Designing wearable devices for the factory: Rapid contextual experience prototyping

Osswald, Sebastian; Weiss, Astrid; Tscheligi, Manfred

doi:10.1109/cts.2013.6567280

Cited by 15 publications

(13 citation statements)

References 13 publications

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“…Data visualization in a spatio-temporal manner, using geovisual analytics, enables humans to identify patterns (Compieta et al, 2007;Andrienko et al, 2007;von Landesberger et al, 2016). Near real-time visualization could be of potential interest thanks to the emergence of wearable devices for employees and managers (Osswald et al, 2013). These devices allow a virtual view of performance and states of the manufacturing environment.…”

Section: Relevant Workmentioning

confidence: 99%

Spatially-Linked Manufacturing Data to Support Data Analysis

Schabus¹,

Scholz²

2017

giforum

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The paper presents a Linked Data approach within a manufacturing organization to foster sharing, reusing, integrating and the collaborative analysis of datasets originating from different business units and heterogeneous data sources. The paper relies on a semiconductor company that serves as case study. The authors elaborate on manufacturing data and their representation in a spatially-enabled graph database, and as Linked Data based on an ontology describing the indoor space and production processes. A graph database enables data sharing as well as the semantic search and retrieval of data utilizing web-based services. The results present the analysis of historic, future and spatio-temporal data as well as the analysis of similarities of semanticallyannotated linked manufacturing data.

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Section: Relevant Workmentioning

confidence: 99%

Spatially-Linked Manufacturing Data to Support Data Analysis

Schabus¹,

Scholz²

2017

giforum

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“…In the literature, there are several approaches to increase the efficiency of manufacturing lines [6,15,16]. To utilize optimization results [17] describe wearable devices for managers and employees of manufacturing companies. The authors of [4] propose that manufacturing decision making can be supported by adding the dimensions space and time to production related data.…”

Section: Relevant Workmentioning

confidence: 99%

“…The manufacturing context in this paper is a semiconductor production facility, which is special, due to the cleanroom conditions. This section relies on the work evident in studies [8,9,17].…”

Section: Indoor Manufacturing Space and Manufacturing Processesmentioning

confidence: 99%

Towards an Affordance-Based Ad-Hoc Suitability Network for Indoor Manufacturing Transportation Processes

Scholz

Schabus

2017

IJGI

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Abstract:In manufacturing companies, productivity and efficiency are the main priorities, besides an emphasis on quality issues. The outcome of this research contributes to increasing production quality and efficiency in manufacturing. The article deals with indoor manufacturing environments and the transportation processes of production assets-referred to as smart transportation. The authors modelled the objects present in the indoor manufacturing environment with ontologies including their affordances and spatial suitability. To support flexible production and dynamic transportation processes have to be tailored towards the 'needs' of the production asset. Hence, the authors propose an approach utilizing an ad-hoc suitability network to support the "optimal" path computation for transportation processes. The objective is to generate a graph for routing purposes for each individual production asset, with respect to the affordances of the indoor space for each production asset, and measurements of a sensor network. The generation of the graph follows an ad-hoc strategy, in two ways. First, the indoor navigation graph is created exactly when a path needs to be found-when a production asset shall be transported to the next manufacturing step. Secondly, the transportation necessities of each production asset, as well as any disturbances present in the environment, are taken into account at the time of the path calculation. The novelty of this approach is that the development of the navigation graph-including the weights-is done with affordances, which are based on an ontology. To realize the approach, the authors developed a linked data approach based on manufacturing data and on an application ontology, linking the indoor manufacturing environment and a graph-based network. The linked data approach is finally implemented as a spatial graph database containing walkable corridors, production equipment, assets and a sensor network. The results show the optimal path for transportation processes with respect to affordances of the indoor manufacturing environments. An evaluation of the computational complexity shows that the affordance-based ad-hoc graphs are thinner and thus reduce the computational complexity of shortest path calculations. Hence, we conclude that an affordance-based approach can help to decrease computational efforts for calculating "optimal" paths for transportation purposes.

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“…By applying methods of geo-visual analytics, humans can be supported in identifying patterns within the given data [29,30]. In addition, near real-time visualization of manufacturing data is of interest due to the emergence of wearable devices for employees and managers [31]. Identifying spatial-temporal patterns may help to develop optimization strategies and technologies to increase manufacturing efficiency, i.e., cost savings and increased performance [32].…”

Section: Related Workmentioning

confidence: 99%

Mapping Parallels between Outdoor Urban Environments and Indoor Manufacturing Environments

Schabus

Scholz

Lampoltshammer

2017

IJGI

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Abstract:The concepts of "Smart Cities" and "Smart Manufacturing" are different data-driven domains, although both rely on intelligent information technology and data analysis. With the application of linked data and affordance-based approaches, both domains converge, paving the way for new and innovative viewpoints regarding the comparison of urban tasks with indoor manufacturing tasks. The present study builds on the work, who state that cities are scaled versions of each other, by extending this thesis towards indoor manufacturing environments. Based on their structure and complexity, these environments are considered to form ecosystems of their own, comparable to "small cities". This conceptual idea is demonstrated by examining the process of human problem-solving in transportation situations from both perspectives (i.e., city-level and manufacturing-level). In particular, the authors model tasks of human operators that are used to support transportation processes in indoor manufacturing environments based on affordances and spatial-temporal data. This paper introduces the fundamentals of the transformation process of outdoor tasks and process planning activities to indoor environments, particularly to semiconductor manufacturing environments. The idea is to examine the mapping of outdoor tasks and applications to indoor environments, and vice-versa, based on an example focusing on the autonomous transportation of production assets in a manufacturing environment. The approach is based on a spatial graph database, populated with an indoor navigation ontology and instances of indoor and outdoor objects. The results indicate that human problem-solving strategies can be applied to indoor manufacturing environments to support decision-making in autonomous transportation tasks.

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Designing wearable devices for the factory: Rapid contextual experience prototyping

Cited by 15 publications

References 13 publications

Spatially-Linked Manufacturing Data to Support Data Analysis

Spatially-Linked Manufacturing Data to Support Data Analysis

Towards an Affordance-Based Ad-Hoc Suitability Network for Indoor Manufacturing Transportation Processes

Mapping Parallels between Outdoor Urban Environments and Indoor Manufacturing Environments

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