An ensemble data-driven fuzzy network for laser welding quality prediction

This paper presents an Ensemble Data-Driven Fuzzy Network (EDDFN) for laser welding quality prediction that is composed of a number of strategically selected Data-Driven Fuzzy Models (DDFMs). Each model is trained by an Adaptive Negative Correlation Learning approach (ANCL). A monitoring system provides quality-relevant information of the laser beam spectrum and the geometry of the melt pool. This information is used by the proposed ensemble model to assist in the prediction of the welding quality. Each DDFM is based on three conceptual components, i.e. a selection procedure of the most representative welding information, a granular comprehension process of data and the construction of a fuzzy reasoning mechanism as a series of Radial Basis Function Neural Networks (RBF-NNs). The proposed model aims at providing a fuzzy reasoning engine that is able to preserve a good balance between transparency and accuracy while improving its prediction properties. We apply the EDDFN to a real case study in manufacturing industry for the prediction of welding quality. The corresponding results confirm that the EDDFN provides better prediction properties compared to a single DDFM with an overall prediction performance > 78%.