhad, Mashhad, Iran.
3- Academic member of Islamic Azad University, Mashhad Branch.
2- Msc Student, Mech. Eng., Ferdowsi University Of Mashhad, Mashhad, Iran.
3- Academic member of Islamic Azad University, Mashhad Branch.
Abstract: (5810 Views)
The quality of knitted fabric in circular knitting machines is highly sensitive to any undesired changes in the mechanism and components involved. For instance, a broken needle causes defects on the surface of knitted fabric. Consequently in order to increase the quality and reduce production cost, rapid detection and diagnosis of defected needles on industrial circular weft knitting machines is a crucial need. In these machines when the yarn is pulled down by the needles to knit a loop the created yarn tension, causes fluctuations in the feeding yarn flow. The aim of present research is to identify broken needle defects and their numbers, during yarn feeding in a circular knitting machine, employing neural network analysis on yarn fluctuation signals. The experiments procedures were designed so that three needle defected conditions were implemented on an industrial circular knitting machine. The yarn fluctuation signals were captured and saved, then using wavelet the contaminated signal noise was removed. Statistical and wavelet analysis are implemented to produce the required features. Finally the capability of neuro network for classification of four groups of data including healthy, one, two and four broken needles were examined. The results show that 99.43 % accurate distinction of broken needles is achieved in 50 iterations.
Article Type: Research Article |
Subject:
Instrumentation|Automation
Received: 2014/08/14 | Accepted: 2014/10/2 | Published: 2014/11/15
Received: 2014/08/14 | Accepted: 2014/10/2 | Published: 2014/11/15
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