Sun, C., Dominguez-Caballero, J., Ward, R. orcid.org/0000-0002-6201-0285 et al. (2 more authors) (2022) Machining cycle time prediction : data-driven modelling of machine tool feedrate behavior with neural networks. Robotics and Computer-Integrated Manufacturing, 75. 102293. ISSN 0736-5845
Abstract
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 Elsevier Ltd. |
Keywords: | Data-driven model; Neural networks; Feedrate; Machine Tool; digital twins; Industry 4.0 |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 17 Feb 2022 12:27 |
Last Modified: | 17 Feb 2022 12:27 |
Status: | Published |
Publisher: | Elsevier BV |
Refereed: | Yes |
Identification Number: | 10.1016/j.rcim.2021.102293 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:183752 |