Issue No. 02 - Mar.-Apr. (2015 vol. 35)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/MCG.2015.38
Evan Shellshear , Fraunhofer-Chalmers Center for Industrial Mathematics, Sweden
Rolf Berlin , Fraunhofer-Chalmers Center for Industrial Mathematics, Sweden
Johan S. Carlson , Fraunhofer-Chalmers Center for Industrial Mathematics, Sweden
Many industry leaders have recognized the need to reinvent the way factories operate. This has led to the Industrie 4.0 and Industrial Internet initiatives, which advocate the use of cyber-physical systems to provide customers with personalized products. The authors present a data-driven approach based on the idea of a digital factory--that is, a virtual representation of production plants and processes that can facilitate virtual analysis. Specifically, the proposed tools can help decision makers assess existing assembly lines for their maximum degree of customization using up-to-date and physically accurate models of their factories. The authors achieve this by maintaining up-to-date information about a factory floor via incremental laser scanning of structural changes. They then combine this with simulation tools to compute maximum design volumes to guarantee collision-free and product-customizable production lines. They also present a case study that examines the reusability of a rust treatment facility for multiple car chassis models at Volvo.
Visual analytics, Computer vision, Visual computing, Laser scanning, Simulation, Manufacturing
E. Shellshear, R. Berlin and J. S. Carlson, "Maximizing Smart Factory Systems by Incrementally Updating Point Clouds," in IEEE Computer Graphics and Applications, vol. 35, no. 2, pp. 62-69, 2015.