In-situ sensing, in conjunction with learning models, presents a unique opportunity to address persistent defect issues in Additive Manufacturing (AM) processes. However, this integration introduces significant data privacy concerns, such as data leakage, sensor data compromise, and model inversion attacks, revealing critical details about part design, material composition, and machine parameters. Differential Privacy (DP) models, which inject noise into data under mathematical guarantees, offer a nuanced balance between data utility and privacy by obscuring traces of sensing data. However, the introduction of noise into learning models, often functioning as black boxes, complicates the prediction of how specific noise levels impact model accuracy. This study introduces the Differential Privacy-HyperDimensional computing (DP-HD) framework, leveraging the explainability of the vector symbolic paradigm to predict the noise impact on the accuracy of in-situ monitoring, safeguarding sensitive data while maintaining operational efficiency. Experimental results on real-world high-speed melt pool data of AM for detecting overhang anomalies demonstrate that DP-HD achieves superior operational efficiency, prediction accuracy, and robust privacy protection, outperforming state-of-the-art Machine Learning (ML) models. For example, when implementing the same level of privacy protection (with a privacy budget set at 1), our model achieved an accuracy of 94.43\%, surpassing the performance of traditional models such as ResNet50 (52.30\%), GoogLeNet (23.85\%), AlexNet (55.78\%), DenseNet201 (69.13\%), and EfficientNet B2 (40.81\%). Notably, DP-HD maintains high performance under substantial noise additions designed to enhance privacy, unlike current models that suffer significant accuracy declines under high privacy constraints.

翻译：原位传感与学习模型相结合，为解决增材制造（AM）过程中长期存在的缺陷问题提供了独特机遇。然而，这种集成也带来了显著的数据隐私问题，例如数据泄露、传感器数据泄露和模型反演攻击，可能暴露零件设计、材料成分和机器参数等关键细节。差分隐私（DP）模型通过在数据中注入噪声并提供数学保证，通过模糊传感数据痕迹，在数据效用与隐私之间实现了精细平衡。然而，将噪声引入通常作为黑箱运行的学习模型中，使得预测特定噪声水平如何影响模型准确性变得复杂。本研究提出了差分隐私-超维度计算（DP-HD）框架，利用向量符号范式的可解释性来预测噪声对原位监测准确性的影响，从而在保持运行效率的同时保护敏感数据。基于真实世界增材制造高速熔池数据（用于检测悬垂异常）的实验结果表明，DP-HD在运行效率、预测准确性和鲁棒的隐私保护方面均表现优异，超越了当前最先进的机器学习（ML）模型。例如，在实施相同级别的隐私保护（隐私预算设为1）时，我们的模型达到了94.43%的准确率，优于传统模型如ResNet50（52.30%）、GoogLeNet（23.85%）、AlexNet（55.78%）、DenseNet201（69.13%）和EfficientNet B2（40.81%）。值得注意的是，DP-HD在旨在增强隐私的大量噪声添加下仍能保持高性能，而现有模型在高隐私约束下则会出现显著的准确性下降。