Machine Learning (ML) models combined with in-situ sensing offer a powerful solution to address defect detection challenges in Additive Manufacturing (AM), yet this integration raises critical data privacy concerns, such as data leakage and sensor data compromise, potentially exposing sensitive information about part design and material composition. Differential Privacy (DP), which adds mathematically controlled noise to ML models, provides a way to balance data utility with privacy by concealing identifiable traces from sensor data. However, introducing noise into ML models, especially black-box Artificial Intelligence (AI) models, complicates the prediction of how noise impacts model accuracy. This study presents the Differential Privacy-Hyperdimensional Computing (DP-HD) framework, which leverages Explainable AI (XAI) and the vector symbolic paradigm to quantify noise effects on accuracy. By defining a Signal-to-Noise Ratio (SNR) metric, DP-HD assesses the contribution of training data relative to DP noise, allowing selection of an optimal balance between accuracy and privacy. Experimental results using high-speed melt pool data for anomaly detection in AM demonstrate that DP-HD achieves superior operational efficiency, prediction accuracy, and privacy protection. For instance, with a privacy budget set at 1, DP-HD achieves 94.43% accuracy, outperforming state-of-the-art ML models. Furthermore, DP-HD maintains high accuracy under substantial noise additions to enhance privacy, unlike current models that experience significant accuracy declines under stringent privacy constraints.

翻译：机器学习模型与现场传感技术相结合，为增材制造中的缺陷检测挑战提供了强大解决方案，但这种集成引发了关键的数据隐私问题，例如数据泄露和传感器数据泄露，可能暴露有关零件设计和材料成分的敏感信息。差分隐私通过向机器学习模型添加数学可控噪声，通过隐藏传感器数据中的可识别痕迹，提供了一种平衡数据效用与隐私的方法。然而，将噪声引入机器学习模型，特别是黑盒人工智能模型，使得预测噪声如何影响模型准确性变得复杂。本研究提出了差分隐私-超维计算框架，该框架利用可解释人工智能和向量符号范式来量化噪声对准确性的影响。通过定义信噪比指标，DP-HD评估训练数据相对于差分隐私噪声的贡献，从而允许在准确性和隐私之间选择最佳平衡点。使用高速熔池数据进行增材制造异常检测的实验结果表明，DP-HD实现了卓越的运行效率、预测准确性和隐私保护。例如，在隐私预算设为1时，DP-HD达到94.43%的准确率，优于最先进的机器学习模型。此外，与当前模型在严格隐私约束下准确性显著下降不同，DP-HD在添加大量噪声以增强隐私的情况下仍能保持高准确性。