Industry 4.0 represents high-level methodologies for the development of new generation manufacturing metrology systems, which are more intelligent (smart), autonomous, flexible, high-productive, and self-adaptable. One of the systems capable of responding to these challenges is a cyber-physical manufacturing metrology system (CP2MS) with techniques of artificial intelligence (AI). In general, CP2MS systems generate Big data, horizontally by integration [coordinate measuring machines (CMMs)] and vertically by control. This paper presents a cyber-physical manufacturing metrology model (CP3M) for Industry 4.0 developed by applying AI techniques such as engineering ontology (EO), ant-colony optimization (ACO), and genetic algorithms (GAs). Particularly, the CP3M presents an intelligent approach of probe configuration and setup planning for inspection of prismatic measurement parts (PMPs) on a CMM. A set of possible PMP setups and probe configurations is reduced to optimal number using developed GA-based methodology. The major novelty is the development of a new CP3M capable of responding to the requirements of an Industry 4.0 concept such as intelligent, autonomous, and productive measuring systems. As such, they respond to one smart metrology requirement within the framework of Industry 4.0, referring to the optimal number of PMPs setups and for each setup defines the configurations of probes. The main contribution of the model is productivity increase of the measuring process through the reduction of the total measurement time, as well as the elimination of errors due to the human factor through intelligent planning of probe configuration and part setup. The experiment was successfully performed using a PMP specially designed and manufactured for the purpose.

中文翻译：

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迈向工业 4.0 的网络物理制造计量模型

工业 4.0 代表了开发新一代制造计量系统的高级方法，这些系统更智能（智能）、自主、灵活、高产和自适应。能够应对这些挑战的系统之一是具有人工智能 (AI) 技术的网络物理制造计量系统 (CP2MS)。一般而言，CP2MS 系统通过集成 [坐标测量机 (CMM)] 水平生成大数据，并通过控制在垂直方向生成大数据。本文介绍了一种工业 4.0 的信息物理制造计量模型 (CP3M)，该模型是通过应用工程本体 (EO)、蚁群优化 (ACO) 和遗传算法 (GA) 等人工智能技术开发的。特别，CP3M 提供了一种智能的探头配置和设置规划方法，用于检测 CMM 上的棱镜测量部件 (PMP)。使用已开发的基于 GA 的方法，可以将一组可能的 PMP 设置和探头配置减少到最佳数量。主要的创新之处在于开发了一种新的 CP3M，它能够响应工业 4.0 概念的要求，例如智能、自主和高效的测量系统。因此，它们响应工业 4.0 框架内的一项智能计量要求，参考 PMP 设置的最佳数量，并为每个设置定义探头的配置。该模型的主要贡献是通过减少总测量时间来提高测量过程的生产力，以及通过智能规划探头配置和零件设置来消除人为因素造成的错误。使用为此目的专门设计和制造的 PMP 成功地进行了实验。