Recurrent ANN-based modelling of the dynamic evolution of the surface roughness in grinding
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Grinding is critical in modern manufacturing due to its capacity for producing high surface quality and high-precision parts. One of the most important parameters that indicate the grinding quality is the surface roughness (R a). Analytical models developed to predict surface finish are not easy to apply in the industry. Therefore, many researchers have made use of artificial neural networks. However, all the approaches provide a particular solution for a wheel–workpiece pair, not generalizing to new grinding wheels. Besides, these solutions do not give surface roughness values related to the grinding wheel status. Therefore, in this work the modelling of the dynamic evolution of the surface roughness (R a) based on recurrent neural networks is presented with the capability to generalize to new grinding wheels and conditions taking into account the wheel wear. Results show excellent prediction of the surface finish dynamic evolution. The absolute maximum error is below 0.49 µm, being the average error around 0.32 µm. Besides, the analysis of the relative importance of the inputs shows that the grinding conditions have higher influence than the wheel characteristics over the prediction of the surface roughness confirming experimental knowledge of grinding technology users.
KeywordsGrinding Surface roughness Dynamic evolution modelling Recurrent neural networks
The authors gratefully acknowledge the funding support received from the Spanish Ministry of Economy and Competitiveness and the FEDER operation program for funding the Project DPI2014-56137-C2-1-R and DPI2012-32882. This work was also supported in part by the Regional Government of the Basque Country through the Departamento de Educación, Universidades e Investigación (Project IT719-13) and UPV/EHU under Grant UFI11/28.
- 2.Jiang J, Ge P, Hong J (2008) Study on micro-interacting mechanism modeling in grinding process and ground surface roughness prediction. Int J Adv Technol 67(5–8):1035–1052Google Scholar
- 4.Aguiar PR, Cruz CED, Paula WCF (2008) Predicting surface roughness in grinding using neural networks. In: Arámburo J, Treviño AR (eds) Advances in robotics, automation and control. I-Tech, Vienna, Austria, pp 33–44Google Scholar
- 7.Yang Q, Jin J (2010) Study on machining prediction in plane grinding based on artificial neural network. In: Proceedings of international conference on intelligent systems and knowledge engineering (ISKE), Hangzhou, China, 15–16 NovemberGoogle Scholar
- 8.Li G, Liu J (2011) On-line prediction of surface roughness in cylindrical traverse grinding based on BP + GA algorithm. In: Proceedings of second international conference on mechanic automation and control engineering (MACE), Hohhot, China, 15–17 July 2001. pp 1456–1459Google Scholar
- 20.Beale MH, Hagan MT, Demuth HB (2012) Neural Network Toolbox™ user’s guide. The MathWorks Inc., Natick, MA, USAGoogle Scholar
- 22.Dan Foresse, F, Hagan, MT (1997) Gauss–Newton approximation to Bayesian learning. In: International conference on neural networks, Houston, USA, 9–12 Jun 1997Google Scholar
- 23.LeCun Y, Kanter I, Solla SA (1991) Second-order properties of error surfaces: learning time and generalization. Adv Neural Inf Process Syst 3:918–924Google Scholar