Machine learning methods are considered a promising approach for improving operations and processes in manufacturing. However, the application of machine learning often requires the expertise of a data scientist combined with thorough knowledge of the manufacturing processes. Small and medium-sized companies that specialize in certain high value-added, variant rich production processes often lack an in-house data scientist and therefore miss out on generating a deeper data-driven insight from their production data streams. This paper proposes a three-step machine learning methodology to empower process experts with limited knowledge in machine learning: 1) data exploration through clustering, 2) representation of the production systems behaviour through specially structured neural networks and 3) querying this representation through evolutionary algorithms to achieve decision support through online optimization or scenario simulation. The chosen algorithms focus on parameter-light, well-established, general use algorithms in order to lower knowledge requirements for their application.

机器学习方法被认为是改善制造中的操作和过程的一种有前途的方法。但是，机器学习的应用通常需要数据科学家的专业知识以及对制造过程的全面了解。专门从事某些高附加值，丰富的变型生产过程的中小型公司通常缺少内部数据科学家，因此错过了从生产数据流中生成更深层的数据驱动见解的机会。本文提出了一种三步式机器学习方法，以使过程专家在机器学习方面的知识有限：1）通过聚类进行数据探索，2）通过特殊结构的神经网络表示生产系统的行为； 3）通过进化算法查询该表示，以通过在线优化或场景模拟获得决策支持。所选择的算法着重于参数轻的，公认的通用算法，以降低其应用程序的知识要求。