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

机译:利用K-Collect Exbeld回归和广义回归神经网络的焊接性预测的适当 - 不一致,具有平均可接受的误差

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摘要

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
机译:电阻点焊的质量存在明显的不一致问题,但在生产中却具有各种优势。可以使用异常检测或实例选择方法消除这些不一致的焊接数据。但是,在可焊性预测问题中,我们称之为固有矛盾的这种矛盾可能无法消除,因为它可以用来提取其他信息。在这项研究中,我们使用k-最近邻(kNN)回归和广义回归神经网络这两种机器学习方法研究了这种不一致对预测性能的影响,以便确定解决可焊性预测中固有矛盾问题的方法。 。我们还针对存在适当不一致的预测模型提出了一种新的预测性能度量,即平均可接受误差(MACE)。使用实际的可焊性测试数据对提出的方法进行了测试

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