Research Papers

Prediction Modeling Framework With Bootstrap Aggregating for Noisy Resistance Spot Welding Data

[+] Author and Article Information
Junheung Park

Ford Motor Company,
15403 Commerce Drive South,
Dearborn, MI 48120
e-mail: jpark45@ford.com

Kyoung-Yun Kim

Department of Industrial and
Systems Engineering,
Wayne State University,
4815 Fourth Street,
Detroit, MI 48202
e-mail: kykim@eng.wayne.edu

1Corresponding author.

Manuscript received December 28, 2016; final manuscript received May 8, 2017; published online August 24, 2017. Assoc. Editor: Robert Gao.

J. Manuf. Sci. Eng 139(10), 101003 (Aug 24, 2017) (11 pages) Paper No: MANU-16-1681; doi: 10.1115/1.4036787 History: Received December 28, 2016; Revised May 08, 2017

In resistance spot welding (RSW), data inconsistency is a well-known issue. Such inconsistent data are usually treated as noise and removed from the original dataset before conducting analyses or constructing prediction models. This may not be desirable for all design and manufacturing applications since data that are often considered noise can contain important information in determining weldment design, and proper welding conditions. In this paper, we present the Meta2 prediction framework to provide cost-effective opportunities for proper welding material and condition selection from the noisy RSW quality data. The Meta2 framework employs bootstrap aggregating with support vector regression (SVR) to improve the prediction accuracy on the noisy RSW data with computational efficiency. Hyper-parameters for SVR are selected by particle swarm optimization (PSO) with meta-modeling to reduce the computational cost. Experiments on three artificially generated noisy datasets and a real RSW dataset indicate that Meta2 is capable of providing satisfactory solutions with a noticeably reduced computational cost. The authors find Meta2 promising as a potential prediction model algorithm for this type of noisy data.

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Fig. 1

Overall procedure of Meta2

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Fig. 2

Ranges of nugget with for unique welding conditions. All unique welding conditions (left) and zoomed-in ones with highly variable nugget width range (right).

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Fig. 3

A number of data for each unique welding condition. All unique welding conditions (left) and zoomed-in ones with high variable nugget width range (right).

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Fig. 4

Pseudo code of bagging [42]

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Fig. 5

Mean number of fitness function evaluations for GPSO and Meta2 (left) and mean elapsed time in minutes (right) for dataset2

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Fig. 6

Mean number of fitness function evaluations for GPSO and Meta2 (left) and mean elapsed time in minutes (right) for the RSW quality dataset



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