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Part manufacturing under reduced workforce restrictions

Sebastian Schlund (ORCID: 0000-0002-8142-0255)
  • Grant DOI 10.55776/I6222
  • Funding program Principal Investigator Projects International
  • Status Ended
  • Start November 15, 2023
  • End December 14, 2025
  • Funding amount € 184,509

Weave

Disciplines

Computer Sciences (30%); Economics (70%)

Keywords

  • Part Manufacturing,
  • Manufacturing Engineering,
  • Human Centered Design,
  • Remote Assistance,
  • Teleoperation
Abstract Final report

One of the central elements of European industrial production is the manufacturing of finished parts from raw materials. Even in highly automated scenarios, the part manufacturing processes (e.g., milling) still require human interaction. Human workers are needed in the factories to ensure steady manufacturing and high part quality. Due to this dependency, part manufacturing is highly susceptible to reduced workforce situations as they occur, e.g., during pandemics like the previous COVID-19 outbreak. New modes of telework collaboration based on interactive systems that comprise visualization and communication technologies, collaborative robots, fast internet connections, and remote control of machine tools bear the potential to overcome these challenges. In the project PartReWork, we aim to find out how such modes and systems need to be designed to share tasks between teleworkers and on-site employees. We apply an interdisciplinary approach between manufacturing engineering, ergonomics/human factors, and human-computer interaction to find out how the concept of Human-centered Design (HCD) needs to be designed. After describing use-case scenarios, we will specify the user context and identify requirements. Afterward, we will develop solutions for the collaboration and evaluate them. Finally, the findings are generalized to form a framework that provides guidelines for manufacturers to partially relocate on-site manufacturing tasks to telework.

Part manufacturing is a central area of industrial production where products are manufactured through processes such as turning and milling. Despite increasing automation, continuous human intervention for tasks such as machine setup, process monitoring, maintenance, and quality control is required. This dependence on qualified personnel makes production systems vulnerable to workforce restrictions arising from pandemics or shortage of skilled workers. Visualization technologies, communication systems, collaborative robotics, and solutions for remote access and control offer potential to address these challenges. They enable the development of telecollaboration modes that can mitigate personnel shortages. The project "PartReWork" investigated how telework can be systematically integrated into part manufacturing. The project's objective was the design and validation of remote collaboration modes based on human-centered design principles. This included task analysis to identify the remote potential of various activities, task redesign for redistribution between on-site and telework, and ensuring usability through human-centered design methods. For implementation, use cases with varying automation levels were defined, technical systems were modeled, and task sequences were analyzed and evaluated regarding their telework suitability. Based on functional and non-functional requirements, design solutions were developed for collaboration modes that integrate remote assistance, remote maintenance, and remote control. Storyboards and wireframes served to visualize complex integration scenarios and enabled the derivation of technical requirements with human-centered focus. Iteratively, both implementations with reduced development effort based on existing infrastructure and advanced integration solutions were developed. The implemented approaches enable capturing the display and control panel information independent of automation level and without direct intervention in machine control. Evaluation was conducted through controlled user studies, usability assessments, and measurements of operational performance. Validation in various experimental scenarios showed that manually required on-site time could be reduced by 36% while productivity remained nearly constant at 99.6% to 99.9%. In multi-machine experiments, walking distances were reduced by 50%, and assistance experiments showed a marked reduction in assistance duration. Project results were transformed into a generalized framework that provides a methodology for integrating telework. The framework considers immediate implementation requirements and long-term development perspectives. The results demonstrate that telework in manufacturing does not replace traditional workflows but offers important opportunities to adapt production systems to operational challenges such as labor shortages.

Research institution(s)
  • Technische Universität Wien - 100%
Project participants
  • Margit Pohl, Technische Universität Wien , national collaboration partner
International project participants
  • Jan C. Aurich, Technische Universität Kaiserslauten - Germany, project partner

Research Output

  • 11 Citations
  • 4 Publications
  • 1 Datasets & models
  • 1 Fundings
Publications
  • 2025
    Title Your Fault or My Fault? Investigating Possible Error-Related Potential Differences Stemming from Internal/external Error Attribution in a Screen-Based Task
    DOI 10.1007/978-3-032-00815-2_20
    Type Book Chapter
    Author Kang T
    Publisher Springer Nature
    Pages 215-224
  • 2025
    Title Studying Dual-Task Awareness in Industrial Settings Through Reaction Times and Physiological Signals
    DOI 10.1109/cogsima64436.2025.11079508
    Type Conference Proceeding Abstract
    Author Eesee A
    Pages 151-156
  • 2026
    Title Design and validation of remote collaboration modes for part manufacturing under reduced workforce conditions
    DOI 10.1007/s11740-026-01430-w
    Type Journal Article
    Author Schworm P
    Journal Production Engineering
  • 2024
    Title I see artifacts: ICA-based EEG artifact removal does not improve deep network decoding across three BCI tasks
    DOI 10.1088/1741-2552/ad788e
    Type Journal Article
    Author Kang T
    Journal Journal of Neural Engineering
    Pages 066036
    Link Publication
Datasets & models
  • 2025 Link
    Title Dataset for "Studying Dual-Task Awareness in Industrial Settings Through Reaction Time and Physiological Signals"
    Type Database/Collection of data
    Public Access
    Link Link
Fundings
  • 2026
    Title FFG AIMROBIX
    Type Research grant (including intramural programme)
    Start of Funding 2026
    Funder Austrian Research Promotion Agency

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