Prediction, monitoring and control of surface roughness in high-torque milling machine operations

Abstract

The development and testing of an application that will predict, monitor and control surface roughness are described. It comprises three modules for off-line roughness prediction, surface roughness monitoring and surface roughness control, and is especially designed for high-torque, high-power milling operations, which are widely used nowadays in the manufacture of wind turbine components. The application is tested in a milling machine with a high working volume. Due to the highly complex phenomena that generate surface roughness and the large number of factors that interact during the cutting process, models to calculate the average surface roughness parameter (Ra) are based on artificial neural networks (ANN) as they are especially suitable for modelling complex relationships between inputs and outputs.

Keywords:

• milling
• surface roughness
• artificial neural networks
• process monitoring
• cutting parameters

Acknowledgements

This investigation has been partially supported by the European Commission through the NEXT Generation Production Systems, Integrated Project IP 011815 and the CENIT project EeE funded by the Spanish Ministry of Science and Innovation. The authors would especially like to thank Dr Wilco Verbeeten from Nicolas Correa S.A. for his kind-spirited and useful advice.