Kibira Deogratias, Morris K C, Kumaraguru Senthilkumaran
Morgan State University, Baltimore, MD 21251.
National Institute of Standards and Technology, Gaithersburg, MD 20899.
J Res Natl Inst Stand Technol. 2016 Jun 2;121:282-313. doi: 10.6028/jres.121.013. eCollection 2016.
The emerging concept of smart manufacturing systems is defined in part by the introduction of new technologies that are promoting rapid and widespread information flow within the manufacturing system and surrounding its control. These systems can deliver unprecedented awareness, agility, productivity, and resilience within the production process by exploiting the ever-increasing availability of real-time manufacturing data. Optimized collection and analysis of this voluminous data to guide decision-making is, however, a complex and dynamic process. To establish and maintain confidence that smart manufacturing systems function as intended, performance assurance measures will be vital. The activities for performance assurance span manufacturing system design, operation, performance assessment, evaluation, analysis, decision making, and control. Changes may be needed for traditional approaches in these activities to address smart manufacturing systems. This paper reviews the current methods and tools used for establishing and maintaining required system performance. It then identifies trends in data and information systems, integration, performance measurement, analysis, and performance improvement that will be vital for assured performance of smart manufacturing systems. Finally, we analyze how those trends apply to the methods studied and propose future research for assessing and improving manufacturing performance in the uncertain, multi-objective operating environment.
智能制造系统这一新兴概念的部分定义在于引入了新技术，这些新技术促进了制造系统内部及其控制环节的快速且广泛的信息流。通过利用日益丰富的实时制造数据，这些系统能够在生产过程中实现前所未有的感知能力、敏捷性、生产率和恢复力。然而，对这些海量数据进行优化收集和分析以指导决策是一个复杂且动态的过程。为了建立并维持对智能制造系统按预期运行的信心，性能保证措施至关重要。性能保证活动涵盖制造系统设计、运行、性能评估、评价、分析、决策和控制。在这些活动中，可能需要对传统方法进行变革以适应智能制造系统。本文回顾了用于建立和维持所需系统性能的当前方法和工具。然后识别出数据和信息系统、集成、性能测量、分析以及性能改进方面的趋势，这些趋势对于确保智能制造系统的性能至关重要。最后，我们分析这些趋势如何应用于所研究的方法，并提出未来在不确定的多目标运行环境中评估和改进制造性能的研究方向。
