# Industrial Data Science for Batch Manufacturing Processes

arXiv: https://arxiv.org/abs/2209.09660 · HTML: https://mlsanity.com/paper/2209.09660/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 112 references that are in the map.
- First posted on arXiv 20 Sep 2022.

## Builds on (most cited first, 6 of 6)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Shapley Flow: A Graph-based Approach to Interpreting Model Predictions](https://mlsanity.com/paper/2010.14592/) (2020)
- [Functional Regression](https://mlsanity.com/paper/1406.4068/) (2014)
- [The ALAMO approach to machine learning](https://mlsanity.com/paper/1705.10918/) (2017)
- [On the implementation of a global optimization method for mixed-variable problems](https://mlsanity.com/paper/2009.02183/) (2020)

## Abstract (arXiv)

Batch processes show several sources of variability, from raw materials' properties to initial and evolving conditions that change during the different events in the manufacturing process. In this chapter, we will illustrate with an industrial example how to use machine learning to reduce this apparent excess of data while maintaining the relevant information for process engineers. Two common use cases will be presented: 1) AutoML analysis to quickly find correlations in batch process data, and 2) trajectory analysis to monitor and identify anomalous batches leading to process control improvements.