A digital twin for intra-logistics process planning for the automotive sector supported by big data analytics

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

One of the areas that can heavily benefit with Industry 4.0 is the logistics, namely with the association of sensing technologies and the application of techniques such as Big Data Analytic, Data Visualization, prediction algorithms, and especially 3D simulation. The association of real data, prediction techniques, and 3D models, allow the creation of realistic Digital Twins that emulate factory processes, making possible the experimentation and testing of new ideas and different scenarios by tweaking key variables, without stopping production. However, there are many challenges in order to handle and compute all fast-growing, multi dimension data generated, so that all this production related data can be quickly used for defect control, preventive maintenance, advanced analytics for production and resources management, or even later simulation. The work presented in this paper focus in this “in between” processing work, presenting an easily deployable and self-reconfigurable Big Data architecture, where different technologies can work together to extract, transform, load, apply analytics, and then feed a 3D Digital Simulation model. The work presented in this paper is funded by the EU project BOOST4.0 and focus in a specific logistic process of car manufacturing.

Original languageEnglish
Title of host publicationASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE): Volume 2B: Advance Manufacturing
PublisherAmerican Society of Mechanical Engineers (ASME)
Number of pages7
ISBN (Electronic)9780791859384
DOIs
Publication statusPublished - 21 Jan 2020
EventASME 2019 International Mechanical Engineering Congress and Exposition, IMECE 2019 - Salt Lake City, United States
Duration: 11 Nov 201914 Nov 2019

Conference

ConferenceASME 2019 International Mechanical Engineering Congress and Exposition, IMECE 2019
CountryUnited States
CitySalt Lake City
Period11/11/1914/11/19

Keywords

  • Big Data
  • Digital Twin
  • Distributed Processing
  • Industry 4.0
  • Manufacturing Data
  • Swam Architecture

Fingerprint Dive into the research topics of 'A digital twin for intra-logistics process planning for the automotive sector supported by big data analytics'. Together they form a unique fingerprint.

  • Cite this

    Guerreiro, G., Figueiras, P., Costa, R., Marques, M., Graça, D., Garcia, G., & Jardim-Gonçalves, R. (2020). A digital twin for intra-logistics process planning for the automotive sector supported by big data analytics. In ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE): Volume 2B: Advance Manufacturing [IMECE2019-11362, V02BT02A021] American Society of Mechanical Engineers (ASME). https://doi.org/10.1115/IMECE2019-11362