A survey of visualization for smart manufacturing View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-04

AUTHORS

Fangfang Zhou, Xiaoru Lin, Chang Liu, Ying Zhao, Panpan Xu, Liu Ren, Tingmin Xue, Lei Ren

ABSTRACT

In smart manufacturing, people are facing an increasing amount of industrial data derived from various digitalized and connected sources in all kinds of formats. Analyzing and utilizing the data can support informed decision-making at different stages of the entire manufacturing life cycle. In recent years, visualization, as an important technology for understanding large and complex data, has been frequently introduced for industrial data analysis, empowering people with insights for process innovation and efficiency improvement. In this paper, we present a literature review of the visualization technologies specifically tailored for smart manufacturing applications. We propose a taxonomy to categorize the existing research based on application scenarios and industry sectors. We also introduce some concrete examples of applied research projects from different phases of the manufacturing life cycle and discuss the application features of several representative industries. Finally, we identify existing technical challenges and point out directions for future research. More... »

PAGES

419-435

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s12650-018-0530-2

DOI

http://dx.doi.org/10.1007/s12650-018-0530-2

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1110203599


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