A Spatiotemporal Scheme for Process Control Using Image Analysis of Speckle Patterns

ABSTRACT Conventional image‐based process control schemes require distinct, detectable features (e.g., edges or textures). In the absence of such features, current methods fail or require additional preprocessing. They are also prone to false detections from random noise, dirt, or scratches on the surface. In addition, they often focus on pixel intensity differences, making them less effective for subtle shifts or uniform surfaces. Analyzing speckles generated by coherent illumination (e.g., lasers) has been proven superior in situations where the surface has subtle or no features. Components with smooth surfaces (e.g., gears) lack distinct image features, making traditional methods ineffective. Laser speckle image provides unique dynamic and surface‐sensitive insights that are not attainable with conventional imaging. The robustness of speckles to lighting and contamination makes speckle analysis even better suited for industrial environments. In addition, speckle patterns allow for fine‐grained analysis of specific regions. This study introduces speckle pattern analysis for control charting and process control. The developed scheme is also capable of simultaneously detecting shifts that occur in different parts of the speckle pattern. It has two main steps. In the first step, we develop an EMWA control chart based on the maximum absolute Fourier magnitude spectrum differences of the current image (speckle pattern) and the reference speckle pattern. In the second step, which is performed when the chart signals, we use a p value analysis to identify the shifted areas of the image. Our scheme relies on dividing the image into equal‐sized grids, analyzing them individually, and then aggregating the results. We use a gear's case for our numerical analyses and conduct simulation runs under different shift sizes and gridding scenarios. Finally, we illustrate how the developed scheme can be used for online process monitoring.