In Wire Arc Additive Manufacturing (WAAM), weld beads are deposited bead-by-bead and layer-by-layer, leading to the final part. Thus, the lack of uniformity or geometrically defective bead will subsequently lead to voids in the printed part, which will have a great impact on the overall part quality and mechanical strength. To resolve this, several techniques have been proposed to identity such defects using vision or thermal-based sensing, so as to aid in the implementation of in-situ corrective measures to save time and cost. However, due to the environment that they are operating in, these sensors are not an effective way of picking up irregularities as compared to acoustic sensing. Therefore, in this paper, we seek to study into three acoustic feature-based machine learning frameworks — Principal Component Analysis (PCA) + K-Nearest Neighbors (KNN), Mel Frequency Cepstral Coefficients (MFCC) + Neural Network (NN) and Mel Frequency Cepstral Coefficients (MFCC) + Convolutional Neural Network (CNN) and evaluate their performance for the real-time identification of geometrically defective weld bead. Experiments are carried out on stainless steel (ER316LSi), bronze (ERCuNiAl) and mixed dataset containing both stainless steel and bronze. The results show that all three frameworks outperform the state-of-the-art acoustic signal based ANN approach in terms of accuracy. The best performing framework PCA+KNN outperforms ANN by more than 15%, 30% and 30% for stainless steel, bronze and mixed datasets, respectively.

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