Data analytics using simulation for smart manufacturing

Shao, Guodong; Shin, Seung‐Jun; Jain, Sanjay

doi:10.1109/wsc.2014.7020063

Cited by 84 publications

(57 citation statements)

References 11 publications

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“…In addition, DGAL fits into the framework of, but is more general than, Decision Guidance Management Systems proposed in [39]. Finally, the concept of centralized Analytical KB (AKB) is borrowed from our previous work on SPAF [6,25], which was limited to MP or CP optimization only. The results reported in this paper are only a first step toward reusability and modularity in SM analysis and optimization.…”

Section: Discussionmentioning

confidence: 99%

“…Diagnostic analytics includes such tasks as continuous testing for a significant statistical difference between the estimated vs. observed values of metrics, and direct application of fault classifiers to detect failures on the manufacturing floor. Research in diagnostic analytics in manufacturing can be found in [6,7]. Predictive analytics include techniques of stochastic simulation and statistical learning for regression, classification, and what-if estimation.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Analysis and optimization in smart manufacturing based on a reusable knowledge base for process performance models

Brodsky

Shao

Krishnamoorthy

et al. 2015

2015 IEEE International Conference on Big Data (Big Data)

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In this paper, we propose an architectural design and software framework for fast development of descriptive, diagnostic, predictive, and prescriptive analytics solutions for dynamic production processes. The proposed architecture and framework will support the storage of modular, extensible, and reusable Knowledge Base (KB) of process performance models. The approach requires the development of automatic methods that can translate the high-level models in the reusable KB into low-level specialized models required by a variety of underlying analysis tools, including data manipulation, optimization, statistical learning, estimation, and simulation. We also propose an organization and key structure for the reusable KB, composed of atomic and composite process performance models and domain-specific dashboards. Furthermore, we illustrate the use of the proposed architecture and framework by performing diagnostic tasks on a composite performance model.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis and optimization in smart manufacturing based on a reusable knowledge base for process performance models

Brodsky

Shao

Krishnamoorthy

et al. 2015

2015 IEEE International Conference on Big Data (Big Data)

Self Cite

View full text Add to dashboard Cite

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“…Simulation applications are projected to have wider applications in SMS. For example, Shao et al [85] demonstrated how simulation can be used to generate data to evaluate manufacturing data analytics applications. For this approach to be effective in real application, however, data-generating models require improved verification and validation methods.…”

Section: Computer Simulationmentioning

confidence: 99%

Methods and Tools for Performance Assurance of Smart Manufacturing Systems

Kibira

Morris

Kumaraguru

2016

J. RES. NATL. INST. STAN.

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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.

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“…Shao et al (2014) developed and validated a virtual model for generating energy usage data for machining operations. Furthering this research, Jain et al (2015) uses a two-tailed z-test to prove statistical concurrence of experimental results from a virtual factory at both the machine and manufacturing cell levels of detail.…”

Section: Related Work and Virtual Factory Validationmentioning

confidence: 99%

Virtual Factory Framework for Supporting Production Planning and Control

Kibira

Shao

2016

IFIP Advances in Information and Communication Technology

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Developing optimal production plans for smart manufacturing systems is challenging because shop floor events change dynamically. A virtual factory incorporating engineering tools, simulation, and optimization generates and communicates performance data to guide wise decision making for different control levels. This paper describes such a platform specifically for production planning. We also discuss verification and validation of the constituent models. A case study of a machine shop is used to demonstrate data generation for production planning in a virtual factory.

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Data analytics using simulation for smart manufacturing

Cited by 84 publications

References 11 publications

Analysis and optimization in smart manufacturing based on a reusable knowledge base for process performance models

Analysis and optimization in smart manufacturing based on a reusable knowledge base for process performance models

Methods and Tools for Performance Assurance of Smart Manufacturing Systems

Virtual Factory Framework for Supporting Production Planning and Control

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