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Research Papers

Manufacturing Assembly Time Estimation Using Structural Complexity Metric Trained Artificial Neural Networks

[+] Author and Article Information
Michael G. Miller

Research Assistant
Department of Mechanical Engineering,
Clemson University,
Clemson, SC 29634-0921
e-mail: mm3@clemson.edu

Joshua D. Summers

Professor
Department of Mechanical Engineering,
Clemson University,
Clemson, SC 29634-0921
e-mail: jsummer@clemson.edu

James L. Mathieson

Research Assistant
Department of Mechanical Engineering,
Clemson University,
Clemson, SC 29634-0921
e-mail: jmathie@clemson.edu

Gregory M. Mocko

Associate Professor
Department of Mechanical Engineering,
Clemson University,
Clemson, SC 29634-0921
e-mail: gmocko@clemson.edu

1Corresponding author.

Contributed by the Computers and Information Division of ASME for publication in the JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINNERING. Manuscript received October 16, 2012; final manuscript received October 12, 2013; published online January 22, 2014. Editor: Bahram Ravani.

J. Comput. Inf. Sci. Eng 14(1), 011005 (Jan 22, 2014) (10 pages) Paper No: JCISE-12-1188; doi: 10.1115/1.4025809 History: Received October 16, 2012; Revised October 12, 2013

Assembly time estimation is traditionally a time-intensive manual process that requires detailed geometric and process information, which is often subjective and qualitative in nature. As a result, assembly time estimation is rarely applied during early design iterations. In this paper, the authors explore the possibility of automating the assembly time estimation process while reducing the level of design detail required. In this approach, they train artificial neural networks (ANNs) to estimate the assembly times of vehicle subassemblies using either assembly connectivity or liaison graph properties, respectively, as input data. The effectiveness of estimation is evaluated based on the distribution of estimates provided by a population of ANNs trained on the same input data using varying initial conditions. Results indicate that this method can provide time estimates of an assembly process with ±15% error while relying exclusively on the geometric part information rather than process instructions.

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References

Figures

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Fig. 1

Cost engagements and expense occurrences throughout life cycle [12]

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Fig. 2

Automotive manufacturing product life cycle, adapted from [15]

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Fig. 3

Typical Vehicle Development Timeline

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Fig. 4

Bipartite graph and tabular equivalent of automotive subassembly

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Fig. 5

Connectivity graph after first set of assembly tasks

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Fig. 6

Tabular view of connectivity graph after one process

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Fig. 12

Probability density plot with target value and 15% range

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Fig. 11

Probability density plot for connectivity graph #5 and ANN structure 134

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Fig. 10

Process of using the model

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Fig. 9

Model building process

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Fig. 8

MTM-based estimates and original connectivity-based estimates

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Fig. 7

Tabular view of connectivity graph after three processes

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