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 de-identification 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 trade-off 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 to detect 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 de-identified dataset significantly improved privacy preservation while sacrificing little data utility, once shared on the cloud-based AM system for collaborative process-defect modeling.

Issue Section:
Research Papers
Keywords:
additive manufacturing, cloud manufacturing, de-identification, intellectual property, process-defect modeling, privacy-preserving data sharing, big data and analytics, computational foundations for additive manufacturing, cyber physical security for factories, cybermanufacturing, industrial Internet of things, machine learning for engineering applications
Topics:
Additive manufacturing, Algorithms, Data privacy, Design, Intellectual property, Modeling, Printing, Trajectories (Physics), Machine learning, Security, Manufacturing, Tradeoffs, Convolutional neural networks, Preservation

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