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March 25, 2026Procedia Computer Science0 citationsOpen Access

Ensuring the quality of manual labour - flexible augmented-reality-based assistance for a rework process in the automobile assembly

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MHMatthias HauptvogelTHTina HaaseDBDirk Berndt

Key Points

  • This research aims to improve the quality of manual labor during the rework process in automobile assembly using augmented reality.
  • Developed an augmented reality-based assistance system for rework tasks.
  • Utilized digital twins for real-time tracking and guidance.
  • Conducted preliminary lab tests and gathered user feedback.
  • The system significantly reduces human error in assembly tasks.
  • Initial tests indicate higher efficiency and better outcomes in the rework process.
  • The solution shows potential for cost savings in assembly operations.

Abstract

The paper presents a flexible augmented-reality-based assistance system designed to enhance the quality of manual labour in automobile assembly, specifically focusing on a rework process during car door production at Audi AG. The research addresses the increasing complexity of manufacturing due to diverse vehicle models and customization demands, which complicate assembly tasks and quality control. The proposed digital assistance system automatically makes use of the digital twins of the product and the production system, enabling real-time guidance for the workers. By employing augmented reality visualizations, the system aims to reduce human error and to improve efficiency in manual tasks. The study details the development and implementation of the required modules of the assistance system, including real-time tracking and visualization of components. Preliminary lab tests and first user feedback suggest that the solution has the potential to effectively support workers in the analysed rework tasks. The application of the system is promising improved outcomes and cost savings for the assembly process. Future work will focus on further refinement of the system and validation of the performance assumptions in real production environments.

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Cite This Study

Hauptvogel et al. (2026) studied this question.

synapsesocial.com/papers/69c37acab34aaaeb1a67caf9https://doi.org/10.1016/j.procs.2026.02.283
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Assessing user performance in augmented reality assembly guidance for industry 4.0 operators2024 · 48 citations
  2. 2Placing Workers at the Center – Evaluating a Human-Centered Digital Assistance System in Automotive Assembly2026
  3. 3AssemblyMate: an interactive context-aware AI-XR co-worker with multimodal spatial-temporal reasoning for manufacturing assembly2026
  4. 4Application of augmented reality in automotive industry2024 · 34 citations
  5. 5Decision support for augmented reality-based assistance systems deployment in industrial settings2024 · 9 citations