Industry 4.0 or smart manufacturing/production, to have assembly lines being automated and interacting with each other and with close to no human interaction, includes data exchange, decision making, prediction, etc. through IoT, computing, and intelligent data analysis. Today, production and assembly manufacturing lines are capturing a wide range of data that can be used to improve performance and productivity by using Data Analytics and Machine Learning (ML). These huge amounts of massive historical data are potential for analysis and prediction. Data analytics can be used e.g., for real-time predictive maintenance, optimization of production operations, improving productivity and energy efficiency etc.
AUTOMAD project aims to develop a decision-making system based on domain knowledge and contextual information using machine learning and data analytics. It will help in increasing assembly line capacity and production flexibility. Also, it will identify intersections between i.e, data analytics, context and domain-specific knowledge to create real value from the available data enabling efficient production and support through automation and digitization. Thus, the AUTOMAD project will help to overcome the challenges of integrating an intelligent solution into the Industry 4.0 context.
|First Name||Last Name||Title|
|Mobyen Uddin||Ahmed||Associate Professor|
|Sharmin Sultana||Sheuly||Doctoral student|
Data Analytics using Statistical Methods and Machine Learning: A Case Study of Power Transfer Units (Mar 2021) Sharmin Sultana Sheuly, Shaibal Barua, Shahina Begum, Mobyen Uddin Ahmed, Ekrem Güclü , Michael Osbakk International Journal of Advanced Manufacturing Technology (IJAMT)
Intelligent Data Analytics for Maintenance in Industry 4.0 (Oct 2019) Mobyen Uddin Ahmed, Shaibal Barua, Shahina Begum, Ekrem Güclü , Manasi Jayapal , Sharmin Sultana Sheuly