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The grinding process is continuously adapting to industrial requirements. New advanced materials have been developed, which have been ground. In this regard, new abrasive grains have emerged to respond to the demands of industry to reach the optimum combination of abrasive-workpiece material, which allows for both the minimization of wheel wear and increased tool life. To this endand following previous experimental worksthe present study models in 3D the wear behavior of Sol-Gel alumina abrasive grain using Discrete Element Methods. It is established that the alumina behaves as a ductile material upon contact due to the effect of high temperature and pressure. This model reproduces the third body generation in the contact, taking into account the tribochemical nature of the wear flat, w hich i s the most h armful type o f wear i n the grinding p rocess. The evolution of the wear during a complete contact is analyzed, revealing similarities in the wear of white fused alumina (WFA) and Sol-Gel (SG) alumina. However, the SG abrasive grain suffers less wear than the WFA under the same contact conditions. The proposed wear model can be applied to any abrasive-workpiece combination.

Abstract:Manufacturing of grinding wheels is continuously adapting to new industrial requirements. New abrasives and new wheel configurations, together with wheel wear control allow for grinding process optimization. However, the wear behavior of the new abrasive materials is not usually studied from a scientific point of view due to the difficulty to control and monitor all the variables affecting the tribochemical wear mechanisms. In this work, an original design of pin-on-disk tribometer is developed in a CNC (Computer Numerical Control) grinding machine. An Alumina grinding wheel with special characteristics is employed and two types of abrasive are compared: White Fused Alumina (WFA) and Sol-Gel Alumina (SG). The implemented tribometer reaches sliding speeds of between 20 and 30 m/s and real contact pressures up to 190 MPa. The results show that the wear behavior of the abrasive grains is strongly influenced by their crystallographic structure and the tribometer appears to be a very good tool for characterizing the wear mechanisms of grinding wheels, depending on the abrasive grains.

Tool wear monitoring is a critical issue in advanced manufacturing systems. In the search for sensing devices that can provide information about the grinding process, Acoustic Emission (AE) appears to be a promising technology. The present paper presents a novel deep learning-based proposal for grinding wheel wear status monitoring using an AE sensor. The most relevant finding is the possibility of efficient feature extraction form frequency plots using CNNs. Feature extraction from FFT plots requires sound domain-expert knowledge, and thus we present a new approach to automated feature extraction using a pre-trained CNN. Using the features extracted for different industrial grinding conditions, t-SNE and PCA clustering algorithms were tested for wheel wear state identification. Results are compared for different industrial grinding conditions. The initial state of the wheel, resulting from the dressing operation, is clearly identified for all the experiments carried out. This is a very important finding, since dressing strongly affects operation performance. When grinding parameters produce acute wear of the wheel, the algorithms show very good clustering performance using the features extracted by the CNN. Performance of both t-SNE and PCA was very much the same, thus confirming the excellent efficiency of the pre-trained CNN for automated feature extraction from FFT plots.

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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.