Research Papers

An Approach to Determine Important Attributes for Engineering Change Evaluation

[+] Author and Article Information
Chandresh Mehta

Mechanical Engineering,
University of Michigan,
Ann Arbor, MI 48105
e-mail: mehtacr@umich.edu

Lalit Patil

e-mail: lpatil@illinois.edu

Debasish Dutta

e-mail: ddutta@illinois.edu
Mechanical Science and Engineering,
University of Illinois at Urbana-Champaign,
Urbana, IL 61801

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the Journal of Mechanical Design. Manuscript received November 21, 2010; final manuscript received January 8, 2013; published online March 26, 2013. Assoc. Editor: Olivier de Weck.

J. Mech. Des 135(4), 041003 (Mar 26, 2013) (17 pages) Paper No: MD-10-1436; doi: 10.1115/1.4023551 History: Received November 21, 2010; Revised January 08, 2013

Enterprises plan detailed evaluation of only those engineering change (EC) effects that might have a significant impact. Using past EC knowledge can prove effective in determining whether a proposed EC effect has significant impact. In order to utilize past EC knowledge, it is essential to identify important attributes that should be compared to compute similarity between ECs. This paper presents a knowledge-based approach for determining important EC attributes that should be compared to retrieve similar past ECs so that the impact of proposed EC effect can be evaluated. The problem of determining important EC attributes is formulated as the multi-objective optimization problem. Measures are defined to quantify importance of an attribute set. The knowledge in change database and the domain rules among attribute values are combined for computing the measures. An ant colony optimization (ACO)-based search approach is used for efficiently locating the set of important attributes. An example EC knowledge-base is created and used for evaluating the measures and the overall approach. The evaluation results show that our measures perform better than state-of-the-art evaluation criteria. Our overall approach is evaluated based on manual observations. The results show that our approach correctly evaluates the value of proposed change impact with a success rate of 83.33%.

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Fig. 1

Partial illustration of a typical STEP-compliant data model for capturing the knowledge associated with a change

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Fig. 2

A typical attribute selection method (adapted from Ref. [23])

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Fig. 3

Problem input, output and the components of our overall approach

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Fig. 4

Two examples ECs of type change in shape from the database of 17 ECs. The entire data are available at Ref. [38]. A few attribute-values associated with old and new instance of the part are shown below the corresponding picture.

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Fig. 5

Ten datasets created from 17 ECs in database. These datasets are utilized for evaluating approaches discussed in this paper.

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Fig. 6

Constituents of IAS

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Fig. 7

Tandem solution generation procedure followed by a pair of agents. Agent z is from group Z and agent u is from group U. The important attribute set determined by the pair z and u is the set Apz∪Apu. The two agents exchange information during the iterative selection process.

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Fig. 10

Nonzero portions of matrix tex for the case study and evaluation. The matrix tex captures the information about attributes that are mutually exclusive. The first row and column of each matrix indicates the attribute integer id. All the empty cells have zero values.

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Fig. 8

Overall loss in predicting impact and effect attributes values using various subset evaluation criteria

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Fig. 9

Nonzero portions of matrix tin for the case study and evaluation. The matrix tin captures the information about attributes that are mutually inclusive. The first row and column indicates the attribute integer id. All the empty cells have zero values.



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