# Deep Multistage Multi-Task Learning for Quality Prediction of Multistage Manufacturing Systems

arXiv: https://arxiv.org/abs/2105.08180 · HTML: https://mlsanity.com/paper/2105.08180/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 54 references that are in the map.
- First posted on arXiv 17 May 2021.

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

- [Speech Recognition with Deep Recurrent Neural Networks](https://mlsanity.com/paper/1303.5778/) (2013)
- [Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models](https://mlsanity.com/paper/1612.08468/) (2016)
- [Structured Point Cloud Data Analysis via Regularized Tensor Regression for Process Modeling and Optimization](https://mlsanity.com/paper/1807.10278/) (2018)
- [Dynamic Multivariate Functional Data Modeling via Sparse Subspace Learning](https://mlsanity.com/paper/1804.03797/) (2018)

## Built on by (most cited first, 3 of 3)

- [Knowledge Distillation-based Information Sharing for Online Process Monitoring in Decentralized Manufacturing System](https://mlsanity.com/paper/2302.12004/) (2023)
- [Stochastic Deep Koopman Model for Quality Propagation Analysis in Multistage Manufacturing Systems](https://mlsanity.com/paper/2309.10193/) (2023)
- [Deep Koopman-based Control of Quality Variation in Multistage Manufacturing Systems](https://mlsanity.com/paper/2407.16933/) (2024)

## Abstract (arXiv)

In multistage manufacturing systems, modeling multiple quality indices based on the process sensing variables is important. However, the classic modeling technique predicts each quality variable one at a time, which fails to consider the correlation within or between stages. We propose a deep multistage multi-task learning framework to jointly predict all output sensing variables in a unified end-to-end learning framework according to the sequential system architecture in the MMS. Our numerical studies and real case study have shown that the new model has a superior performance compared to many benchmark methods as well as great interpretability through developed variable selection techniques.