MARC details
| 000 -LEADER |
|---|
| fixed length control field |
02398naa a2200241 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
161010b xxu||||| |||| 00| 0 eng d |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Gupta, A. |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Novel approach for part family formation for reconfiguration manufacturing system Authors Authors and affiliations |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc |
|
| Name of publisher, distributor, etc |
|
| Date of publication, distribution, etc |
|
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
51 (1) Jan - Mar 2014, 76-97p. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
The Reconfigurable Manufacturing Systems (RMS) is the next step in manufacturing, allowing the production of any quantity of highly customised and complex parts together with the benefits of mass production. In RMSs, parts are grouped into families, each of which requires a specific system configuration. Initially system is configured to produce the first family of parts. Once it is finished, the system is reconfigured in order to produce the second family, and so forth. The effectiveness of a RMS depends on the formation of the optimum set of part families addressing various reconfigurability issues. For this, a two-phase approach is developed where parts are first grouped into families and then families are sequenced, computing the required machines and modules configuration for each family. In the First phase, parts are grouped into families based on their common features. The correlation matrix is developed as operations sequence similarity coefficient matrix. Principal Component Analysis (PCA) is applied to find the eigenvalues and eigenvectors on the correlation similarity matrix. A scatter plot analysis as a cluster analysis is applied to make parts groups while maximizing correlation between parts as per operations sequence similarity. Agglomerative Hierarchical K-means algorithm improved the parts family formation using Euclidean distance resulting a set of part families. In the second phase, optimal selection and sequences of the resulted part families is achieved by using a Mixed Integer Linear Programming (MILP) model minimizing reconfigurability and under-utilization costs to get the minimum cost solution. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Reconfigurable manufacturing system |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Principle Component Analysis |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
K-means algorithm |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Part family selection |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Cluster analysis |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Sequencing |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
MILP |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Jain, P.K. |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Kumar, D. |
| 773 0# - HOST ITEM ENTRY |
|---|
| Host Biblionumber |
90610 |
| Place, publisher, and date of publication |
|
| Other item identifier |
B- 2511 |
| Title |
BV- Opsearch (Jan - Sept 2014) |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Item type |
Articles |
