Machine vision systems are increasingly being used in industrial applications because of their ability to quickly provide information on product geometry, surface defects, surface finish, and other product and process characteristics. Previous research for monitoring these visual characteristics using image data has focused on either detecting changes within an image or between images. Extending these methods to include both the spatial and the temporal aspects of image data would provide more detailed diagnostic information, which would be of great value to industrial practitioners. Therefore, in this article, we show how image data can be monitored using a spatiotemporal framework that is based on an extension of a generalized likelihood ratio control chart. The performance of the proposed method is evaluated through computer simulations and experimental studies. The results show that our proposed spatiotemporal method is capable of quickly detecting the emergence of a fault. The computer simulations also show that our proposed generalized likelihood ratio control charting method provides a good estimate of the change point and the size/location of the fault, which are important fault diagnostic metrics that are not typically provided in the image monitoring literature. Finally, we highlight some research opportunities and provide some advice to practitioners.

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Dimensional variation is inherent to any manufacturing process. In order to minimize its impact on assembly products it is important to understand how the variation propagates through the assembly process. Unfortunately, manufacturing processes are complex and in many cases highly nonlinear. Traditionally, assembly process modeling has been approached as a linear process. However, many assemblies undergo highly complex nonlinear physical processes, such as compliant deformation, contact interaction, and welding thermal deformation. This paper presents a new variation propagation methodology considering the compliant contact effect, which will be analyzed through nonlinear frictional contact analysis. Its variation prediction will be accurately and efficiently conducted using an enhanced dimension reduction method. A case study is presented to show the applicability of the proposed methodology.