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.
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.