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Using Artificial Intelligence and data in manufacturing to maximise assets

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Case study

Using Artificial Intelligence and data in manufacturing to maximise assets

The EPSRC-funded Transfer Learning for Robust, Reliable and Transferable Cyber Manufacturing Systems project, led by Cinzia Giannetti of Swansea University College of Engineering, works with industry partners to increase the use of artificial intelligence in manufacturing and help them use data to make better decisions.

“The main body of my research is looking at developing machine learning technologies, and algorithms that can use the vast amounts of data collected during manufacturing processes and use them to enable companies to make better, faster and optimal decisions,” says Giannetti.

Machine Learning can, for example, help a company to run what is called ‘predictive maintenance’, to maximise the use of assets and machines, Giannetti says.

“The idea there is to use data to develop and train very complex predictive models using deep learning and then use those models to predict the remaining useful life of equipment, and determine the right time to do maintenance.

“The research itself involves collecting and synthesising large volumes of data collected through different machines and sensors. We then want to visualise this data and use it to develop a predictive model,” she says.

“Given the amount of data, the precision we want to achieve and the complex architecture of Deep Neural Networks, we need an adequate computing facility,” Giannetti says.

The Research Software Engineers at Supercomputing Wales have been vital in getting this work done, she says.

“They provided training and, in the initial stages, helped us to perform some optimisation to run the models more efficiently. The biggest advantage of Supercomputing Wales is not only the availability of high performance hardware but actually having people that can support us in running the programmes – the training, the support, the help in setting up the system. We’ve become quite independent now, but at the beginning it was crucial to us,” Giannetti says.

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