Evaluation of design information disclosure through thermal feature extraction in metal based additive manufacturing

Abstract

Manufacturing-as-a-Service (MaaS) can accelerate additive manufacturing (AM) process-defect modeling by augmenting training data to all collaborating users via a data sharing network. However, sharing process data may disclose product design information. This paper aims to evaluate design information disclosure of various thermal history-based feature extraction methods for metal-based AM anomaly detection. This is accomplished by evaluating the design information (i.e., printing orientation) retained, and the overall data usability (i.e., anomaly detection) preserved in the extracted features for various state-of-the-art feature extraction methods. The evaluation results indicate that there are urgent needs in privacy preserving data sharing for additive MaaS (AMaaS).