Image-Based Feedback Control Using Tensor Analysis

Abstract

In manufacturing systems, many quality measurements are in the form of images, including overlay measurements in the semiconductor manufacturing and dimensional deformation profiles of fuselages in an aircraft assembly process. To reduce the process variability and ensure on-target quality, process control strategies should be deployed, in which the high-dimensional image output is controlled by one or more input variables. To design an effective control strategy, the process model should be first estimated via relationship exploration between the image output and inputs, off-line. Next, the control law is formulated by minimizing the control objective function online. The main challenges of achieving such a control strategy include (i) the high dimensional output of a regression model, (ii) the integrated analysis of both the spatial structure of image outputs and the temporal structure of the image sequence, and (iii) non-iid noises. To address these challenges, we propose a novel tensor-based process control approach by incorporating the tensor time series and regression techniques. Based on the process model, we can then obtain the control law by minimizing a control objective function. Although our proposed approach is motivated by a 2D image case, it can be extended to higher-order tensors such as point clouds. Simulation and case studies show that our proposed method is more effective than benchmarks in terms of relative mean square error.

KEYWORD:

• Feedback control
• High-dimensional data streams
• Tensor-based ARMAX
• Tucker decomposition

Additional information

Funding

The work of Kamran Paynabar was partially supported by NSF CMMI-1839591.