Choi SangSu, Jung Kiwook, Kulvatunyou Boonserm, Morris K C
IGI, LLC, Clarksburg, MD, USA 20871.
Materials&Production Engineering Research Institute, LG Electronics Inc., 222 LG-ro, Jinwi-myeon, Pyeongtaek-si, Gyeonggi-do, 17709, South Korea.
J Res Natl Inst Stand Technol. 2016 Sep 20;121:422-433. doi: 10.6028/jres.121.021. eCollection 2016.
Smart manufacturing is defined by high degrees of automation. Automation, in turn, is defined by clearly defined processes. The use of standards in this environment is not just commonplace, but essential to creating repeatable processes and reliable systems. As with the rest of society, manufacturing systems are becoming more tightly connected through advances in information and communication technologies (ICT). As a result, manufacturers are able to receive information from their business partners and operational units much more quickly and are expected to respond quickly as well. Quick responses to changes in a manufacturing system are much more challenging than the responses that we have come to expect in other aspects of our lives. Manufacturing revolves around heavy capital investments to rapidly produce large amounts of product in anticipation of steady streams of commerce. Changes under these conditions not only disrupt the operations, slowing the production of goods, but also create difficulties with managing the capital investments. These are challenges manufacturers face daily. A large part of these challenges is understanding how best to refit manufacturing facilities to respond to variability, and how to plan new production facilities. By analyzing the information that is available in a manufacturing system, manufacturers can make more informed decisions as to how to respond to change. Advances in the technological infrastructure underlying manufacturing systems are enabling more reliable and timely flow of information across all levels of the manufacturing operation. We propose that effective utilization of such operational information will enable more automated, agile responses to the changing conditions, i.e. Smart Manufacturing. In this paper, we analyze the sources and the standards supporting the flow of that information throughout the enterprise. The analysis is based an intersection of two reference models: the Factory Design and Improvement (FDI) process and the ISA88 hierarchical model of manufacturing operations. The FDI process consists of a set of high-level activities for designing and improving manufacturing operations. The ISA88 hierarchical model specifies seven levels of control within a manufacturing enterprise.
智能制造的定义是高度自动化。而自动化又是由明确界定的流程来定义的。在这种环境下，标准的使用不仅司空见惯，而且对于创建可重复的流程和可靠的系统至关重要。与社会的其他领域一样，通过信息和通信技术（ICT）的进步，制造系统之间的联系正变得越来越紧密。因此，制造商能够更快地从其商业伙伴和运营单位接收信息，并且也被期望能够快速做出回应。对制造系统中的变化做出快速响应比我们在生活的其他方面所期望的响应要困难得多。制造业围绕着大量资本投资展开，以期在稳定的商业流预期下迅速生产大量产品。在这些条件下的变化不仅会扰乱运营，减缓货物生产，还会给资本投资管理带来困难。这些都是制造商每天面临的挑战。这些挑战的很大一部分在于理解如何最好地改造制造设施以应对变化，以及如何规划新的生产设施。通过分析制造系统中可用的信息，制造商可以就如何应对变化做出更明智的决策。制造系统底层技术基础设施的进步使得信息能够在制造运营的各个层面更可靠、更及时地流动。我们认为，有效利用此类运营信息将能够对不断变化的条件做出更自动化、更灵活的响应，即智能制造。在本文中，我们分析了支持该信息在整个企业中流动的来源和标准。该分析基于两个参考模型的交叉点：工厂设计与改进（FDI）流程和制造运营的ISA88层次模型。FDI流程由一组用于设计和改进制造运营的高级活动组成。ISA88层次模型规定了制造企业内的七个控制级别。
