Manufacturing IT, MES
Can industrial edge computing fit into the Purdue model?
MIT researchers have developed an automated AI system for training and running certain neural networks that also cuts down the pounds of carbon emissions involved.
A machine learning algorithm developed by MIT researchers combines data from COVID-19’s spread with a neural network to assess the impact of quarantine measures and predict when infections will slow down in each country.
Remote monitoring and data visualization are key to improving overall manufacturing efficiency.
Six edge computing questions to ask about data collection, networking and control systems.
Even in the wake of COVID-19 (coronavirus), manufacturers still need human workers to manage the supply chain. The need for more automation and information is an opportunity for manufacturers.
MIT Professor Aleksander Madry strives to build machine-learning models that are more reliable, understandable and robust.
With automation at an inflection point, a variety of technologies and interoperable standards efforts are bringing higher levels of flexibility, integration and optimization.
Expand industrial data access and get more out of it with tools such as message queuing telemetry transport (MQTT), which can help manufacturers realize Industry 4.0’s benefits.
Many manufacturers are becoming increasingly concerned with Covid-19 breakouts on the production floor. There are ways to improve worker safety through preventive best practices and proper preparation.