University of Sheffield
Canvas Category Consultancy : Research : Academic
The University of Sheffield Advanced Manufacturing Research Centre (AMRC), AMRC Training Centre and Nuclear AMRC form a world-leading cluster for research, innovation and training, and work with advanced manufacturing companies around the globe.
Assembly Line
Wayland Additive raises £4.2M to boost global expansion in metal 3D printing
UK additive manufacturing company Wayland Additive announced a £4.2 million fundraise to enable continued growth of global customers. A Huddersfield-based company, Wayland designs, develops, makes and sells industrial Calibur3 metal additive manufacturing machines, utilising cutting-edge electron beam 3D printing technology. It has successfully rolled out its technology to various buyers across different sectors, including aerospace, mining, engineering, medical, motorsport and military and defence, with operations across North America and Europe. This latest fundraising will allow Wayland to continue to expand its growing global customer base.
FourJaw raises £1.8m to support continued growth
We have raised £1.8 million from NPIF – Mercia Equity Finance, which is managed by Mercia and is part of the Northern Powerhouse Investment Fund, Mercia’s EIS funds and private investors. The funding will enable us to further enhance our technology and invest in areas of the business best placed to support our continued growth.
Good vibrations as Productive Machines raises £2.2 million for machine tool AI
Sheffield-based Productive Machines, an AI start-up has raised £2.2 million in Seed funding to expand the reach of its advanced machine tool process optimisation technology. A spin out of The University of Sheffield, Productive Machines is commercialising its The University of Sheffield Advanced Manufacturing Research Centre (AMRC) six-year research project to a fully-automated Software-as-a-Service (SaaS) product.
The round was led by UK Innovation & Science Seed Fund (UKI2S)and includes NPIF – Mercia Equity Finance, ACT Venture Partners and Fuel Ventures, alongside grant funding from Innovate UK.
Factory+: a connected, smart factory driven by big data
Factory+ is an open-access digital architecture for manufacturing shop floors that simplifies the way data can be handled across an organisation. Factory+ aims to provide a synthesised way for machinery to capture and use data to solve problems; to make manufacturing more sustainable, efficient and ready for Industry 4.0 – or even 5.0. It is a truly collaborative project of Internet of Things (IoT) engineers, robotic engineers, software engineers and data scientists.
Data scientists are considered the users of the Factory+ architecture and need to be able to pull data for any project. The value of having data scientists involved in this is that, while we don’t have the domain knowledge of an engineer, we do know what should be considered when collecting useful data for an array of problems without simply trying to collect and store all available data; an endeavour quickly curtailed by storage limitations.
Machining cycle time prediction: Data-driven modelling of machine tool feedrate behavior with neural networks
Accurate prediction of machining cycle times is important in the manufacturing industry. Usually, Computer-Aided Manufacturing (CAM) software estimates the machining times using the commanded feedrate from the toolpath file using basic kinematic settings. Typically, the methods do not account for toolpath geometry or toolpath tolerance and therefore underestimate the machining cycle times considerably. Removing the need for machine-specific knowledge, this paper presents a data-driven feedrate and machining cycle time prediction method by building a neural network model for each machine tool axis. In this study, datasets composed of the commanded feedrate, nominal acceleration, toolpath geometry and the measured feedrate were used to train a neural network model. Validation trials using a representative industrial thin-wall structure component on a commercial machining center showed that this method estimated the machining time with more than 90% accuracy. This method showed that neural network models have the capability to learn the behavior of a complex machine tool system and predict cycle times. Further integration of the methods will be critical in the implantation of digital twins in Industry 4.0.