A multi‐image monitoring framework for statistical process control to improve manufacturing systems

Machine vision system has been widely used for detecting surface defects in a manufacturing process. This paper proposes a new framework for image monitoring with control charts that aims to improve the overall performance in detecting a variety of surface‐related process shifts. Specifically, multiple images of the same object are acquired under different capturing parameters. To illustrate this framework, this paper considers two multi‐image control chart approaches: (1) fusing multiple images together with multilinear principal component analysis and monitoring with a single‐image control chart and (2) using a combined single‐image control chart. A two‐image simulation study, based upon real cross‐correlated images, is accomplished to compare the performances of these two multi‐image control chart approaches to a traditional single‐image control charts. The results indicate that multi‐image control charts outperform single‐image control charts when multiple shifts are considered. Another two‐image simulation study investigates the effect of cross‐correlations to multi‐image control charts’ performances. This study indicates that the two multi‐image control charts have similar performances at low cross‐correlation but the fused‐image control chart outperforms the combined single‐image control chart at high cross‐correlation. In addition, a case study with real images is conducted to demonstrate the proposed multi‐image monitoring framework's ability in detecting shifts.