Machine-Part cell formation through visual decipherable clustering of Self Organizing Map

Details Machine-Part cell formation through visual decipherable clustering of Self Organizing Map Machine-Part cell formation through visual decipherable clustering of Self Organizing Map

2011-05-06

arxiv.org/abs/1105.1247v1

The International Journal of Advanced Manufacturing Technology, 2011, Volume 52, Numbers 9-12, 1019-1030

Computer Science

Artificial Intelligence

18 pages,3 table, 4 figures

Scientific paper

10.1007/s00170-010-2802-4

Machine-part cell formation is used in cellular manufacturing in order to process a large variety, quality, lower work in process levels, reducing manufacturing lead-time and customer response time while retaining flexibility for new products. This paper presents a new and novel approach for obtaining machine cells and part families. In the cellular manufacturing the fundamental problem is the formation of part families and machine cells. The present paper deals with the Self Organising Map (SOM) method an unsupervised learning algorithm in Artificial Intelligence, and has been used as a visually decipherable clustering tool of machine-part cell formation. The objective of the paper is to cluster the binary machine-part matrix through visually decipherable cluster of SOM color-coding and labelling via the SOM map nodes in such a way that the part families are processed in that machine cells. The Umatrix, component plane, principal component projection, scatter plot and histogram of SOM have been reported in the present work for the successful visualization of the machine-part cell formation. Computational result with the proposed algorithm on a set of group technology problems available in the literature is also presented. The proposed SOM approach produced solutions with a grouping efficacy that is at least as good as any results earlier reported in the literature and improved the grouping efficacy for 70% of the problems and found immensely useful to both industry practitioners and researchers.

Affiliated with

Also associated with

No associations

Rating

Machine-Part cell formation through visual decipherable clustering of Self Organizing Map does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Machine-Part cell formation through visual decipherable clustering of Self Organizing Map, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Machine-Part cell formation through visual decipherable clustering of Self Organizing Map will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-576164