Issues on a conveyor belt-driven production line can easily be missed or overlooked, leading in some cases to damaged products and costly repairs. The resulting downtime is costly, and leads to missed production targets. While manual quality inspections can help prevent some of these problems, it’s time consuming, inaccurate and causes bottlenecks in performance. 

My team collaborated with Amazon Web Services (AWS) to develop an edge-to-cloud machine monitoring solution to address these exact manufacturing challenges. In this blog, along with our partners at AWS, we go behind the scenes of the automated quality inspection solution, explaining what it does and how it works, combining the best edge computing and AI technology from ADLINK and AWS. 

The Power of Edge and Cloud  

ADLINK’s machine vision platform to detect defects in products as they move along the conveyor line, as well as ADLINK edge hardware and software to monitor the health of the conveyor equipment as vibration, temperature and load data is generated in real-time. AWS IoT Greengrass 2.0 provides the secure link between edge solutions and the cloud, while AWS IoT SiteWise collects and organizes data across factories and geographical locations to show the status of production lines.  

Of course we have deep experience in data acquisition and automating action at the edge, which—along with data analysis, management, and training in the cloud—offers a secure system that enables rich and informative decision-making based on AI-guided solutions, such as quality inspection of operations and production. The end result for manufacturers is the ability to continuously improve operations and process efficiencies. 

Read the full blog to see how to detect defects and drive action in real-time on a production line, using a machine vision system integrating ADLINK edge AI platforms with AWS IoT SiteWise and AWS IoT Greengrass 2.0.