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Research Project 1.2
Digital technologies for smart automation
Objective:
Develop and use industrial digitalisation, reducing the perceived complexity of automation
The project focuses on solutions addressing the perceived complexity when developing, integrating, using, and maintaining automation systems. The focus is to create solutions addressing the perception that automation technology is too complex and technically advanced. Digitalisation, a main potential driver for smart manufacturing, is closely linked to Industry 4.0 digital technologies (i.e.,combinations of information, computing, communication, and connectivity technologies), such as the Internet of Things (loT), Artificial Intelligence (Al), and big data technologies. By continually adjusting and optimising online production, digital technologies aim to improve processes' flexibility and reliability and improve industrial firms' product quality and maintenance practices in production. One of the greater potentials of advanced digital technologies in manufacturing is the possibility of providing remote access and integration into physical production systems
The project will provide the needed components for a supporting digital infrastructure for smart automation and will closely work with UC1 and UC2. The infrastructure in this context covers different aspects of manufacturing digitalisation, including data collection models, communication tech nologies, software and hardware architectures, cybersecurity processes, and optimisation models.
To enable smart automation, it is essential to deploy digital technologies so that raw data is converted into data that can be applied by equipment or humans to generate value or reduce waste in production. This requires consideration of the data flow within the production system, i.e., the entire process of converting raw data into useful data, which includes data management aspects such as the collection, analysis, and visualisation of data. Critical factors to consider when selecting and integrating digital technologies to enable smart automation are to define requirements for each phase of the data value chain where people, process, and technology aspects are addressed
Expected outcomes:
Solutions for smart automation with a focus on reducing complexity in developing, integrating, using, and maintaining automation. This is expected to demonstrate production equipment recycling and reusing, leading to automation applications with an acceptable investment, high efficiency, high adaptability, and such flexibility that it may produce several different products and adapt to future product variants without large additional investments.
Staffing: 1 senior PhD student(oart of), 2 postdoc.
Duration: 2024-2028
Partners
