Methods and Tools for Performance Assurance of Smart Manufacturing Systems

被引:34
作者
Kibira, Deogratias [1 ]
Morris, K. C. [2 ]
Kumaraguru, Senthilkumaran [3 ]
机构
[1] Morgan State Univ, Baltimore, MD 21251 USA
[2] NIST, Informat Modeling & Testing Grp, Syst Integrat Div, Engn Lab, Gaithersburg, MD 20899 USA
[3] Indian Inst Informat Technol Design & Manufacture, Madras 60012, Tamil Nadu, India
关键词
smart manufacturing; manufacturing performance methods; manufacturing performance challenges; performance measurement; productivity; sustainability; agility; DECISION-SUPPORT; BIG DATA; MANAGEMENT; ANALYTICS; FRAMEWORK; CHALLENGES; SIMULATION; DESIGN;
D O I
10.6028/jres.121.013
中图分类号
TH7 [仪器、仪表];
学科分类号
0804 ; 080401 ; 081102 ;
摘要
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.
引用
收藏
页码:282 / 313
页数:32
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