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Research Papers

User-Guided Visual Analysis of Cyber-Physical Production Systems

[+] Author and Article Information
Tobias Post

Computer Graphics and HCI Group,
University of Kaiserslautern,
Kaiserslautern 67653, Germany
e-mail: tpost@rhrk.uni-kl.de

Rebecca Ilsen, Jan C. Aurich

Institute for Manufacturing Technology
and Production Systems,
University of Kaiserslautern,
Kaiserslautern 67653, Germany

Bernd Hamann

Department of Computer Science,
University of California (UC Davis),
Davis, CA 95616

Hans Hagen

Computer Graphics and HCI Group,
University of Kaiserslautern,
Kaiserslautern 67653, Germany

Contributed by the Design Engineering Division of ASME for publication in the JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING. Manuscript received September 29, 2015; final manuscript received September 18, 2016; published online February 16, 2017. Editor: Bahram Ravani.

J. Comput. Inf. Sci. Eng 17(2), 021005 (Feb 16, 2017) (8 pages) Paper No: JCISE-15-1310; doi: 10.1115/1.4034872 History: Received September 29, 2015; Revised September 18, 2016

Modern cyber-physical production systems (CPPS) connect different elements like machine tools and workpieces. The constituent elements are often equipped with high-performance sensors as well as information and communication technology, enabling them to interact with each other. This leads to an increasing amount and complexity of data that requires better analysis tools to support system refinement and revision performed by an expert. This paper presents a user-guided visual analysis approach that can answer relevant questions concerning the behavior of cyber-physical systems. The approach generates visualizations of aggregated views that capture an entire production system as well as specific characteristics of individual data features. To show the applicability of the presented methodologies, an exemplary production system is simulated and analyzed.

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Figures

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Fig. 1

Flow view of a virtual production system showing the geometric model of the factory and its machines, the product flow for all products color-coded by product type, and the machine workloads for all machines

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Fig. 2

Detailed views of the product flow demonstrating the visualization of different properties like product types (left image), methods to reduce visual clutter (middle image), and waiting times of the products (right image)

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Fig. 3

Sequence of product flows for a short temporal window moving forward in time (left to right image), resulting in products moving through the virtual factory

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Fig. 4

Unsorted (top image) and sorted (bottom image) workload of the first drilling machine showing the development of the machine's queue with its individual products waiting to be processed

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Fig. 5

Workload view showing a workload for each machine in the virtual factory, thereby guiding the user to machines potentially being overloaded or redundant at certain interesting points in time

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Fig. 6

Production view for all products of type C showing inactive and processing phases of their three operations under different aspects of manufacturing time, manufacturing begin, and degree of completion

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