The big ideas of the Fourth Industrial Revolution

Posted on 10 Feb 2019 and read 686 times
The big ideas of the Fourth Industrial Revolution Sheffield-based Tinsley Bridge — a leading manufacturer of slideways, shear blades and steel-mill blades — has embarked on a project to demonstrate that Artificial Intelligence (AI) and machine learning are valuable tools to improve manufacturing productivity for SMEs.

The company has joined forces with the University of Sheffield AMRC’s Factory 2050, which is using AI to learn what machine usage looks like on the company’s workshop floor (

The aim is to create a demonstrator to show other SMEs in the Sheffield City Region just how accessible Industry 4.0 technologies are, and how they can potentially revolutionise productivity.

Rikki Coles, AI project engineer at the AMRC (, said: “Using edge computing devices retrofitted to the company’s CNC machines, we have collected power consumption data during the production of automotive suspension components.

"It isn’t a complicated parameter to measure on a CNC machine, but using AI and machine learning, we can actually do a lot with such simple data.”

The data was run through an AI algorithm to provide new insights for the control and monitoring of manufacturing processes.

Analysing the power signatures from the data, the algorithm worked out how many components were machined — and that three different types of component were manufactured.

Mr Coles said: “The project demonstrates that with a low-cost device collating quite simple data, AI and machine learning can be used to provide valuable insights for manufacturers.”

Russell Crow, Tinsley Bridge’s director of engineering, said: “Interrogating our machine usage rates means we have better visibility of what was being manufactured and when, and the ability to assess if we are scheduling effectively.

“This data will allow us to look at boosting our productivity on the shopfloor.”

The next phase of the project will involve algorithms that can detect non-conforming components while in production or identify a machine problem — such as inconsistent tool wear — that requires intervention.

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