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

Abstract

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 singleimage 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 crosscorrelation. In addition, a case study with real images is conducted to demonstrate the proposed multi-image monitoring framework's ability in detecting shifts.