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Abstract In collaborative additive manufacturing (AM), sharing process data across multiple users can provide small to medium-sized manufacturers (SMMs) with enlarged training data for part certification, facilitating accelerated adoption of metal-based AM technologies. The aggregated data can be used to develop a process-defect model that is more precise, reliable, and adaptable. However, the AM process data often contains printing path trajectory information that can significantly jeopardize intellectual property (IP) protection when shared among different users. In this study, a new adaptive AM data deidentification method is proposed that aims to mask the printing trajectory information in the AM process data in the form of melt pool images. This approach integrates stochastic image augmentation (SIA) and adaptive surrogate image generation (ASIG) via tracking melt pool geometric changes to achieve a tradeoff between AM process data privacy and utility. As a result, surrogate melt pool images are generated with perturbed printing directions. In addition, a convolutional neural network (CNN) classifier is used to evaluate the proposed method regarding privacy gain (i.e., changes in the accuracy of identifying printing orientations) and utility loss (i.e., changes in the ability of detecting process anomalies). The proposed method is validated using data collected from two cylindrical specimens using the directed energy deposition (DED) process. The case study results show that the deidentified dataset significantly improved privacy preservation while sacrificing little data utility, once shared on the cloud-based AM system for collaborative process-defect modeling. 

Abstract There is an urgent need for developing collaborative process-defect modeling in metal-based additive manufacturing (AM). This mainly stems from the high volume of training data needed to develop reliable machine learning models for in-situ anomaly detection. The requirements for large data are especially challenging for small-to-medium manufacturers (SMMs), for whom collecting copious amounts of data is usually cost prohibitive. The objective of this research is to develop a secured data sharing mechanism for directed energy deposition (DED) based AM without disclosing product design information, facilitating secured data aggregation for collaborative modeling. However, one major obstacle is the privacy concerns that arise from data sharing, since AM process data contain confidential design information, such as the printing path. The proposed adaptive design de-identification for additive manufacturing (ADDAM) methodology integrates AM process knowledge into an adaptive de-identification procedure to mask the printing trajectory information in metal-based AM thermal history, which otherwise discloses substantial printing path information. This adaptive approach applies a flexible data privacy level to each thermal image based on its similarity with the other images, facilitating better data utility preservation while protecting data privacy. A real-world case study was used to validate the proposed method based on the fabrication of two cylindrical parts using a DED process. These results are expressed as a Pareto optimal solution, demonstrating significant improvements in privacy gain and minimal utility loss. The proposed method can facilitate privacy improvements of up to 30% with as little as 0% losses in dataset utility after de-identification.