论文标题
批量制造过程的工业数据科学
Industrial Data Science for Batch Manufacturing Processes
论文作者
论文摘要
批处理过程显示了几种可变性来源，从原材料的特性到制造过程中不同事件期间变化的初始和不断发展的条件。在本章中，我们将用一个工业示例说明如何使用机器学习来减少这种明显的数据，同时维护过程工程师的相关信息。将提供两个常见的用例：1）自动分析以快速找到批处理过程中的相关性，以及2）轨迹分析以监视和识别异常批次，从而导致过程控制改进。
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.
