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Sensors 2014, 14(5), 8756-8778; doi:10.3390/s140508756
Article

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

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Received: 8 February 2014; in revised form: 16 April 2014 / Accepted: 15 May 2014 / Published: 19 May 2014
(This article belongs to the Section Physical Sensors)
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Abstract: 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 Ra 1 μm (average value 0.32 μm). The present approach can be easily generalized to other grinding operations.
Keywords: virtual sensor; grinding process; wheel wear; surface roughness; Artificial Neural Networks virtual sensor; grinding process; wheel wear; surface roughness; Artificial Neural Networks
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Arriandiaga, A.; Portillo, E.; Sánchez, J.A.; Cabanes, I.; Pombo, I. Virtual Sensors for On-line Wheel Wear and Part Roughness Measurement in the Grinding Process. Sensors 2014, 14, 8756-8778.

AMA Style

Arriandiaga A, Portillo E, Sánchez JA, Cabanes I, Pombo I. Virtual Sensors for On-line Wheel Wear and Part Roughness Measurement in the Grinding Process. Sensors. 2014; 14(5):8756-8778.

Chicago/Turabian Style

Arriandiaga, Ander; Portillo, Eva; Sánchez, Jose A.; Cabanes, Itziar; Pombo, Iñigo. 2014. "Virtual Sensors for On-line Wheel Wear and Part Roughness Measurement in the Grinding Process." Sensors 14, no. 5: 8756-8778.



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