How Digital Assistance Systems Improve Work Productivity in Assembly

Hinrichsen, Sven; Bendzioch, Sven

doi:10.1007/978-3-319-94334-3_33

Cited by 26 publications

(8 citation statements)

References 5 publications

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“…The workstation ( Figure 1A ) is additionally equipped with blue plastic containers for storing assembly parts and tools, and the Poka–Yoke system for automatic control of assembly activities and prevention of errors. Systems that help workers to perform assembly activities make it easier to perform these activities and enable the worker to reduce errors (Fast-Berglund et al, 2013 ) and increase productivity (Hinrichsen and Bendzioch, 2018 ). The installed Poka–Yoke system ( Figure 1B ) has 6 independent lines to supply 6 different key components of the product, which are equipped with modules for access to the control at the entrance as well as the exit of the line.…”

Section: Methodsmentioning

confidence: 99%

Development of Modular and Adaptive Laboratory Set-Up for Neuroergonomic and Human-Robot Interaction Research

et al. 2022

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The industry increasingly insists on academic cooperation to solve the identified problems such as workers' performance, wellbeing, job satisfaction, and injuries. It causes an unsafe and unpleasant working environment that directly impacts the quality of the product, workers' productivity, and effectiveness. This study aimed to give a specialized solution for tests and explore possible solutions to the given problem in neuroergonomics and human–robot interaction. The designed modular and adaptive laboratory model of the industrial assembly workstation represents the laboratory infrastructure for conducting advanced research in the field of ergonomics, neuroergonomics, and human–robot interaction. It meets the operator's anatomical, anthropometric, physiological, and biomechanical characteristics. Comparing standard, ergonomic, guided, and collaborative work will be possible based on workstation construction and integrated elements. These possibilities allow the industry to try, analyze, and get answers for an identified problem, the condition, habits, and behavior of operators in the workplace. The set-up includes a workstation with an industry work chair, a Poka–Yoke system, adequate lighting, an audio 5.0 system, containers with parts and tools, EEG devices (a cap and smartfones), an EMG device, touchscreen PC screen, and collaborative robot. The first phase of the neuroergonomic study was performed according to the most common industry tasks defined as manual, monotonous, and repetitive activities. Participants have a task to assemble the developed prototype model of an industrial product using prepared parts and elements, and instructed by the installed touchscreen PC. In the beginning, the participant gets all the necessary information about the experiment and gets 15 min of practice. After the introductory part, the EEG device is mounted and prepared for recording. The experiment starts with relaxing music for 5 min. The whole experiment lasts two sessions per 60 min each, with a 15 min break between the sessions. Based on the first experiments, it is possible to develop, construct, and conduct complex experiments for industrial purposes to improve the physical, cognitive, and organizational aspects and increase workers' productivity, efficiency, and effectiveness. It has highlighted the possibility of applying modular and adaptive ergonomic research laboratory experimental set-up to transform standard workplaces into the workplaces of the future.

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Section: Methodsmentioning

confidence: 99%

Development of Modular and Adaptive Laboratory Set-Up for Neuroergonomic and Human-Robot Interaction Research

et al. 2022

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“…In [14], the authors wrote about how the assistance systems increase productivity in assembly processes, and they formulated the principles of designing assembly assistance systems. They stated that it is unclear what problems might appear in the representation of information in manual assemblies or how these problems should be solved using assistive systems.…”

Section: Related Workmentioning

confidence: 99%

Towards an Assembly Support System with Dynamic Bayesian Network

et al. 2022

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Due to the new technological advancements and the adoption of Industry 4.0 concepts, the manufacturing industry is now, more than ever, in a continuous transformation. This work analyzes the possibility of using dynamic Bayesian networks to predict the next assembly steps within an assembly assistance training system. The goal is to develop a support system to assist the human workers in their manufacturing activities. The evaluations were performed on a dataset collected from an experiment involving students. The experimental results show that dynamic Bayesian networks are appropriate for such a purpose, since their prediction accuracy was among the highest on new patterns. Our dynamic Bayesian network implementation can accurately recommend the next assembly step in 50% of the cases, but to the detriment of the prediction rate.

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“…Zusätzlich sind diese Produkte vielfach mit einer Firmware zu versehen oder umfangreichen Funktionsprüfungen zu unterziehen [5]. Empirische Untersuchungen zur Informationsbereitstellung in der manuellen Montage zeigen, dass -in Ergänzung zu Hollnagel [26] -fünf Defizitkategorien unterschieden werden können [24]: 1) Benötigte Informationen fehlen im Montagesystem. 2) Es werden unnötige Informationen dargestellt.…”

Section: Ausgangssituationunclassified

“…Um die Einsatzpotenziale eines Assistenzsystems zu ermitteln, wurden Arbeits-und Zeitstudien in dem Pilotbereich des Unternehmens durchgeführt [24]. Der Arbeitsprozess der Rahmenmontage lässt sich grob in sechs Arbeitsschritte unterteilen [24]. Der erste Schritt ist die Auftragsvorbereitung.…”

Section: Analyse Der Ausgangssituationunclassified

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Informatorische Assistenzsysteme in der variantenreichen manuellen Montage

et al. 2020

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ZusammenfassungDie zunehmende Komplexität von manuellen Montageprozessen wirft in Unternehmen die Frage auf, wie die damit verbundenen Anforderungen an die Verarbeitung größerer Informationsmengen und die damit einhergehenden Zuwächse an kognitiven Beanspruchungen infolge zunehmender Unsicherheit seitens der Beschäftigten zu bewältigen sind. Eine Antwort hierauf liegt in der Integration von informatorischen Assistenzsystemen in bestehende Montagesysteme. Erklärtes Ziel solcher Assistenzsysteme ist die informatorische Unterstützung der Beschäftigten in der Form, dass in der Summe eine kognitive Entlastung erfolgt, die zu weniger Zeitverlusten und Montagefehlern und damit zu einer höheren Produktqualität führt. Dies setzt voraus, dass informatorische Assistenzsysteme nach kognitiv-ergonomischen Gesichtspunkten gestaltet sind und ihr Einsatz durch eine möglichst objektive Erfassung von kognitiven Beanspruchungen vor Ort am konkreten Arbeitsplatz begleitet wird.

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How Digital Assistance Systems Improve Work Productivity in Assembly

Cited by 26 publications

References 5 publications

Development of Modular and Adaptive Laboratory Set-Up for Neuroergonomic and Human-Robot Interaction Research

Development of Modular and Adaptive Laboratory Set-Up for Neuroergonomic and Human-Robot Interaction Research

Towards an Assembly Support System with Dynamic Bayesian Network

Informatorische Assistenzsysteme in der variantenreichen manuellen Montage

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