Högskolan i Skövde

his.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • apa-cv
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Enabling cyber-physical system for manufacturing systems using augmented reality
University of Skövde, School of Engineering Science.
2023 (English)Independent thesis Advanced level (degree of Master (One Year)), 12 credits / 18 HE creditsStudent thesis
Abstract [en]

This project focuses on addressing the challenges faced by manufacturing lines such as complexity and flexibility through the integration of Augmented Reality (AR), Internet of Things (IoT), and Big Data technologies. The objective is to develop a framework that enhances the efficiency, flexibility, and sustainability of manufacturing processes in the context of Industry 4.0. 

The project involves the design and implementation of an artifact solution using the UNITY platform. The solution enables users to remotely control and monitor a manufacturing line in real-time through an AR interface. By taking advantage of leveraging IoT devices and sensors, real-time data is collected from the production line, providing valuable insights into performance, maintenance needs, and resource optimization. The collected data is processed and analyzed using Big Data techniques, enabling predictive maintenance, quality control, and optimization of manufacturing processes. 

The outcomes of this project will provide valuable insights into the potential of AR, IoT, and Big Data technologies in revolutionizing the manufacturing industry. The artifact solution serves as a proof-of-concept, demonstrating the feasibility and benefits of adopting these technologies for sustainable manufacturing in the context of Industry 4.0. Future research and development can build upon this work to further refine and scale the solution for broader industrial applications.

Place, publisher, year, edition, pages
2023. , p. ix, 50
Keywords [en]
Augmented reality, IoT, big data, sustainable manufacturing, industry 4.0
National Category
Mechanical Engineering
Identifiers
URN: urn:nbn:se:his:diva-22917OAI: oai:DiVA.org:his-22917DiVA, id: diva2:1778395
Subject / course
Virtual Product Realization
Educational program
Intelligent Automation - Master's Programme, 60 ECTS
Supervisors
Examiners
Available from: 2023-07-01 Created: 2023-07-01 Last updated: 2023-07-01Bibliographically approved

Open Access in DiVA

fulltext(4313 kB)88 downloads
File information
File name FULLTEXT01.pdfFile size 4313 kBChecksum SHA-512
613ee781b528db10290a04da918aadc128cbf91a8aeeaca1cb32a570f5b3376d9d1050443cb63af6012959e294953c175536042741a66259de110e77799a82fa
Type fulltextMimetype application/pdf

By organisation
School of Engineering Science
Mechanical Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 88 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 97 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • apa-cv
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf