Design De-Identification of Thermal History for Collaborative Process-Defect Modeling of Directed Energy Deposition Processes
Design De-Identification of Thermal History for Collaborative Process-Defect Modeling of Directed Energy Deposition Processes
复制标题
定向能量沉积过程协同过程缺陷建模的热历史设计去识别
DOI：
10.1115/1.4056488
复制
发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Tian, Wenmeng
中科院分区：
文献类型：
--
作者：
Fullington, Durant;Bian, Linkan;Tian, Wenmeng
金属基增材制造（AM）中迫切需要开发协同工艺缺陷建模。这主要是由于开发用于原位异常检测的可靠机器学习模型所需的大量训练数据。对于中小型制造商（SMM）来说，对大数据的需求尤其具有挑战性，因为收集大量数据通常成本高昂。本研究的目的是开发一个安全的数据共享机制，定向能量沉积（DED）为基础的AM不透露产品设计信息，促进安全的数据聚合的协同建模。然而，一个主要的障碍是由数据共享引起的隐私问题，因为AM工艺数据包含机密的设计信息，例如打印路径。所提出的增材制造（ADDAM）自适应设计去识别方法将AM工艺知识集成到自适应去识别过程中，以掩盖金属基AM热历史中的打印轨迹信息，否则其公开了实质的打印路径信息。这种自适应方法根据每个热图像与其他图像的相似性，对每个热图像应用灵活的数据隐私级别，从而在保护数据隐私的同时促进更好的数据效用保存。一个真实世界的案例研究被用来验证所提出的方法的基础上使用DED工艺制造的两个圆柱形零件。这些结果表示为帕累托最优解，展示了隐私增益和最小效用损失的显着改善。所提出的方法可以促进高达30%的隐私改进，去识别后数据集效用损失少至0%。
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.