Industrial Data Science for Batch Manufacturing Processes
This work addresses process control improvements for industrial engineers, but it is incremental as it applies existing methods to a specific domain.
The paper tackles variability in batch manufacturing processes by using machine learning to reduce data while preserving relevant information for engineers, presenting AutoML for correlation analysis and trajectory analysis for anomaly detection.
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.