Predicting knitted fabric properties and dimensional properties was a long-felt need for many years and a considerable amount of work was carried out using some simple, complex mathematical and empirical models. Fabric width of single jersey finished cotton knitted fabric is an important parameter that affects its comfort properties. This is also an important problem related to manufacturing costs. The most important problem in the knitting industry is attaining optimum fabric width during the garment manufacturing process. The textile industry needs to find and fix a flawless model to predict the best possible fabric width accurately to overcome the unwanted textile wastage and provide the most advantageous technology in knitted fabric manufacturing. This paper is a review that survey recent technologies applied for prediction models to attain optimum fabric width of single jersey finished cotton knitted fabric. This paper mainly focused on providing a brief review on the techniques such as feature selection using rough set theory, Data Mining (DM) techniques and managing of big data with Hadoop MapReduce framework. It summarizes the previous research works carried out on the above mentioned techniques. Finally, it enlists the outcomes of the survey and gaps in this research work.
Fabric width, rough set theory, data mining techniques, managing of big data
I Bhuvaneshwarri , A Tamilarasi "Appropriate Software Techniques for Prediction Model to Attain Optimum Fabric Width of Single Jersey Finished Cotton Knitted Fabric in Textile Knitting Industries: A Detailed Survey" Iconic Research And Engineering Journals Volume 2 Issue 1 2018 Page 47-51
I Bhuvaneshwarri , A Tamilarasi "Appropriate Software Techniques for Prediction Model to Attain Optimum Fabric Width of Single Jersey Finished Cotton Knitted Fabric in Textile Knitting Industries: A Detailed Survey" Iconic Research And Engineering Journals, 2(1)