Document Type

Article

Publication Version

Published Version

Publication Date

2014

Journal or Book Title

Proceedings of the 24th International Conference on Flexible Automation & Intelligent Manufacturing

First Page

395

Last Page

401

DOI

10.14809/faim.2014.0395

Conference Title

24th International Conference on Flexible Automation & Intelligent Manufacturing

Conference Date

May 20–23, 2014

City

San Antonio, TX, United States

Abstract

A significant inconsistency problem exists in the quality of resistance spot welding, and yet it offers various advantages in production. These inconsistent welding data can be eliminated using anomaly detection or instance selection methods. However, in the weldability prediction problem, this inconsistency we refer to as proper-inconsistency, may not be eliminated since it can be used to extract additional information. In this research, we examine the effects of this inconsistency on prediction performance using two machine learning methods, k-Nearest Neighbors (kNN) regression and Generalized Regression Neural Network, in order to identify an approach towards tackling the proper-inconsistency problem in weldability prediction. We also propose a new prediction performance measure, Mean Acceptable Error (MACE), for prediction models in the presence of proper-inconsistency. The proposed method is tested with actual weldability test data

Comments

This proceeding is from Proceedings of the 24th International Conference on Flexible Automation & Intelligent Manufacturing (2014): 395–401, doi:10.14809/faim.2014.0395. Posted with permission.

Copyright Owner

The authors

Language

en

File Format

application/pdf

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