Research Papers: Design for Manufacture and the Life Cycle

Determining Granularity of Changeable Manufacturing Systems Using Changeable Design Structure Matrix and Cladistics

[+] Author and Article Information
Tarek AlGeddawy

Mechanical and Industrial Engineering,
University of Minnesota Duluth,
1305 Ordean Court,
Duluth, MN 55812
e-mail: geddawy@d.umn.edu

Hoda ElMaraghy

Intelligent Manufacturing Systems Center,
University of Windsor,
2285 Wyandotte West,
Windsor, ON N9B 1K3, Canada
e-mail: hae@uwindsor.ca

Contributed by the Design for Manufacturing Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received December 27, 2013; final manuscript received December 26, 2014; published online February 6, 2015. Assoc. Editor: Rikard Söderberg.

J. Mech. Des 137(4), 041702 (Apr 01, 2015) (12 pages) Paper No: MD-13-1592; doi: 10.1115/1.4029515 History: Received December 27, 2013; Revised December 26, 2014; Online February 06, 2015

Changeable manufacturing systems offer a high level of adaptability and agility in response to product and market changes. They are characterized by modularity and scalability, which are derivatives of system granularity. Determining the best granularity level of a changeable system helps maximize its ability to change throughout its planned utilization horizon. A new model and two case studies are presented to show: (1) new changeability design structure matrix (CDSM) to express all planned system configurations, (2) cladistics analysis to hierarchically cluster CDSM into component modules, and (3) new granularity index (GI) to determine the best system granularity level which balances the merits of manufacturing system modularity with integration.

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Fig. 1

Granularity levels of an automobile (modified from Ref. [28])

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Fig. 2

Cladistics analysis of a group of work-pieces

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Fig. 3

IDEF0 representation of the SCA model

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Fig. 4

The four DSM scenarios of two components in a changeable manufacturing system

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Fig. 5

The generation of a CDSM from system configuration DSMs

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Fig. 6

The effect of importance of integration versus segregation and existence of system components on GI

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Fig. 7

The required configurations of the studied reconfigurable machine-tool and the corresponding CDSM analysis

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Fig. 8

Cladistic and changeability analysis of the machine-tool

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Fig. 9

Generic structure of an iFactory station

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Fig. 10

The families of products produced by each iFactory system configuration

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Fig. 11

The four anticipated system configurations of the iFactory

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Fig. 12

Graph representation of the four planned iFactory configurations

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Fig. 13

DSM representation of four iFactory configurations and the aggregated CDSM

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Fig. 14

The cladogram resulting from cladistics analysis of the CDSM

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Fig. 15

The suggested integrated system module for a better system changeability

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Fig. 16

The four anticipated iFactory configurations using the recommended integrated module (9)

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Fig. 17

Different granularity levels effect on GI




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