Explainable AI based Predictions for Workpiece Quality

It is well known that sound emissions of drilling and milling machines can be used to predict process and work piece quality. Deep Learning models have successfully been applied for that task. However, artificial neural networks are perceived as black boxes as the inference mechanisms of the network are not transparent. In this paper we present a Convolutional Neural Network trained on audio data with which process and product quality of a milling process can be controlled. The model was trained on 10-second audio snippets recorded from a milling machine that were converted into spectrograms. The trained model classifies the drilling process and predicts the expected workpiece quality. Additionally, rough estimates of the center rough and average roughness depth values are predicted. Moreover, we apply the Explainable AI methods that produce explanations by highlighting sections of the spectrogram that were relevant for the prediction. The spectrograms enriched with the explanations provide insights into the decision making process of the deep learning model in a human interpretable form. Patterns of how and why of a prediction become visible. Areas considered relevant by the model can be compared to the expectations of the human user. Through this explanation component, the model is not only able to make predictions but also to increase trust in the system which is an important aspect of acceptance of AI based quality assurance.