Multisource data fusion for defect detection in composite additive manufacturing using explainable deep neural network

Abstract

Composite additive manufacturing has the potential to replace traditional composite manufacturing for aerospace applications. Additive manufacturing provides greater adaptability to complex designs, reduce material waste, and streamline the production process. However, in comparison to conventional composite manufacturing methods, additive manufacturing is inherently more susceptible to processing anomalies and defects, primarily due to the novelty of the process and the absence of applied pressure. To effectively capitalize on the benefits of additive manufacturing, a quality and reliability assurance system is critical. In this study, we have generated multisource data to analyze the inner layer thermography and surface quality, employing a multi-camera system including thermal and charge-coupled device cameras. This multisource data is utilized in an explainable zero bias deep neural network framework to detect manufacturing defects. This deep learning algorithm introduces a new zero bias layer following the regular dense layer. After being trained on normal (defect-free) samples of each data stream, the trained model is transformed into an anomaly detector by extracting low dimension features from the zero-bias layer. This allowed identification various anomalies without having to train the model on any defective inputs. As a result, the model's accuracy to detect multiple types of anomalies is higher with multisource data compared to models based on single data source. Anomaly detection accuracy was 99.72% when using data from multiple sources, 98.28% when using only CCD images, and 95% when using only thermal camera data.