Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/171743
Title: Improving quality control with industrial AIoT at HP factories: experiences and learned lessons
Authors: Yang, Joy Qiping
Zhou, Siyuan
Le, Duc Van
Ho, Daren
Tan, Rui
Keywords: Engineering::Computer science and engineering
Issue Date: 2021
Source: 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
Conference: 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON 2021)
Abstract: 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.
URI: https://hdl.handle.net/10356/171743
ISBN: 9781665441087
DOI: 10.1109/SECON52354.2021.9491592
Schools: School of Computer Science and Engineering 
Research Centres: HP-NTU Digital Manufacturing Corporate Lab
Rights: © 2021 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Conference Papers

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