Improving quality control with industrial AIoT at HP factories: experiences and learned lessons
Enabled by the increasingly available embedded hardware accelerators, the capability of executing advanced machine learning models at the edge of the Internet of Things (IoT) triggers wide interest of applying the resulting Artificial Intelligence of Things (AIoT) systems in industrial applications....
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sg-ntu-dr.10356-1717432023-11-07T01:36:37Z Improving quality control with industrial AIoT at HP factories: experiences and learned lessons Yang, Joy Qiping Zhou, Siyuan Le, Duc Van Ho, Daren Tan, Rui School of Computer Science and Engineering 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON 2021) HP-NTU Digital Manufacturing Corporate Lab Engineering::Computer science and engineering Industrial AIoT Quality Control Enabled by the increasingly available embedded hardware accelerators, the capability of executing advanced machine learning models at the edge of the Internet of Things (IoT) triggers wide interest of applying the resulting Artificial Intelligence of Things (AIoT) systems in industrial applications. The in situ inference and decision made based on the sensor data containing patterns with certain sophistication allow the industrial system to address a variety of heterogeneous, local-Area non-Trivial problems in the last hop of the IoT networks, avoiding the wireless bandwidth bottleneck and unreliability issues and also the cumbersome cloud. However, the literature still lacks presentations of industrial AIoT system developments that provide insights into the challenges and offer important lessons for the relevant research and engineering communities, no matter the development is successful or not. In light of this, we present the design, deployment, and evaluation of an industrial AIoT system for improving the quality control of Hewlett-Packard's ink cartridge manufacturing lines. While our development has obtained promising results, we also discuss the lessons learned from the whole course of the effort, which could be useful to the developments of other industrial AIoT systems. This research was conducted in collaboration with HP Inc. and supported by the Singapore Government through the Industry Alignment Fund-Industry Collaboration Projects Grant. 2023-11-07T01:21:51Z 2023-11-07T01:21:51Z 2021 Conference Paper Yang, J. Q., Zhou, S., Le, D. V., Ho, D. & Tan, R. (2021). Improving quality control with industrial AIoT at HP factories: experiences and learned lessons. 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON 2021). https://dx.doi.org/10.1109/SECON52354.2021.9491592 9781665441087 https://hdl.handle.net/10356/171743 10.1109/SECON52354.2021.9491592 2-s2.0-85111735504 en © 2021 IEEE. All rights reserved. |
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Engineering::Computer science and engineering Industrial AIoT Quality Control Yang, Joy Qiping Zhou, Siyuan Le, Duc Van Ho, Daren Tan, Rui Improving quality control with industrial AIoT at HP factories: experiences and learned lessons |
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Enabled by the increasingly available embedded hardware accelerators, the capability of executing advanced machine learning models at the edge of the Internet of Things (IoT) triggers wide interest of applying the resulting Artificial Intelligence of Things (AIoT) systems in industrial applications. The in situ inference and decision made based on the sensor data containing patterns with certain sophistication allow the industrial system to address a variety of heterogeneous, local-Area non-Trivial problems in the last hop of the IoT networks, avoiding the wireless bandwidth bottleneck and unreliability issues and also the cumbersome cloud. However, the literature still lacks presentations of industrial AIoT system developments that provide insights into the challenges and offer important lessons for the relevant research and engineering communities, no matter the development is successful or not. In light of this, we present the design, deployment, and evaluation of an industrial AIoT system for improving the quality control of Hewlett-Packard's ink cartridge manufacturing lines. While our development has obtained promising results, we also discuss the lessons learned from the whole course of the effort, which could be useful to the developments of other industrial AIoT systems. |
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School of Computer Science and Engineering |
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School of Computer Science and Engineering Yang, Joy Qiping Zhou, Siyuan Le, Duc Van Ho, Daren Tan, Rui |
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Conference or Workshop Item |
author |
Yang, Joy Qiping Zhou, Siyuan Le, Duc Van Ho, Daren Tan, Rui |
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Yang, Joy Qiping |
title |
Improving quality control with industrial AIoT at HP factories: experiences and learned lessons |
title_short |
Improving quality control with industrial AIoT at HP factories: experiences and learned lessons |
title_full |
Improving quality control with industrial AIoT at HP factories: experiences and learned lessons |
title_fullStr |
Improving quality control with industrial AIoT at HP factories: experiences and learned lessons |
title_full_unstemmed |
Improving quality control with industrial AIoT at HP factories: experiences and learned lessons |
title_sort |
improving quality control with industrial aiot at hp factories: experiences and learned lessons |
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2023 |
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https://hdl.handle.net/10356/171743 |
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1783955516551069696 |