Open Access Open Access  Restricted Access Subscription or Fee Access

Study on Tools and Techniques to Manage the Textile Quality Control

M. Selvanayaki, T. S. Anushya Devi

Abstract


In recent years, the rapid development in IT applications like MIS, ERP, Network, Multimedia and Data Mining etc. are indispensable tools to boost productivity and drive maximum benefits that has ushered in a revolution in manufacturing and interactive marketing across the globe. The aim of this study is to elaborate how the textile industry can manage to improve their production capacity and resources to increase customer demands regarding individualized products with good quality using data mining tools and techniques. Data mining analysis offers many potential to improve the Quality control in manufacturing process and to enhance the usefulness of existing data.


Keywords


Data Mining, Quality Control, Manufacturing System, Data Warehousing

Full Text:

PDF

References


Alzghoul, A., Löfstrand, M.,” Increasing availability of industrial systems through data stream mining”, Computers & Industrial Engineering (2010).

M.S. Choudhury, S. Shah, N. Thornhill, D.S. Shook, Automatic detection and quantification of stiction in control valves, Control Engineering Practice 14 (12) (2006) 1395–1412.

N.F. Thornhill, A. Horch, Advances and new directions in plant-wide disturbance detection and diagnosis, Control Engineering Practice 15 (10) (2007) 1196–1206.

Prof. S.K. Tyagi Dr. B. K. Sharma Data Mining Tools and Techniques to Manage the Textile Quality Control Data for Strategic Decision Making International Journal of Computer Applications (0975 – 8887) Volume 13– No.4, January 2011.

Parmeet Kaur and Parmjeet Kaur An overview of data mining tools. International Journal of Engineering Applied Sciences and Technology, 2016.

Pimwadee chaovalit, National Science and Technology Development AgencyAryya gangopadhyay, george karabatis, and zhiyuan chen, University of Maryland, Baltimore County Time Discrete Wavelet Transform-Based Time Series Analysis and Mining.

In Lee Artificial Intelligence Search Methods for Multi-Machine Two-Stage Scheduling.


Refbacks

  • There are currently no refbacks.