Accurate Measurement of Temperatures in Industrial Grinding Operations with Steep Gradients.

Due to the continuously growing demands from high-added-value sectors such as aerospace, e-mobility or biomedical bound-abrasive technologies are the key to achieving extreme requirements. During grinding, energy is rapidly dissipated as heat, generating thermal fields on the ground part which are characterized by high temperatures and very steep gradients. The consequences on the ground part are broadly known as grinding burn. Therefore, the measurement of workpiece temperature during grinding has become a critical issue. Many techniques have been used for temperature measurement in grinding, amongst which, the so-called grindable thermocouples exhibit great potential and have been successfully used in creep-feed grinding operations, in which table speed is low, and therefore, temperature gradients are not very steep. However, in conventional grinding operations with faster table speeds, as most industrial operations are, the delay in the response of the thermocouple results in large errors in the maximum measured value. In this paper, the need for accurate calibration of the response of grindable thermocouples is studied as a prior step for signal integration to correct thermal inertia. The results show that, if the raw signal is directly used from the thermocouples, the deviation in the maximum temperature with respect to the theoretical model is over 200 K. After integration using the calibration constants obtained for the ground junction, the error can be reduced to 93 K even for feed speeds as high as 40 m/min and below 20 K for lower feed speeds. The main conclusion is that, following the proposed procedure, maximum grinding temperatures can be effectively measured using grindable thermocouples even at high values of table speed.

On The Influence of Rotary Dresser Geometry on Wear Evolution and Grinding Process.

Dressing is a critical issue for optimizing the grinding process. Dresser tool and dresser parameters must be designed according to the grinding wheel material, shape, or even the dimensional and geometrical tolerances of the workpiece and its surface roughness. Likewise, one of the problematic issues of dressers is the wear that they suffer. In order to tackle this issue, the present work characterized the wear of two rotary dressers by analysing the wear behaviour depending on the pit radius of the dressers while studying the influence of the wear on ground surfaces. This work showed that the rotary dresser with a higher pit radius presents wear that is approximately 28% higher than the dresser with a half pit radius.

On the Influence of Infra-Red Sensor in the Accurate Estimation of Grinding Temperatures.

Workpiece rejection originated by thermal damage is of great concern in high added-value industries, such as automotive or aerospace. Surface temperature control is vital to avoid this kind of damage. Difficulties in empirical measurement of surface temperatures in-process imply the measurement in points other than the ground surface. Indirect estimation of temperatures demands the use of thermal models. Among the numerous temperature measuring techniques, infra-red measurement devices excel for their speed and accurate measurements. With all of this in mind, the current work presents a novel temperature estimation system, capable of accurate measurements below the surface as well as correct interpretation and estimation of temperatures. The estimation system was validated by using a series of tests in different grinding conditions that confirm the hypotheses of the error made when measuring temperatures in the workpiece below the surface in grinding. This method provides a flexible and precise way of estimating surface temperatures in grinding processes and has shown to reduce measurement error by up to 60%.

Virtual sensors for on-line wheel wear and part roughness measurement in the grinding process.

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.