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. 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.