A Neural Network Clamping Force Model for Bolt Tightening of Wind Turbine Hubs, The 15th IEEE International Conference on Computer and Information Technology

Secco, Emanuele Lindo and Nagar, Atulya K. and Deters, Christian and Wudemann, Helge and Lam, Hak-Keung and Althoefer, Kaspar (2015) A Neural Network Clamping Force Model for Bolt Tightening of Wind Turbine Hubs, The 15th IEEE International Conference on Computer and Information Technology. In: 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing. IEEE, pp. 288-296. ISBN 9781509001538

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Official URL: http://dx.doi.org/10.1109/CIT/IUCC/DASC/PICOM.2015...

Abstract

"Industrial manufacturing of large-scale wind turbines requires the accurate tightening of multiple bolts and nuts, which connect the ball bearings - supporting wind turbine blades - with the hub, a huge mechanical component supporting blades pitch motion. An accurate tightening of bolts and nuts requires uniformly distributed clamping forces along flanges and surfaces of contact between hub and bearings. Due to the role of friction forces and the dynamics of the phenomenon, this process is nonlinear and currently performed manually; it is also time consuming, requiring high-cost equipment and expert operators. This paper proposes a set of neural networks, which infer the clamping force achievable with a tightening tool while fastening M24 nuts on bolts. The tool embeds a torque sensor and shaft encoder, therefore two types of inputs of the neural networks are considered in order to fit the clamping force output: the time signals of (a) the applied torque of the tool and (b) the combination of the torque and of the angular speed of the tool. According to results, neural networks properly model the clamping force, both during the training stage and when exposed to unseen testing data. This approach could be generalized to other industrial processes and specifically to those requiring repetitive tightening tasks and involving highly nonlinear aspects, such as friction forces. "

Item Type: Book Section
Keywords: self-adaptive manufacturing, bolt tightening, wind turbine, neural network
Faculty / Department: Faculty of Science > Mathematics and Computer Science
Depositing User: Users 23 not found.
Date Deposited: 15 Jan 2016 12:03
Last Modified: 15 Jan 2016 12:03
URI: http://hira.hope.ac.uk/id/eprint/690

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