In-situ monitoring of stochastic textured surfaces with EWMA scheme using dynamic KDE-based probability limits
提出一种基于似然比检验的EWMA原位监测方案，利用梯度提升回归树和动态核密度估计概率限，检测随机纹理表面的全局偏移，适用于增材制造等工业场景。
Due to the stochastic nature of textured surfaces, in-situ quality monitoring for texture-related defects using statistical process monitoring (SPM) is important yet challenging in academic research and industrial applications. This article presents an in-situ EWMA monitoring scheme based on the likelihood ratio test to quantify and detect unexpected global shifts in textured surfaces. We employ Gradient Boosting Regression Trees to implicitly characterise the joint distribution of textile image pixels and for the texture modelling. With the limited number of Phase I samples, the proposed scheme with a data-driven control limit algorithm can estimate the distribution of the charting statistics via the kernel density estimation (KDE) method, continuously update the probability limits at each time point during the monitoring phase and implement the dynamic monitoring to identify defective surfaces with a relatively satisfying in-control monitoring performance. The simulated stochastic experiments confirm the advantage of the proposed method. Also, a real layerwise images monitoring case based on Fused Deposition Modeling from Additive Manufacturing (AM) is provided.