Deep Koopman-based Control of Quality Variation in Multistage Manufacturing Systems
This addresses quality variation problems for manufacturing systems, offering a method suitable for nonlinear cases without extensive expert knowledge, though it appears incremental as it builds on existing Koopman operator concepts.
The paper tackles quality control in multistage manufacturing systems by developing a feedforward control scheme using a stochastic deep Koopman model to minimize quality variations from process disturbances, validated through two roll-to-roll case studies.
This paper presents a modeling-control synthesis to address the quality control challenges in multistage manufacturing systems (MMSs). A new feedforward control scheme is developed to minimize the quality variations caused by process disturbances in MMSs. Notably, the control framework leverages a stochastic deep Koopman (SDK) model to capture the quality propagation mechanism in the MMSs, highlighted by its ability to transform the nonlinear propagation dynamics into a linear one. Two roll-to-roll case studies are presented to validate the proposed method and demonstrate its effectiveness. The overall method is suitable for nonlinear MMSs and does not require extensive expert knowledge.