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Automatic Classification for Anti Mixup Events in Advanced Manufacturing System

[+] Author Affiliations
Marina Paolanti, Emanuele Frontoni, Adriano Mancini, Roberto Pierdicca, Primo Zingaretti

Università Politecnica delle Marche, Ancona, Italy

Paper No. DETC2015-46303, pp. V009T07A061; 6 pages
  • ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 9: 2015 ASME/IEEE International Conference on Mechatronic and Embedded Systems and Applications
  • Boston, Massachusetts, USA, August 2–5, 2015
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5719-9
  • Copyright © 2015 by ASME


The mix-up is a phenomenon in which a tablet/capsule gets into a different package. It is an annoying problem because mixing different products in the same package could result dangerous for consumers that take the incorrect product or receive an unintended ingredient. So, the consequences could be very dangerous: overdose, interaction with other medications a consumer may be taking, or an allergic reaction. The manufacturers are not able to guarantee the contents of the packages and so for this reason they are very exposed to the risk in which users rightly want to obtain compensation for possible damages caused by the mix-up. The aim of this work is the identification of mix-up events, through machine learning approach based on data, coming from different embedded systems installed in the manufacturing facilities and from the information system, in order to implement integrated policies for data analysis and sensor fusion that leads to waste and detection of pieces that do not comply. In this field, two types of approaches from the point of view of embedded sensors (optical and NIR vision and interferometry) will be analyzed focusing in particular on data processing and their classification on advanced manufacturing scenarios. Results are presented considering a simulated scenario that uses pre-recorded real data to test, in a preliminary stage, the effectiveness and the novelty of the proposed approach.

Copyright © 2015 by ASME



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