Traditional Statistical Process Control (SPC) is essential for quality management but is limited by its reliance on often violated statistical assumptions, leading to unreliable monitoring in modern, complex manufacturing environments. This paper introduces a hybrid framework that enhances SPC by integrating the distribution free, model agnostic guarantees of Conformal Prediction. We propose two novel applications: Conformal-Enhanced Control Charts, which visualize process uncertainty and enable proactive signals like 'uncertainty spikes', and Conformal-Enhanced Process Monitoring, which reframes multivariate control as a formal anomaly detection problem using an intuitive p-value chart. Our framework provides a more robust and statistically rigorous approach to quality control while maintaining the interpretability and ease of use of classic methods.

翻译：传统统计过程控制（Statistical Process Control, SPC）在质量管理中至关重要，但其局限性在于依赖于常被违反的统计假设，导致在现代复杂制造环境中的监控结果不可靠。本文提出了一种混合框架，通过整合保形预测（Conformal Prediction）的无分布、模型无关的统计保证来增强SPC。我们提出了两种新颖的应用：保形增强控制图，用于可视化过程不确定性并实现如“不确定性尖峰”等主动预警信号；以及保形增强过程监控，该方法通过直观的p值图将多变量控制重新构建为正式异常检测问题。我们的框架为质量控制提供了一种更稳健且统计上更严谨的方法，同时保持了经典方法的可解释性和易用性。