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

arXiv: https://arxiv.org/abs/2407.16933 · HTML: https://mlsanity.com/paper/2407.16933/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 25 references that are in the map.
- First posted on arXiv 24 Jul 2024.

## Builds on (most cited first, 4 of 4)

- [Deep learning for universal linear embeddings of nonlinear dynamics](https://mlsanity.com/paper/1712.09707/) (2017)
- [Stochastic Adversarial Koopman Model for Dynamical Systems](https://mlsanity.com/paper/2109.05095/) (2021)
- [Deep Multistage Multi-Task Learning for Quality Prediction of Multistage Manufacturing Systems](https://mlsanity.com/paper/2105.08180/) (2021)
- [Stochastic Deep Koopman Model for Quality Propagation Analysis in Multistage Manufacturing Systems](https://mlsanity.com/paper/2309.10193/) (2023)

## Abstract (arXiv)

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.