poster

An Haptic Interface for Industrial High-Precision Manufacturing Tasks

Authors:

Sergio Di Martino,

Vincenzo Norman VitaleAuthors Info & Claims

AVI '20: Proceedings of the 2020 International Conference on Advanced Visual Interfaces

Article No.: 77, Pages 1 - 3

https://doi.org/10.1145/3399715.3399952

Published: 02 October 2020 Publication History

Get Access

Abstract

Within the Industry 4.0 context, a great number of machineries has been equipped with multiple sensors collecting vast amounts of heterogeneous data, including multimedia ones. In the context of high-precision industrial manufacturing, the output of these sensors can be exploited to leverage on human intelligence for monitoring the quality of the production. Nevertheless, in complex scenarios, the amount of sensed data could lead to a visual and acoustic overload for the Decision Maker. In this poster we propose a multi-modal user interface (UI) we devised to support the Decision Maker in monitoring the outcome of high-precision manufacturing machineries. In particular, to mitigate the acoustic and visual overloads, we propose the use of the haptic channel, both to control the playback of the collected data stream, and to get feedbacks about anomalous situations.

References

[1]

Baotong Chen, Jiafu Wan, Lei Shu, Peng Li, Mithun Mukherjee, and Boxing Yin. 2017. Smart factory of industry 4.0: Key technologies, application case, and challenges. IEEE Access 6 (2017), 6505--6519.

Crossref

Google Scholar

[2]

Sergio Di Martino, Luca Fiadone, Adriano Peron, Alberto Riccabone, and Vincenzo Norman Vitale. 2019. Industrial Internet of Things: Persistence for Time Series with NoSQL Databases. In 2019 IEEE 28th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE). IEEE, 340--345.

Google Scholar

[3]

Soumaya El Kadiri, Bernard Grabot, Klaus-Dieter Thoben, Karl Hribernik, Christos Emmanouilidis, Gregor Von Cieminski, and Dimitris Kiritsis. 2016. Current trends on ICT technologies for enterprise information systems. Computers in Industry 79 (2016), 14--33.

Digital Library

Google Scholar

[4]

Nivan Ferreira, Jorge Poco, Huy T Vo, Juliana Freire, and Cláudio T Silva. 2013. Visual exploration of big spatio-temporal urban data: A study of new york city taxi trips. IEEE transactions on visualization and computer graphics 19, 12 (2013), 2149--2158.

Google Scholar

[5]

Frederick D Gregory and Liyi Dai. 2015. Multisensory information processing for enhanced human-machine symbiosis. In International Conference on Human Interface and the Management of Information. Springer, 354--365.

Crossref

Google Scholar

[6]

Mario Hermann, Tobias Pentek, and Boris Otto. 2016. Design principles for industrie 4.0 scenarios. In 2016 49th Hawaii international conference on system sciences (HICSS). IEEE, 3928--3937.

Digital Library

Google Scholar

[7]

Byungkyu Kim, Hyunjae Kang, Deok-Ho Kim, and Jong-Oh Park. 2006. A flexible microassembly system based on hybrid manipulation scheme for manufacturing photonics components. The International Journal of Advanced Manufacturing Technology 28, 3--4 (2006), 379--386.

Crossref

Google Scholar

[8]

Heiner Lasi, Peter Fettke, Hans-Georg Kemper, Thomas Feld, and Michael Hoffmann. 2014. Industry 4.0. Business & information systems engineering 6, 4 (2014), 239--242.

Google Scholar

[9]

Jay Lee, Hossein Davari Ardakani, Shanhu Yang, and Behrad Bagheri. 2015. Industrial big data analytics and cyber-physical systems for future maintenance & service innovation. Procedia CIRP 38 (2015), 3--7.

Crossref

Google Scholar

[10]

Tim Niesen, Constantin Houy, Peter Fettke, and Peter Loos. 2016. Towards an integrative big data analysis framework for data-driven risk management in industry 4.0. In 2016 49th Hawaii International Conference on System Sciences (HICSS). IEEE, 5065--5074.

Digital Library

Google Scholar

[11]

Camilla Robino, Laura Di Rocco, Sergio Di Martino, Giovanna Guerrini, and Michela Bertolotto. 2018. Multigranular spatio-temporal exploration: An application to on-street parking data. In International Symposium on Web and Wireless Geographical Information Systems. Springer, 90--100.

Digital Library

Google Scholar

[12]

Konstantinos Sipsas, Kosmas Alexopoulos, Vangelis Xanthakis, and George Chryssolouris. 2016. Collaborative maintenance in flow-line manufacturing environments: An Industry 4.0 approach. Procedia Cirp 55 (2016), 236--241.

Google Scholar

[13]

Muhammad Syafrudin, Ganjar Alfian, Norma Latif Fitriyani, and Jongtae Rhee. 2018. Performance analysis of IoT-based sensor, big data processing, and machine learning model for real-time monitoring system in automotive manufacturing. Sensors 18, 9 (2018), 2946.

Crossref

Google Scholar

[14]

Shiyong Wang, Jiafu Wan, Di Li, and Chunhua Zhang. 2016. Implementing smart factory of industrie 4.0: an outlook. International Journal of Distributed Sensor Networks 12, 1 (2016), 3159805.

Digital Library

Google Scholar

Cited By

View all

Bechinie CZafari SGallhuber KKroeninger lPuthenkalam JTscheligi M(2024)Sensing the Machine: Evaluating Multi-modal Interaction for Intelligent Dynamic GuidanceProceedings of the 29th International Conference on Intelligent User Interfaces10.1145/3640543.3645179(66-73)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3640543.3645179

Recommendations

Drivers and Barriers of Industry 4.0 Adoption in Indonesian Manufacturing Industry
APCORISE '20: Proceedings of the 3rd Asia Pacific Conference on Research in Industrial and Systems Engineering

Industry 4.0, the fourth revolution Industry, has become a global trend in the manufacturing industry because it is able to produce a "smart factory" with cyber-physical systems (CPS). CPS integrate the physical world with digital by monitoring physical ...
Cloud-based manufacturing equipment and big data analytics to enable on-demand manufacturing services
Highlights
- Proposed the concept of cloud-based manufacturing equipment and discussed the technical challenges in the context of Industry 4.0.
Abstract
Making manufacturing as on-demand cloud services is a transformative paradigm to achieve the required business flexibility in the context of Industry 4.0 via enabling rapid configuration of loosely-connected manufacturing devices to ...
Graphical abstract

Display Omitted
Industry 4.0 concepts and lean manufacturing implementation for optimizing a company logistics flows
Abstract
Industry 4.0 concepts are used as a response to an increasingly varied demand in companies and flexibility corresponds to a lever for improving production manufacturing performance. This production chain flexibility based on industry 4.0 concepts, ...

Comments

Information & Contributors

Information

Published In

AVI '20: Proceedings of the 2020 International Conference on Advanced Visual Interfaces

September 2020

613 pages

ISBN:9781450375351

DOI:10.1145/3399715

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

In-Cooperation

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 October 2020

Check for updates

Author Tags

Qualifiers

Poster
Research
Refereed limited

Conference

AVI '20

AVI '20: International Conference on Advanced Visual Interfaces

September 28 - October 2, 2020

Salerno, Italy

Acceptance Rates

AVI '20 Paper Acceptance Rate 36 of 123 submissions, 29%;

Overall Acceptance Rate 128 of 490 submissions, 26%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
85
Total Downloads

Downloads (Last 12 months)12
Downloads (Last 6 weeks)1

Reflects downloads up to 05 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Bechinie CZafari SGallhuber KKroeninger lPuthenkalam JTscheligi M(2024)Sensing the Machine: Evaluating Multi-modal Interaction for Intelligent Dynamic GuidanceProceedings of the 29th International Conference on Intelligent User Interfaces10.1145/3640543.3645179(66-73)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3640543.3645179

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Recommendations

Drivers and Barriers of Industry 4.0 Adoption in Indonesian Manufacturing Industry

Cloud-based manufacturing equipment and big data analytics to enable on-demand manufacturing services

Industry 4.0 concepts and lean manufacturing implementation for optimizing a company logistics flows

Comments

Published In

In-Cooperation

Publisher

Publication History

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Other Metrics

Article Metrics

Other Metrics

Cited By

Login options

Full Access

PDF

eReader

Abstract

References

Cited By

Recommendations

Drivers and Barriers of Industry 4.0 Adoption in Indonesian Manufacturing Industry

Cloud-based manufacturing equipment and big data analytics to enable on-demand manufacturing services

Industry 4.0 concepts and lean manufacturing implementation for optimizing a company logistics flows

Comments

Information

Published In

In-Cooperation

Publisher

Publication History

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations