Neuro-Fuzzy Model for the Prediction and Classification of the Fused Zone Levels of Imperfections in Ti6Al4V Alloy Butt Weld

Abstract

Weld imperfections are tolerable defects as stated from the international standard. Nevertheless they can produce a set of drawbacks like difficulty to assembly, reworking, limited fatigue life, and surface imperfections. In this paper Ti6Al4V titanium butt welds were produced by CO2laser welding. The following tolerable defects were analysed: weld undercut, excess weld metal, excessive penetration, incomplete filled groove, root concavity, and lack of penetration. A neuro-fuzzy model for the prediction and classification of the defects in the fused zone was built up using the experimental data. Weld imperfections were connected to the welding parameters by feed forward neural networks. Then the imperfections were clustered using the C-means fuzzy clustering algorithm. The clusters were named after the ISO standard classification of the levels of imperfection for electron and laser beam welding of aluminium alloys and steels. Finally, a single-value metric was proposed for the assessment of the overall bead geometry quality. It combined an index for each defect and functioned according to the criterion “the-smallest-the-best.”