Generation mechanism modeling of surface topography in tangential ultrasonic vibration-assisted grinding with green silicon carbide abrasive wheel

Tangential ultrasonic vibration-assisted grinding (TUAG) has a wide prospect in machining difficult-to-machine materials. However, the surface generation mechanism in TUAG is not fully recovered. This study proposes an analytical model of the surface topography produced by TUAG. Based on the model, the surface topography and roughness are predicted and experimentally verified. In addition, the influence of the grinding parameters on the surface topography is analyzed. The predicted surface topography well coincides with experimental measurements, and the prediction error in surface roughness Ra by the proposed model is less than 5%. Compared with conventional grinding, TUAG produces a surface with more uniform scratches and surface roughness Ra was reduced by up to 27% with the proper parameters. However, the improvement of surface roughness in TUAG is weakened when grinding speed or depth of cut increases. Moreover, the influence of the ultrasonic vibration amplitude on the surface roughness is not monotonous. With the grinding parameters selected in this study, TUAG with an ultrasonic amplitude of 7.5 μm produces the minimum surface roughness.

Experimentally validated atomistic simulation of the effect of relevant grinding parameters on work piece topography, internal stresses, and microstructure

In this work, we present a fully atomistic approach to modeling a finishing process with the goal to shed light on aspects of work piece development on the microscopic scale, which are difficult or even impossible to observe in experiments, but highly relevant for the resulting material behavior. In a large-scale simulative parametric study, we varied four of the most relevant grinding parameters: The work piece material, the abrasive shape, the temperature, and the infeed depth. In order to validate our model, we compared the normalized surface roughness, the power spectral densities, the steady-state contact stresses, and the microstructure with proportionally scaled macroscopic experimental results. Although the grain sizes vary by a factor of more than 1,000 between experiment and simulation, the characteristic process parameters were reasonably reproduced, to some extent even allowing predictions of surface quality degradation due to tool wear. Using the experimentally validated model, we studied time-resolved stress profiles within the ferrite/steel work piece as well as maps of the microstructural changes occurring in the near-surface regions. We found that blunt abrasives combined with elevated temperatures have the greatest and most complex impact on near-surface microstructure and stresses, as multiple processes are in mutual competition here.

A multi-particle abrasive model for investigation of residual stress in belt grinding of titanium alloys

The titanium alloy blade is a key part of an aero-engine, but its high surface efficiency and precision machining present technical problems. Belt grinding can effectively prolong the fatigue life of the blade and enhance the service performance of the aero-engine. However, the residual stress of the workpiece after belt grinding directly affects its service performance and life. The traditional single particle abrasive model simulation is limited in exploring the influence of grinding process parameters on surface residual stress. In this study, an ABAQUS simulation model of multi-particle belt grinding is established for titanium alloy material, a finite element (FE) simulation is conducted with different technological parameters, and the results are analysed. The critical belt grinding experiment is conducted on thin-walled titanium alloy parts, and the distribution characteristics of surface residual stress after grinding are studied to understand the influence of grinding parameters on the formation of surface residual stress. Comparing the results of the FE simulation and the grinding experiment, the common law of stress change and the prediction model are obtained. The results show that the multi-particle belt grinding simulation is consistent with the belt grinding experiment in terms of the influence of grinding parameters on residual stress. The simulation can serve as a guide in actual belt grinding to some extent. Directions for improving the multi-particle abrasive simulation model are discussed.

A Novel Monitoring Method for Belt Wear State Based on Machine Vision and Image-processing Under Varying Grinding Parameters

The wear state of abrasive belt is one of the important factors affecting the grinding precision of belt grinding processes. Accurate monitoring of abrasive belt wear can not only provide the basis for accurate material removal model to improve grinding accuracy, but also can replace the belt to avoid surface burn in time. However, most of the existing abrasive belt wear monitoring methods are only suitable for monitoring the belt wear state under specific grinding parameters, are not universal. This paper introduces a method of belt wear state monitoring based on machine vision and image-processing. All the surface images of the belt were obtained from the new belt to the worn-out of the belt by a non-contact electron microscope. The features of abrasive belt surface images are extracted from RGB color space and wavelet texture. By analyzing the trendency of the extracted features in the whole grinding process, the wear state is divided into three categories. Three image features related to the wear state are selected: the first order distance of color component R, the entropy of horizontal subgraph, and vertical subgraph of texture feature. Based on the selected features and the random forest classification algorithm, the wear state classifier of abrasive belt is established. The performance of the classifier is verified and evaluated by using the data subset of different images. The results show that the proposed method has high recognition accuracy for the belt wear state, and the accuracy can reach 99% in the accelerated wear stage. The proposed method is suitable for the monitoring of the belt wear state by the surface images of the abrasive belt measured under different grinding parameters and different measurement parameters.