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Sustainable lifestyle and health from a public health perspective

Heterogeneous systems - hardware software co-design

Political Science

Information Design

Software Testing Laboratory

Product and Production Development

PREVIVE

Industrial Software Engineering

Formal Modelling and Analysis of Embedded Systems

Model-Based Engineering of Embedded Systems

Real-Time Systems Design

Automated Software language and Software engineering

Renewable Energy

Digitalisation

Resource efficiency

AUTOMAD: AUTOnomous Decision Making in Industry 4.0 using MAchine Learning and Data Analytics

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.

Concluded

Start

2019-06-01

Conclusion

2020-05-31

Project manager at MDH

Senior Lecturer

Mobyen Uddin Ahmed

+4621107369

mobyen.ahmed@mdh.se

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Description of the project

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.

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