Grinding Wheel Wear Research Articles

Grinding force modeling in ultrasonic-assisted face grinding of ZrO2 ceramics and influence of grinding wheel wear on its accuracy

Grinding force modeling in ultrasonic-assisted face grinding of ZrO2 ceramics and influence of grinding wheel wear on its accuracy

Grinding of C/SiC ceramic matrix composites: Influence of grinding parameters on tool wear

C/SiC Ceramic Matrix Composites (CMCs) have been identified as a key material for improving high-speed braking systems and aerospace components as they offer low density, and high specific strength at high temperatures. Grinding is often used for the machining stage due to the high hardness, heterogeneity, and brittle nature of CMSs. Previous studies have explored the effect of grinding parameters, but most of them do not indicate whether they have used the same grinding wheel for all tests. In fact, there is limited understanding of how wear impacts process performance over the grinding wheel's lifespan. In this work, the effect of grinding wheel wear on cutting forces is addressed and grinding wheel topography is analyzed with the objective of identifying a parameter that allows to quantify grinding wheel wear in a non-destructive way. Results have shown that, after a short conditioning stage, cutting forces increase approximately linearly with the machined length. For the machining conditions analyzed, normal forces increase 200 % in the first machined meter (first stage), then rise 17 % per meter thereafter (second stage). Tangential forces rise 300 % in first meter and then climb 27 % per meter subsequently. In this second stage, force ratio approaches a constant value and the generation of flat surfaces on the diamond grains is the dominating wear mechanism. Under such conditions, 3D surface roughness parameters Sa, Sq, Spk and Sku have been proven to be useful for monitoring wheel wear.

Research on On-Line Monitoring of Grinding Wheel Wear Based on Multi-Sensor Fusion.

The state of a grinding wheel directly affects the surface quality of the workpiece. The monitoring of grinding wheel wear state can allow one to efficiently identify grinding wheel wear information and to timely and effectively trim the grinding wheel. At present, on-line monitoring technology using specific sensor signals can detect abnormal grinding wheel wear in a timely manner. However, due to the non-linearity and complexity of the grinding wheel wear process, as well as the interference and noise of the sensor signal, the accuracy and reliability of on-line monitoring technology still need to be improved. In this paper, an intelligent monitoring system based on multi-sensor fusion is established, and this system can be used for precise grinding wheel wear monitoring. The proposed system focuses on titanium alloy, a typical difficult-to-process aerospace material, and addresses the issue of low on-line monitoring accuracy found in traditional single-sensor systems. Additionally, a multi-eigenvalue fusion algorithm based on an improved support vector machine (SVM) is proposed. In this study, the mean square value of the wavelet packet decomposition coefficient of the acoustic emission signal, the grinding force ratio of the force signal, and the effective value of the vibration signal were taken as inputs for the improved support vector machine, and the recognition strategy was adjusted using the entropy weight evaluation method. A high-precision grinding machine was used to carry out multiple sets of grinding wheel wear experiments. After being processed by the multi-sensor integrated precision grinding wheel wear intelligent monitoring system, the collected signals can accurately reflect the grinding wheel wear state, and the monitoring accuracy can reach more than 92%.

A study of diamond grinding wheel wear condition monitoring based on acoustic emission signals

A study of diamond grinding wheel wear condition monitoring based on acoustic emission signals

Research on Cloud-Edge-Device Collaborative Intelligent Monitoring System of Grinding Wheel Wear State for High-Speed Cylindrical Grinding of Bearing Rings

Aiming at the problems of grinding wheel wear during high-speed cylindrical grinding, communication delays, and slow response during data acquisition, processing, and system operation, an intelligent online monitoring technology frame for CNC manufacturing units is proposed, incorporating a real-time-perception grinding mechanism and a cloud-edge device. Based on the grinding data and grinding wheel wear mechanism, a monitoring model using multi-sensor information fusion is constructed to assess the grinding wheel wear state. In addition, edge data acquisition and online monitoring software have been developed to improve the speed of data transmission and processing. Finally, based on the proposed framework, a cloud-edge device collaborative intelligent monitoring system for assessing grinding wheel wear during high-speed cylindrical grinding of bearing rings is constructed. It improves the grinding quality and efficiency, reduces the grinding cost, and incorporates remote control functionality.

Pressure Distribution and Wear of Grinding Wheel in Ultra-Thinning Process of LiTaO3 Wafer

Pressure Distribution and Wear of Grinding Wheel in Ultra-Thinning Process of LiTaO3 Wafer

Research on wear state identification of ordered grinding wheel for C/SiC composites based on DBO-ELM

In the grinding process of 2.5D C/SiC composites by the ordered grinding wheel, the signals are more complicated due to the special structure of the material and the ordered abrasive clusters, which makes it difficult to identify the wear state of the grinding wheel. To solve this problem, this study employs the analysis methods of direct and indirect. The acoustic emission signals, grinding force and grinding temperature signals throughout the entire lifespan of the grinding wheel were collected, and the topography of the grinding wheel was photographed. The difference in wear behavior between the ordered grinding wheel and the traditional disordered grinding wheel was compared and analyzed. The corresponding relationship between the ordered grinding wheel wear behavior and various signals was studied in depth. The results showed that the initial wear occurred during the 1st-80th grinding process, the stable wear occurred during the 81st-224th grinding process, and the serious wear occurred during the 225th-350th grinding process. Additionally, this research further extracted the key features of various signals, and used the Pearson correlation coefficient to identify the features highly related to grinding wheel wear. Subsequently, LDA was used to reduce the dimension of individual signal types. By comparing the recognition effects of different signal type combinations, the optimal combination was determined. Finally, this research proposed a DBO-ELM model for the recognition and classification of ordered grinding wheel wear state. Compared to four common machine learning models, namely KNN, SVM, ELM, and BP, the proposed DBO-ELM model demonstrated a classification accuracy of 94.86 %, which was increased by 25.57 %, 16 %, 16 % and 7.86 % respectively. This demonstrates that the DBO-ELM model proposed in this research has certain advantages and potential in the wear state recognition of abrasive clusters ordered grinding wheel.

Research on grinding wheel wear measurement methods: Current status and future perspectives

Research on grinding wheel wear measurement methods: Current status and future perspectives

Precision evolution model for the grinding-wheel wear in ELID grinding of nonstandard ultra-precision bearing raceways

Precision evolution model for the grinding-wheel wear in ELID grinding of nonstandard ultra-precision bearing raceways

Precision forecasting of grinding wheel Wear: A TransBiGRU model for advanced industrial predictive maintenance

In intelligent manufacturing, accurate prediction of grinding wheel wear is essential to reduce maintenance costs and improve production efficiency. To achieve accurate forecasts, this paper introduces a grinding wheel wear prediction model, TransBiGRU, incorporating a Transformer encoder, Bidirectional Gated Recurrent Unit (BiGRU), positional encoding layer, and position-wise feedforward layer. The model extracts features by analyzing current signal characteristics in basic statistical, time, and frequency domains during the machining process. Training is conducted through K-fold cross-validation to ensure model stability. The experimental results indicate that the model achieved good performance with Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R2) evaluation metrics, obtaining values of 2.0898, 3.262, and 0.9338, respectively. Systematically reducing modules validates the importance of each module in enhancing predictive capabilities. This research achieves the practical application of low-cost current sensors in optimizing maintenance plans and reducing downtime in predicting grinding wheel wear.

A Method for Identifying the Wear State of Grinding Wheels Based on VMD Denoising and AO-CNN-LSTM

Monitoring the condition of the grinding wheel in real-time during the grinding process is crucial as it directly impacts the precision and quality of the workpiece. Deep learning technology plays a vital role in analyzing the changes in sensor signals and identifying grinding wheel wear during the grinding process. Therefore, this paper innovatively proposes a grinding wheel wear recognition method based on Variational Mode Decomposition (VMD) denoising and Aquila Optimizer—Convolutional Neural Network—Long Short-Term Memory (AO-CNN-LSTM). The paper utilizes Acoustic Emission (AE) signals generated during grinding to identify the condition of the grinding wheel. To address noise interference, the study introduces the VMD algorithm for denoising the sample dataset, enhancing the effectiveness of neural network training. Subsequently, the dataset is fed into the designed Convolutional Neural Network—Long Short-Term Memory (CNN-LSTM) structure with AO-optimized parameters. Experimental results demonstrate that this method achieves high accuracy and performance.

An error compensation method for single point oblique axis grinding considering the grinding wheel wear

With the increasing amount of high quality optical products, the manufacturing requirement for the high-precision optical glass lenses is on the rise. Grinding is typically the main manufacturing process for the optical mold. The degree of wheel wear has a direct impact on the precision of the grinding process. In this paper, the wear rule of grinding wheel in the grinding process of optical aspheric surface is studied with the in-site wear measurement system. A new image processing method and a wear degree evaluation method are proposed. The wear prediction model for the grinding wheel is established. Then a modified compensation method of optical aspheric surface considering grinding wheel wear is proposed. The optical aspheric surface is then compensated using a modified multinomial equation considering grinding wheel wear. The results demonstrate that the profile error of optical aspheric surface can be reduced to less than 150 nm. The proposed compensation method that takes into account the grinding wheel wear can highly improve the grinding precision and efficiency of optical aspheric surface as compared with the conventional compensation method.

A multi-sensor monitoring methodology for grinding wheel wear evaluation based on INFO-SVM

It’s a significant challenge to accurate and efficient evaluation of grinding wheel wear. The evaluating grinding wheel wear traditional evaluation model has several weaknesses, including low accuracy, poor efficiency, and the need for a large database. To address these issues, an evaluating grinding wheel wear optimize model method is proposed based on weIghted meaN oF vectOrs optimized Support Vector Machine (INFO-SVM), and an data processing method is proposed based on Whale Optimization Algorithm to optimize Variational Mode Decomposition (WOA-VMD). Firstly, the grinding wheel wear was analyzed by grinding wheel and workpiece topography images. Secondly, the WOA-VMD data processing method has distinguished frequency bands between the grinding process and environmental noise signal, the method thereby eliminating environmental noise to enhance the signal-to-noise ratio in evaluating grinding process signals. Based on ReliefF algorithm established dataset, finally, the INFO-SVM algorithm method to evaluate grinding wheel wear has verified the robustness, effectiveness, and computational efficiency. The experimental results demonstrate the method's effectiveness in noise reduction, high accuracy, fast recognition speed, and strong robustness. Therefore, multi-sensor monitoring holds promising potential for application in the field of grinding wheel wear evaluation.

Online monitoring method of non-cylindrical wheel wear for gear grinding based on dynamic force model

Traditional monitoring methods of grinding wheel wear mainly take on-machine measurement systems or black-box algorithms. The first method has poor real-time performance for it can only be implemented after machining, making it difficult to adjust grinding processes in time. The second method has not revealed the mechanism of wear, resulting in poor model accuracy and flexibility. Besides, previous models are mainly for cylindrical wheels in surface grinding, while the properties of non-cylindrical wheel wear for gear grinding are different. To address this issue, a novel online monitoring method of non-cylindrical wheel wear for gear grinding is proposed. Firstly, a new parameter is defined based on grain height variation to describe the non-cylindrical wheel wear condition, avoiding the problem of underestimating the actual results due to a small measurement range. Then, a theoretical model for dynamic grinding forces is established considering more grain parameters and more complicated and realistic shape, so that on the basis of that model, the online monitoring method for wheel wear is proposed. Finally, experiments under different grinding conditions are conducted to verify the proposed models. The average calculation error of the grinding force model in different grinding parameters is 6.67%, and the maximum prediction error of the monitoring method under different wheel wear conditions is 8.2%.

Electrostatic induction–based online monitoring of grinding wheel wear

Electrostatic induction–based online monitoring of grinding wheel wear

Experimental research on wear mechanism of diamond wheels for grinding Cf/SiC composites grooves

Aiming at the problems of low processing efficiency, low groove shape accuracy and poor surface quality caused by grinding wheel wear in the grinding process of Cf/SiC composite grooves. In this paper, based on the motion mode of grinding wheel grinding, the wear volume formula of grinding wheels was analyzed, and the grinding wheel wear comparison experiment of different bond diamond grinding wheels grinding Cf/SiC composite grooves was carried out, and the more suitable kind of diamond grinding wheel for grinding Cf/SiC composite grooves was selected. From the experimental results, compared with the ceramic-bonded diamond wheel and resin-bonded diamond wheel, the grinding times of electroplated diamond wheel grinding Cf/SiC composites were increased by 20 and 104, respectively, and the surface roughness of the groove was most stable and the processing damage was smallest. Therefore, the best quality of grinding Cf/SiC composites groove was obtained by the electroplated diamond grinding wheel, and its life in grinding Cf/SiC composites was the longest. At the same time, the wear mechanism of the electroplated grinding wheel was explored, the wear on the bottom surface of the electroplated grinding wheel was more serious than that on the circumferential surface. The wear on the bottom surface was mainly wear and tear, and it gradually diffused along the inner diameter of the grinding wheel with the increasing grinding times.

Diamond grinding wheel wear characterization after finishing cemented tungsten carbide cutting inserts

Diamond grinding wheel wear characterization after finishing cemented tungsten carbide cutting inserts

Accurate prediction and compensation of machining error for large components with time-varying characteristics combining physical model and double deep neural networks

The accurate prediction and compensation of machining error for large components are always difficult due to the complex and time-varying intrinsic interactions during material removal process. The machining error generation of large components is directly related with localized tool-workpiece deformation, which is a function of the system stiffness and the machining force. Both system stiffness and machining force pattern can hardly be recognized and determined explicitly only by physical modeling due to the time-varying characteristics of machining process. Therefore, it is important to utilize the physical model to reflect the process fundamentals, and is necessary to calibrate these two critical variables by fusing the process data. In this paper, by investigating the large shaft precision grinding process, a hybrid ‘physical model + artificial intelligence’ approach is proposed. The physical model is based on the double-integral of material removal over time and space, and also considers the deformation induced by the grinding force and the grinding wheel wear evolution. Meanwhile, the double deep neural networks are built, in which a convolution neural network (CNN) is used to obtain the instant system stiffness based on process data, while the grinding force pattern of current pass is calibrated by the instant system stiffness. A long short-term memory (LSTM) network with two sub-networks is connected to CNN to predict the system stiffness and grinding force pattern for subsequent pass. The experimental results show that the proposed method can achieve higher prediction and compensation accuracy of machining error for large shaft grinding with different profiles. Furthermore, the quantitative output of the double neural networks reveals that the system stiffness decreases first and then stabilizes, and the grinding force evolution shows diverse patterns during grinding, which can be used to describe the time-varying characteristics of the grinding system during long-cycle machining process.

Acoustic emission identification of wheel wear states in engineering ceramic grinding based on parameter-adaptive VMD

In engineering ceramic grinding, the wheel wear states have an important influence on the processing efficiency and grinding quality. To address the difficulty of online monitoring of wheel wear states, a signal reconstruction method based on parameter-adaptive variational modal decomposition was proposed. Experiments on the entire life cycle of the grinding wheel were performed, and the wear states were classified based on the observed surface morphologies of the wheel and workpiece. Subsequently, acoustic emission signals were collected by nodes, and high correlation frequency bands were chosen and reconstructed. The signal features were extracted, and the wear states were identified based on a random forest optimization feature combination, finally. In this study, taking alumina ceramic as an example, eight groups of single-factor experiments were designed based on cumulative grinding depth to determine the optimal linear speed of the grinding wheel, worktable moving rate, and other process parameters. A total of 160 groups of AE signal samples were obtained. The results show that feature extraction by frequency bands can better reflect the changes in the grinding wheel wear states than the overall signal. In addition, optimization feature combinations based on the bands after decomposition, filtering, and reconstruction can divide and identify the various wear states more effectively. The comprehensive identification accuracy reached 90.6%. The accuracy of identifying the severe wear state reached 100%. Thus, the proposed method provides theoretical guidance for the practical industrial applications of online monitoring of wheel wear states.

Grinding of composite materials

Grinding plays an important role in ensuring the final machining quality in the manufacturing process of composite parts. Problems such as grinding damage, grinding wheel wear and loading undermine the grinding efficiency and quality. This review presents relevant research progress in the field of composites grinding in recent years from the aspects of material removal mechanisms, surface integrity, and advanced grinding technologies. It further discusses the common problems of composites grinding and summarizes grinding process strategies to suppress damage. It can provide an in-depth understanding of the fundamental mechanisms involved in composites grinding and solutions to the composites grinding problems.