An Empirical Study of Assembly Error Detection Using an Augmented Vision System

Odenthal, Barbara; Mayer, Monique N.; Kabuß, Wolfgang; Kausch, Bernhard; Schlick, Christopher

doi:10.1007/978-3-642-02771-0_66

Cited by 2 publications

(2 citation statements)

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“…To assist the human operator within this novel automation concept, additional laboratory studies of a selfdeveloped augmented vision system for error detection in assembled parts were carried out [29]. However, the augmented vision system has to be extended by a real-time decision-support function based on SOAR.…”

Section: Discussionmentioning

confidence: 99%

Automation of robotic assembly processes on the basis of an architecture of human cognition

Mayer

Schlick

Ewert

et al. 2011

Prod. Eng. Res. Devel.

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A novel concept to cognitive automation of robotic assembly processes is introduced. An experimental assembly cell with two robots was designed to verify and validate the concept. The cell's numerical control-termed a cognitive control unit (CCU)-is able to simulate human information processing at a rule-based level of cognitive control. To enable the CCU to work on a large range of assembly tasks expected of a human operator, the cognitive architecture SOAR is used. On the basis of a self-developed set of production rules within the knowledge base, the CCU can plan assembly processes autonomously and react to adhoc changes in assembly sequences effectively. Extensive simulation studies have shown that cognitive automation based on SOAR is especially suitable for random parts supply, which reduces planning effort in logistics. Conversely, a disproportional increase in processing time was observed for deterministic parts supply, especially for assemblies containing large numbers of identical parts.

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Section: Discussionmentioning

confidence: 99%

Automation of robotic assembly processes on the basis of an architecture of human cognition

Mayer

Schlick

Ewert

et al. 2011

Prod. Eng. Res. Devel.

Self Cite

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“…By displaying a color overlay of the user's hands, feedback can be given without occluding task-relevant objects. Other authors described mechanisms to detect and prevent human and machine errors, using computer vision, machine learning, and remote assistance methods [40,43,59,64]. In a parallel line of research, Bortolini et al [7] investigated the impact of digital technologies in assembly system design and management.…”

Section: Literature Reviewmentioning

confidence: 99%

Cross reality to enhance worker cognition in industrial assembly operations

Simões

Amicis

Barandiarán

et al. 2019

Int J Adv Manuf Technol

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In this paper, we discuss how Cross Reality (XR) and the Industrial Internet of Things (IIoT) can support assembly tasks in hybrid human-machine manufacturing lines. We describe a Cross Reality system, designed to improve efficiency and ergonomics in industrial environments that require manual assembly operations. Our objective is to reduce the high costs of authoring assembly manuals and to improve the process of skills transfer, in particular, in assembly tasks that include workers with disabilities. The automation of short-lived assembly tasks, i.e., manufacturing of limited batches of customized products, does not yield significant returns considering the automation effort necessary and the production time frame. In the design of our XR system, we discuss how aspects of content creation can be automated for short-lived tasks and how seamless interoperability between devices facilitates skills transfer in human-machine hybrid environments.

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An Empirical Study of Assembly Error Detection Using an Augmented Vision System

Cited by 2 publications

References 5 publications

Automation of robotic assembly processes on the basis of an architecture of human cognition

Automation of robotic assembly processes on the basis of an architecture of human cognition

Cross reality to enhance worker cognition in industrial assembly operations

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