ManEx: The Visual Analysis of Measurements for the Assessment of Errors in Electrical Engines

Eirich, Joscha; Koutroulis, Georgios; Mutlu, Belgin; Jäckle, Dominik; Kern, Roman; Schreck, Tobias; Bernard, Jürgen

doi:10.1109/mcg.2022.3155306

Cited by 1 publication

(3 citation statements)

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“…The interview guideline was developed based on our theoretical lens, i.e., the effect of labels on OL. First, considering the dimension active context from the original OL framework (Figure 1), we asked questions on how participants define labels, the roles of the employees, and novel tools such as RfX (Eirich et al, 2022b), IRVINE (Eirich et al, 2021) or ManEx (Eirich et al, 2022a) in using labels or making decisions based on labels. Second, in the dimension task performance and experience, we asked, for instance, about the tasks that are supported by labels or which impediments exist in labeling tasks for organizational members and tools.…”

Section: Data Collection and Codingmentioning

confidence: 99%

“…To build models, ML developers need to understand complex domains, such as manufacturing processes (Eirich et al, 2022a) by consulting experts while domain experts need to understand the decisions of an ML model (van den Broek et al, 2021). The outcome or smallest unit of this mutual learning process are labels.…”

Section: Introductionmentioning

confidence: 99%

“…Nonetheless, VA and BI differ in the following aspects: VA tools are tailormade visual interfaces between human experts and ML models to support highly explorative analyses. Examples are the analysis of speech data with self-organizing maps or root cause analyses of manufacturing errors with causality graphs(Eirich et al, 2022a). Most VA tools are designed to store data in the form of formalized user feedback, which can range from simple text inputs to more abstract forms such as graphs, to continuously develop a VA tool's underlying model.…”

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confidence: 99%

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The Creation, Formalization, and Transfer of Expert Knowledge with Visual Analytics in Industrial Manufacturing Processes of Electrical Vehicles

Eirich¹

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The 21st century will be heavily impacted by the capability to create value from recorded data of all kinds. In this regard, industrial manufacturing intuitions increasingly rely on new data-driven technologies, such as the Internet of Things or Machine Learning. In terms of data collection, manufacturing processes increasingly include sophisticated sensor equipment, which results in interconnected networks of manufacturing all assembling parts and producing data. However, manufacturing institutions currently face two challenges. First, large amounts of parts and hence data are produced during fully automated manufacturing processes. Second, due to the overwhelming amount of recorded data, it is particularly challenging to efficiently analyze manufacturing data. Hence, it is important to efficiently store and share gained knowledge from performed data analyses. Information visualization and Visual Analytics are two prolific branches of data analysis exploiting sophisticated visualization techniques to support the execution of analytical tasks and to store gained knowledge. This thesis looks at how data visualization approaches can help industrial manufacturing organizations to create value from large amounts of manufacturing data, as well as how to efficiently store and share knowledge from manufacturing data analyses. The goal is to understand how Visual Analytics can improve manufacturing processes in the context of knowledge management. Five Visual Analytics systems were designed, developed, and evaluated to tackle different domain problems that emerged from manufacturing setups. Findings from each system were used to carry out additional studies to enhance established theories of knowledge management. As a result, five success stories are provided of how Visual Analytics can significantly improve manufacturing processes and how knowledge can be efficiently created, formalized, and shared in an organization with Visual Analytics.

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Section: Data Collection and Codingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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