Deep Koopman-based Control of Quality Variation in Multistage Manufacturing Systems

TL;DR

This paper introduces a deep Koopman-based control framework for multistage manufacturing systems that effectively minimizes quality variations caused by process disturbances, using a stochastic deep Koopman model to linearize nonlinear dynamics.

Contribution

It develops a novel feedforward control scheme leveraging a stochastic deep Koopman model to improve quality control in nonlinear multistage manufacturing systems.

Findings

Effective reduction of quality variation demonstrated in case studies

Model successfully captures nonlinear quality propagation dynamics

Control scheme applicable without extensive expert knowledge

Abstract

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.