Towards proper-inconsistency in weldability prediction using k-nearest neighbor regression and generalized regression neural network with mean acceptable error




Park, Junheung
Kim, Kyoung-Yun
Sohmshetty, Raj

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DEStech Publications, Inc.


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.


Paper presented at the Proceedings of the 24th International Conference on Flexible Automation & Intelligent Manufacturing, held May 20-23, 2014 in San Antonio, Texas, and organized by the Center for Advanced Manufacturing and Lean Systems, University of Texas at San Antonio
Includes bibliographical references


Welding--Mathematical models, Machine learning, Neural networks (Computer science)