This article provides a comprehensive and in-depth overview of our work on knowledge discovery in simulations. Application-wise, we focus on manufacturing simulations. Specifically, we propose and discuss a methodology for designing, executing, and analyzing large-scale simulation experiments with a broad coverage of possible system behavior targeted at generating knowledge about the system. Based on the concept of data farming, we suggest a two-phase process which starts with a data generation phase, in which a smart experiment design is used to set up and efficiently execute a large number of simulation experiments. In the second phase, the knowledge discovery phase, data mining and visually aided analysis methods are applied on the gathered simulation input and output data. This article gives insights into this knowledge discovery phase by discussing different machine learning approaches and their suitability for different manufacturing simulation problems. With this, we provide guidelines on how to conduct knowledge discovery studies within the manufacturing simulation context. We also introduce different case studies, both academic and applied, and use them to validate our methodology.

中文翻译：

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仿真数据中的知识发现

本文全面而深入地概述了我们在模拟知识发现方面的工作。在应用方面，我们专注于制造模拟。具体来说，我们提出并讨论了一种用于设计、执行和分析大规模仿真实验的方法，该方法广泛覆盖可能的系统行为，旨在生成有关系统的知识。基于数据农业的概念，我们建议从数据生成阶段开始的两阶段过程，其中使用智能实验设计来设置和有效执行大量模拟实验。在第二阶段，知识发现阶段，数据挖掘和视觉辅助分析方法应用于收集的模拟输入和输出数据。本文通过讨论不同的机器学习方法及其对不同制造仿真问题的适用性，深入了解这一知识发现阶段。有了这个，我们提供了有关如何在制造模拟环境中进行知识发现研究的指南。我们还介绍了不同的案例研究，包括学术和应用，并使用它们来验证我们的方法。