User-Guided Visual Analysis of Cyber-Physical Production Systems

Post, Tobias; Ilsen, Rebecca; Hamann, Bernd; Hagen, Hans; Aurich, Jan C.

doi:10.1115/1.4034872

Cited by 19 publications

(5 citation statements)

References 20 publications

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“…Currently, there is a lot of work being done in academia and industry towards visual analytics solutions to assist in the sense-making of the data [7]. There are a number of commercial business intelligence solutions that specialize in data discovery such as Tableau [8], Qlik Sense [9], Power BI [10], etc.…”

Section: Visual Analyticsmentioning

confidence: 99%

Implementing Visual Analytics Pipelines with Simulation Data

Khan¹,

Shakeel²,

Gul³

et al. 2022

Software Usability

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Visual analytics has been widely studied in the past decade both in academia and industry to improve data exploration, minimize the overall cost, and improve data analysis. In this chapter, we explore the idea of visual analytics in the context of simulation data. This would then provide us with the capability to not only explore our data visually but also to apply machine learning models in order to answer high-level questions with respect to scheduling, choosing optimal simulation parameters, finding correlations, etc. More specifically, we examine state-of-the-art tools to be able to perform these above-mentioned tasks. Further, to test and validate our methodology we followed the human-centered design process to build a prototype tool called ViDAS (Visual Data Analytics of Simulated Data). Our preliminary evaluation study illustrates the intuitiveness and ease-of-use of our approach with regards to visual analysis of simulated data.

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Section: Visual Analyticsmentioning

confidence: 99%

Implementing Visual Analytics Pipelines with Simulation Data

Khan¹,

Shakeel²,

Gul³

et al. 2022

Software Usability

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“…Jo et al [12] contributed a novel visualization for ongoing tasks in the production line with an extended Gantt chart. Post et al [13] facilitated user-guided visual analysis for flow, workload, and stacked graphs in a production line. In [14], Arbesser et al developed a visual data quality assessment for time series data.…”

Section: Smart Factory Monitoring Dashboardsmentioning

confidence: 99%

Evaluation of the Flourish Dashboard for Context-Aware Fault Diagnosis in Industry 4.0 Smart Factories

2022

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Cyber-physical systems become more complex, therewith production lines become more complex in the smart factory. Every employed system produces high amounts of data with unknown dependencies and relationships, making incident reasoning difficult. Context-aware fault diagnosis can unveil such relationships on different levels. A fault diagnosis application becomes context-aware when the current production situation is used in the reasoning process. We have already published TAOISM, a visual analytics model defining the context-aware fault diagnosis process for the Industry 4.0 domain. In this article, we propose the Flourish dashboard for context-aware fault diagnosis. The eponymous visualization Flourish is a first implementation of a context-displaying visualization for context-aware fault diagnosis in an Industry 4.0 setting. We conducted a questionnaire and interview-based bilingual evaluation with two user groups based on contextual faults recorded in a production-equal smart factory. Both groups provided qualitative feedback after using the Flourish dashboard. We positively evaluate the Flourish dashboard as an essential part of the context-aware fault diagnosis and discuss our findings, open gaps, and future research directions.

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“…Wörner et al [45] visualized the simulation run of the assembly line to identify potential congestion during the manufacturing process. Post et al [30] developed a visualization system to show temporal changes in the simulated production process.…”

Section: Manufacturing Data Visualizationmentioning

confidence: 99%

PlanningVis: A Visual Analytics Approach to Production Planning in Smart Factories

Dong

Huang

Chen

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. PlanningVis supports interactive exploration, comparison and optimization of production plans. (a) The control panel enables interactively building the configuration data of the planning algorithm. (b) The plan overview summarizes each plan and their differences. (c) The product view reveals the distribution of all the products (c 1 ) and presents various properties of the selected products (c 2 ). (d) The production detail view displays the dependency between products (d 1 , d 2 ) and the daily production details in related plants (d 3 , d 4 ).Abstract-Production planning in the manufacturing industry is crucial for fully utilizing factory resources (e.g., machines, raw materials and workers) and reducing costs. With the advent of industry 4.0, plenty of data recording the status of factory resources have been collected and further involved in production planning, which brings an unprecedented opportunity to understand, evaluate and adjust complex production plans through a data-driven approach. However, developing a systematic analytics approach for production planning is challenging due to the large volume of production data, the complex dependency between products, and unexpected changes in the market and the plant. Previous studies only provide summarized results and fail to show details for comparative analysis of production plans. Besides, the rapid adjustment to the plan in the case of an unanticipated incident is also not supported. In this paper, we propose PlanningVis, a visual analytics system to support the exploration and comparison of production plans with three levels of details: a plan overview presenting the overall difference between plans, a product view visualizing various properties of individual products, and a production detail view displaying the product dependency and the daily production details in related factories. By integrating an automatic planning algorithm with interactive visual explorations, PlanningVis can facilitate the efficient optimization of daily production planning as well as support a quick response to unanticipated incidents in manufacturing. Two case studies with real-world data and carefully designed interviews with domain experts demonstrate the effectiveness and usability of PlanningVis.

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User-Guided Visual Analysis of Cyber-Physical Production Systems

Cited by 19 publications

References 20 publications

Implementing Visual Analytics Pipelines with Simulation Data

Implementing Visual Analytics Pipelines with Simulation Data

Evaluation of the Flourish Dashboard for Context-Aware Fault Diagnosis in Industry 4.0 Smart Factories

PlanningVis: A Visual Analytics Approach to Production Planning in Smart Factories

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