Perspectives on Assistive Systems for Manual Assembly Tasks in Industry

Wolfartsberger, Josef; Haslwanter, Jean D. Hallewell; Lindorfer, Rene

doi:10.3390/technologies7010012

Cited by 19 publications

(4 citation statements)

References 26 publications

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“…Yet, AR is not the only assisting tool in the industrial sector. Collaborative robots are also used and researched (Wolfartsberger 2019). Our interest, however, is in AR service and maintenance systems.…”

Section: Ar For Service and Maintenance Supportmentioning

confidence: 99%

Human-Centered Development and Evaluation of an AR-Assistance System to Support Maintenance and Service Operations at LNG Ship Valves

Stern¹,

Leder²,

Lütjen³

2021

Competence Development and Learning Assistance Systems for the Data-Driven Future

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The use of LNG propulsion in ships has significant environmental benefits but also creates challenges in handling the LNG. Due to the safety regulations, the maintenance of the LNG ship systems is essential and requires a high degree of reliability and accuracy. Here, digital assistance systems, e.g., based on Augment-ed Reality (AR) technology, can support the maintenance and service purposes of LNG ship systems by displaying additional information directly on the objects to be serviced. Thus, this paper deals with the development and evaluation of a digital assistance system using AR technology. The developed assistance system based on an Android smartphone enables users to access maintenance instruc-tions and manuals, simplifies spare parts' ordering, and supports the work pro-cess step by step through context-sensitive virtualizations. Its evaluation was conducted as a combined quantitative and qualitative user study. Overall, the assistance system offers promising potentials for reducing workload and improv-ing processes.

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Section: Ar For Service and Maintenance Supportmentioning

confidence: 99%

Human-Centered Development and Evaluation of an AR-Assistance System to Support Maintenance and Service Operations at LNG Ship Valves

Stern¹,

Leder²,

Lütjen³

2021

Competence Development and Learning Assistance Systems for the Data-Driven Future

View full text Add to dashboard Cite

show abstract

“…The researchers point out that HMDs, which are the most researched AR hardware [10], are neither robust nor can they be worn with protective gear. Additionally, Wolfartsberger et al [15] also noted that the current generation of HMDs still has many disadvantages and recommend using a fixed tablet screen or in-situ projection.…”

Section: Related Workmentioning

confidence: 99%

Integrative Object and Pose to Task Detection for an Augmented-Reality-based Human Assistance System using Neural Networks

Kästner

Eversberg

Mursa

et al. 2021

2020 IEEE Eighth International Conference on Communications and Electronics (ICCE)

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As a result of an increasingly automatized and digitized industry, processes are becoming more complex. Augmented Reality has shown considerable potential in assisting workers with complex tasks by enhancing user understanding and experience with spatial information. However, the acceptance and integration of AR into industrial processes is still limited due to the lack of established methods and tedious integration efforts. Meanwhile, deep neural networks have achieved remarkable results in computer vision tasks and bear great prospects to enrich Augmented Reality applications . In this paper, we propose an Augmented-Reality-based human assistance system to assist workers in complex manual tasks where we incorporate deep neural networks for computer vision tasks. More specifically, we combine Augmented Reality with object and action detectors to make workflows more intuitive and flexible. To evaluate our system in terms of user acceptance and efficiency, we conducted several user studies. We found a significant reduction in time to task completion in untrained workers and a decrease in error rate. Furthermore, we investigated the users learning curve with our assistance system.

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“…Effectiveness was investigated in terms of comparison of overall execution task time, mental demand, physical demand, errors, etc. In the paper of Wolfartsberger et al [46], the authors point out that many technologies are not yet at a level that they can be implemented in practice, therefore they gave a review of the current technologies that support manuals assembly activities and a review on the future perspective of these technologies. Observed technologies that support assembly tasks with respect to their practical implementation in companies are HRC and instructive assistance systems (mixed and virtual reality).…”

Section: Motivation For the Proposed Proceduresmentioning

confidence: 99%

Optimization of Industry 4.0 Implementation Selection Process towards Enhancement of a Manual Assembly Line

et al. 2021

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Last year’s developments are characterized by a dramatic drop in customer demand leading to stiff competition and more challenges that each enterprise needs to cope with in a globalized market. Production in low-mix/high-volume batches is replaced with low-volume/high-variety production, which demands excessive information flow throughout production facilities. To cope with the excessive information flow, this production paradigm requires the integration of new advanced technology within production that enables the transformation of production towards smart production, i.e., towards Industry 4.0. The procedure that helps the decision-makers to select the most appropriate I4.0 technology to integrate within the current assembly line considering the expected outcomes of KPIs are not significantly been the subject of the research in the literature. Therefore, this research proposes a conceptual procedure that focus on the current state of the individual assembly line and proposes the technology to implement. The proposed solution is aligned with the expected strategic goals of the company since procedure takes into consideration value from the end-user perspective, current production plans, scheduling, throughput, and other relevant manufacturing metrics. The validation of the method was conducted on a real assembly line. The results of the validation study emphasize the importance of the individual approach for each assembly line since the preferences of the user as well as his diversified needs and possibilities affect the optimal technology selection.

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Perspectives on Assistive Systems for Manual Assembly Tasks in Industry

Cited by 19 publications

References 26 publications

Human-Centered Development and Evaluation of an AR-Assistance System to Support Maintenance and Service Operations at LNG Ship Valves

Human-Centered Development and Evaluation of an AR-Assistance System to Support Maintenance and Service Operations at LNG Ship Valves

Integrative Object and Pose to Task Detection for an Augmented-Reality-based Human Assistance System using Neural Networks

Optimization of Industry 4.0 Implementation Selection Process towards Enhancement of a Manual Assembly Line

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