Anomaly Detection in WAAM Deposition of Nickel Alloys

Abstract

The current research work investigates the possibility of using machine learning models to deduce the relationship between WAAM (wire arc additive manufacturing) sensor responses and defect presence in the printed part. The work specifically focuses on three materials from the nickel alloy family (Inconel 718, Invar 36 and Inconel 625) and uses three sensor responses for data analysis, which are welding voltage, welding current and welding audio. Two different machine learning models are used—artificial neural networks (ANNs) and random forests (RF). The results for each of the materials, separately, indicate that the accuracies range from 60% to 90% and the correlation coefficient is less than 0.5 (indicating weak positive correlation), depending on the model and material. In addition to separate material analysis, a cross-material data analysis was formed to test the models’ general prediction capabilities. This led to predictions that are significantly worse, with accuracies ranging from 20% to 27% and very weak correlation coefficients (less than 0.1), indicating that the choice of material is still important as a boundary condition. Analysis of the results indicates that the relative importance of audio sensor response depends on the nature of defect formation. Random forests are found to perform the best for single material analysis, with the comparatively inferior performance of ANNs possibly being due to lack of sufficient datapoints.