Virtual Sensors for On-line Wheel Wear and Part Roughness Measurement in the Grinding Process

Sensors

Ander Arriandiaga¹  Eva Portillo¹  Jose A. Sánchez²  Itziar Cabanes¹ 

[1] Department of Automatic Control and System Engineering, University of the Basque Country, C/Alameda Urquijo s/n, 48013 Bilbao, Spain; E-Mails:;Department of Mechanical Engineering, University of the Basque Country, C/Alameda Urquijo s/n, 48013 Bilbao, Spain; E-Mails:

关键词: virtual sensor;    grinding process;    wheel wear;    surface roughness;    Artificial Neural Networks;   

DOI  :  10.3390/s140508756

来源: mdpi

【 摘 要 】

Grinding is an advanced machining process for the manufacturing of valuable complex and accurate parts for high added value sectors such as aerospace, wind generation, etc. Due to the extremely severe conditions inside grinding machines, critical process variables such as part surface finish or grinding wheel wear cannot be easily and cheaply measured on-line. In this paper a virtual sensor for on-line monitoring of those variables is presented. The sensor is based on the modelling ability of Artificial Neural Networks (ANNs) for stochastic and non-linear processes such as grinding; the selected architecture is the Layer-Recurrent neural network. The sensor makes use of the relation between the variables to be measured and power consumption in the wheel spindle, which can be easily measured. A sensor calibration methodology is presented, and the levels of error that can be expected are discussed. Validation of the new sensor is carried out by comparing the sensor's results with actual measurements carried out in an industrial grinding machine. Results show excellent estimation performance for both wheel wear and surface roughness. In the case of wheel wear, the absolute error is within the range of microns (average value 32 μm). In the case of surface finish, the absolute error is well below R_a 1 μm (average value 0.32 μm). The present approach can be easily generalized to other grinding operations.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.