Grinding Wheel Wear Research Articles

Effect of dressing method and dressing overlap ratio on grinding characteristics and grinding wheel wear

Effect of dressing method and dressing overlap ratio on grinding characteristics and grinding wheel wear

Three-dimensional topography modelling and grinding performance evaluating of micro-structured CVD diamond grinding wheel

Micro-structured chemical vapour deposition (CVD) diamond grinding tools are attracting increasing attention in precision grinding and micro-grinding because of their advantages in low grinding forces, high machining accuracy and good wear resistance. A well-developed wheel topography model for this kind of emerging grinding tool not only can facilitate the analysis and optimization the grinding process, but also is the essential basics for the intelligent machining. In this paper, a novel three-dimensional surface topography model of original CVD diamond grinding wheels was firstly established based on the multiple random data of the shape, size, posture and distribution of the CVD diamond grains. And the ground surface topography model was simulated depending on the established wheel model and grinding kinematics. Secondly, the models were verified by the results of grinding experiments in the means of topographical characteristics, surface roughness and PSD. Then, the influence of grinding parameters and wheel wear on the grinding performance of CVD diamond wheels was discussed. Finally, the micro-structured CVD grinding wheel models were presented by coupling different forms of micro-structures. And the effects of micro-structure parameters including widths, inclination angles, structuring ratios and types on the grinding performance of micro-structured CVD diamond wheels were investigated quantitatively. This study provides a quantitative and effective method for the design of CVD diamond grinding wheels and their surface structuring. Furthermore, the proposed micro-structured analysis method is also available for other structured grinding wheels.

Preface

The 2022 2nd International Conference on Mechatronics, Automation and Intelligent Control (MAIC 2022) was successfully held on September 16-18, 2022 in Wuhan, China (virtual conference). MAIC 2022 focused on the latest research results in advanced intelligent control of automation systems, including both theoretical advances and practical implementations, which are becoming more and more popular in industry and in our daily lives.We had the honor of having invited Prof. Peng Zhao from Zhejiang University, China to serve as our Committee General Chair. The conference was composed of keynote speeches, oral presentations, and online Q & A discussion, attracting 100 delegates worldwide. Firstly, keynote speakers are each allocated 30-45 minutes to hold their speeches. Then in the next part, oral presentations, the excellent papers selected are presented by their authors one by one.During the conference, three distinguished professors invited address their keynote speeches. For a start, Prof. Yajun Liu from South China University of Technology, China performed a thought-provoking speech on A Novel Signal Processing Method of Coriolis Mass Flowmeter Based on SVR and Hilbert Transform. To improve the measurement accuracy of mass flow and reduce the impact of noise, a novel signal processing method for Coriolis mass flowmeter based on SVR and Hilbert transform was proposed in this study. Ad then, Prof. Li Guo from Hunan University, China made a speech on the Research on Acoustic Emission Intelligent Monitoring in Grinding Engineering Ceramics. He studied the relationship between the acoustic emission signal of high speed grinding of engineering ceramics and grinding force, grinding temperature, and carried out a high precision AE monitoring of diamond grinding wheel wear in engineering ceramics grinding, solving the problem of acoustic emission monitoring in engineering ceramics grinding. Lastly, Prof. Heming Jia from Sanming University, China made a report on Swarm Intelligence Optimization: Advances in Algorithms and Applications. He introduced the current popular SIOAs and a novel technique called Remora Optimization Algorithm (ROA) and analyzed their search mechanism and existing limitations in different optimization tasks. Their brilliant speeches had triggered heated discussion. And every participant praised this conference for disseminating useful and insightful knowledge.The proceedings of MAIC 2022 are a compilation of the accepted papers and represent an interesting outcome of the conference. These papers feature but are not limited to the following topics: Advanced Motion Control, Electric Automation, Artificial Intelligence, Modeling and Design, Unmanned Vehicle, etc. All the papers have been checked through rigorous review and processes to meet the requirements of publication.We would like to express our sincere gratitude to all the keynote speakers, peer reviewers, and all the participants who supported and contributed to MAIC 2022. Particularly, our special thanks go to the Journal of Physics: Conference Series, for all the efforts of its colleague in publishing this paper volume. We firmly believe that MAIC 2022 had turned out to be a forum for excellent discussions that enable new ideas to come about, promoting collaborative research.The Committee of MAIC 2022List of titles Committee member, Conference General Chair, Technical Program Committee Chairs, Local Organizing Committees, Technical Program Committees are available in this Pdf.

Modelling of grinding mechanics: A review

Grinding is one of the most widely used material removal methods at the end of many process chains. Grinding force is related to almost all grinding parameters, which has a great influence on material removal rate, dimensional and shape accuracy, surface and subsurface integrity, thermodynamics, dynamics, wheel durability, and machining system deformation. Considering that grinding force is related to almost all grinding parameters, grinding force can be used to detect grinding wheel wear, energy calculation, chatter suppression, force control and grinding process simulation. Accurate prediction of grinding forces is important for optimizing grinding parameters and the structure of grinding machines and fixtures. Although there are substantial research papers on grinding mechanics, a comprehensive review on the modeling of grinding mechanics is still absent from the literature. To fill this gap, this work reviews and introduces theoretical methods and applications of mechanics in grinding from the aspects of modeling principles, limitations and possible future trendencies.

Deep Learning-Based Feature Extraction of Acoustic Emission Signals for Monitoring Wear of Grinding Wheels.

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.

Study on the CBN Wheel Wear Mechanism of Longitudinal-Torsional Ultrasonic-Assisted Grinding Applied to TC4 Titanium Alloy.

In this study, the CBN (cubic boron nitride) wheel wear model of TC4 titanium alloy in longitudinal-torsional ultrasonic-assisted grinding (LTUAG) was established to explore the grinding wheel wear pattern of TC4 titanium alloy in LTUAG and to improve the grinding efficiency of TC4 titanium alloy and the grinding wheel life. The establishment of the model is based on the grinding force model, the abrasive surface temperature model, the abrasive wear model, and the adhesion wear model of TC4 titanium alloy in LTUAG. The accuracy of the built model is verified by the wheel wear test of TC4 titanium alloy in LTUAG. Research has shown that the grinding force and grinding temperature in LTUAG increase with the increase of the grinding depth and workpiece feed rate and decrease with the increase of the longitudinal ultrasonic amplitude. It also shows that the grinding force gradually decreases with the increase of the grinding wheel speed, while the grinding temperature gradually increases with the increase of the grinding wheel speed. In addition, the use of LTUAG can significantly reduce the wear rate of the grinding wheel by 25.2%. It can also effectively reduce the grinding force and grinding temperature.

Probabilistic Aspects of Modeling and Analysis of Grinding Wheel Wear.

In this article, the methodology of using probabilistic models of the grinding tool wear process is presented. Probabilistic modeling with empirical data allowed determining the values of other important process features. Among them, the distribution of active grains lifetime or distribution of cumulative attritious wear of the grinding grain apex could be distinguished. The results of modeling and wear analysis of grinding wheels as well as experimental results on peripheral grinding with zoned grinding wheels are presented. The analyzed grinding wheels consisted of three layers: two identical external layers with conventional structure and one internal layer containing the addition of abrasive aggregates. The external layers were profiled by chamfering the edges. As a result, their nominal surfaces were conical. The internal layer had a cylindrical shape and was designed for smoothing the surface after machining with external part. The tools were designed to increase the grinding efficiency and hence a good quality of machined surfaces could be acquired. For the experimental tests, the Ti6Al4V titanium alloy was used. It was found that the change in the shape and position of the grinding zone, as a result of volumetric wheel wear, caused a significant change in fracturing intensity. In the case of multilayer grinding tools, the wear process depends on the physical properties of each layer and their participation during machining of the workpiece. The presented methodology could be applied to a study on the machining process stages, which concerns temporary states and their variability according to the machining time.This makes it possible to reduce the cost of developing new tools dedicated to specific applications.

Analysis of the thermo-mechanical load and productivity during force-compliant grinding of pcBN

Superhard cutting tool materials such as polycrystalline cubic boron nitride (pcBN) are increasingly demanded in the aerospace and automotive industries. Although grinding is a common manufacturing technology for pcBN cutting tools, the lack of knowledge regarding thermal and mechanical loads during grinding of pcBN can cause workpiece damage and high grinding wheel wear. Therefore, this work presents a new approach of force-compliant grinding (FCG) of different pcBN specifications considering the thermal and mechanical load as well as the productivity. During the FCG approach, the normal force between the grinding wheel and the pcBN workpiece was set to a predefined value before material removal starts. Based on an evaluation of the grinding force ratio, the temperature of contact and the material removal rate, a modification of Preston’s theory of polishing was found to be applicable to predict the productivity. Accordingly, both an increasing grinding normal force and grinding force ratio lead to an increasing productivity during FCG of pcBN. However, the quantitative models developed in this work enable to predict the productivity as well as the mechanical and thermal load during FCG of pcBN. These models hence contribute to the extension of process knowledge for grinding pcBN and thus to prevent damage to the workpiece and high wear of the grinding wheel.

Evaluation of the grinding process utilizing an auxiliary compressed air jet on cleaning the grinding wheel surface

Grinding is one of the most complex and precise machining operations, with the performance of the grinding wheel being heavily reliant on its surface conditions. Removed chips tend to be forced back to the wheel surface due to the intense heat generated in the grinding zone. The loaded chips block the wheel's grinding capability, resulting in increased grinding force and temperature, wheel chatter, and workpiece thermal degradation. To solve these problems, this research examines the effectiveness of an auxiliary compressed air jet system during the grinding of spk tool steel with an alumina grinding wheel to clean the wheel surface of lodged chips and then the percentage of wheel loading for different tests will also be evaluated. The compressed air nozzle was examined from a variety of perspectives (0, 30, 45, and 60). Grinding with and without a cleaning system, as well as conventional cutting fluid, were also employed as comparisons. In certain output results, input parameters such as grinding wheel speed (20 m/s), cutting depth (20 m), and feed rate (100 mm/s) were maintained constant. When compared to other experiments with different cleaning nozzles, the optimal inclination angle of 45°, which allows the maximum air to reach the interface between the wheel surface and chips, removed chips by pushing them off the wheel surface. Other cleaning nozzles, such as 0°, 30°, and 60°, were less successful. The incidence angle of 45° produced the best results in terms of surface roughness, diametrical grinding wheel wear, G-ratio, tangential forces, specific energy, and wheel loading analysis. Image processing was used to detect and identify chips accumulated between abrasive grains on the wheel surface. To better understand the structure of the wheel and workpiece surface, optical and scanning electron microscopy pictures were taken and analyzed. This research adds to our understanding of the grinding wheel loading mechanism and its dynamics.

Multi-information fusion recognition model and experimental study of grinding wheel wear status

Multi-information fusion recognition model and experimental study of grinding wheel wear status

Acoustic emission based grinding wheel wear monitoring: Signal processing and feature extraction

Surface grinding, being one of the later stages of a manufacturing process, is crucial to achieve the desired workpiece quality by maintaining proper wheel condition. Machine learning and acoustic emission (AE) detection have demonstrated potentials for developing an effective on-line wheel wear monitoring system. However there are inconsistencies among existing studies regarding to signal sampling and feature extraction methods. In the present paper, surface grinding experiments using medium carbon steel workpieces were conducted to determine the effects of using different signal analysis window lengths, feature types, and AE sensor bandwidths on the wheel wear detection performances. In addition, the use of AE transient spikes occurred during wheel entry and exit were analyzed. Feature were extracted from time domain (TD), frequency domain (FD), and time–frequency domain (TFD) representations of the measured AE signals, which were then refined using sequential floating forward selection (SFFS) algorithm to reduce feature redundancy. Support vector machine (SVM) algorithm with linear kernel was used for pattern classification. A number of insights for future monitoring system development were obtained. Firstly, while the lower frequency AE sensor (below 100 kHz) provided adequate classification performances, the higher frequency AE sensor (above 100 kHz) produced more robust results. Secondly, 100% classification rate was achievable using features derived from Fast Fourier Transform (FFT) in combination with descriptive statistics. Thirdly, it was determined that a window length as short as 1 ms (1000 data points) could capture enough signal information using FFT with the higher frequency AE sensor. Lastly, the choice sampling instance did not significantly impact classification outcomes.

Wear assessment of microcrystalline and electrofused aluminum oxide grinding wheels by multi-sensor monitoring technique

Combining the manufacturing processes with a sophisticated monitoring system is becoming more frequent in industry, following the trend in ‘smart manufacturing’. Multi-sensor monitoring techniques can enhance the efficiency and precision of the manufacturing process. Although many studies have been focused on the grinding process, we are still a long way from a complete understanding, which represents a challenge to the scientific community. The aim of this research was investigate the wear behavior of microcrystalline aluminum oxide grinding wheels, in comparison with a conventional electrofused aluminum oxide grinding wheel, through acoustic emission (AE) monitoring and the use of force transducers. The difference between the two cases is that in the manufacture of microcrystalline aluminum oxide grinding wheels microcrystalline abrasive grains are added to the composition (representing 45 %). A specific experimental rig was assembled for the measurement of the grinding cutting force and AE during the external cylindrical plunge grinding process. Two specific material removal rates were selected for the grinding of nodular cast iron GGG70 workpieces. The experimental rig allows the radial grinding wheel wear to be determined employing an AE-based contact recognition procedure. The wear behavior of the grinding wheel is evaluated by correlating the ground workpiece surface roughness measurements with the post-process analysis of both the grinding force components and AE signals. The results show that the wear behavior of the grinding wheels was dependent on the specific material removal rate used and was characterized by micro and macro-wear mechanisms. It was found through the surface roughness measurements and process characteristics that the addition of microcrystalline aluminum oxide grains has a significant influence on the wear behavior of the grinding wheel. Specifically, the microcrystalline grinding wheel presented lower magnitudes of radial wear when compared to the electrofused grinding wheel. The proposed monitoring method can be widely used to evaluate the wear of grinding wheels during the grinding process.

Grinding behavior of biomimetic fractal-branched silicon carbide ceramic inspired from leaf-vein structure

Silicon carbide ceramics are widely used in many industrial fields owing to their outstanding physical and chemical characteristics. However, their inherent hardness and brittleness make the grinding process very difficult compared to that involving ductile materials. In the present study, the effects of the biomimetic fractal-branched structure, inspired from the leaf-vein, on the grinding behavior of silicon carbide were investigated. Two biomimetic fractal-branched structures with different densities of micro-channels were designed and compared with the non-structured silicon carbide surface. The surface of the silicon carbide ceramic was textured through pulsed-laser ablation. Thereafter, the grinding experiment was conducted on the biomimetic fractal-branched and non-structured workpieces. The surface topography, subsurface damage, grinding force, grinding force ratio, surface roughness and grinding wheel wear were examined throughout the experiment. The experimental results indicated that the normal and tangential grinding forces for the fractal-branched structure surface are 7.61–18.21% and 8.34–26.13% lower than those for the non-structured surface. The grinding force ratio also increased significantly with an increase in the micro-channel density. In addition, a larger volume of coolant was transported through the grinding zone of the fractal-branched structure. The research results confirmed that the biomimetic fractal-branched structure on the silicon carbide surface enhanced the grinding performance and improved the grinding quality.

Numerical calculation of grinding wheel wear for spiral groove grinding

Due to its good cutting performance in titanium alloy machining, integral end mills are more and more used in machining aero-engine impeller blades. The tool spiral groove plays the role of chip acceptor and chip removal, and the accuracy of its parameters has an important effect on the cutting performance. In the grinding process of the spiral groove, the grinding wheel’s external grinding is mainly involved in the grinding task. The grinding wheel’s wear degree is related to the grinding time and grinding times of the grinding wheel, and the wear of the grinding wheel will lead to the change of the parameters of the spiral groove. To achieve the accurate solution of the grinding wheel surface wear profile, image processing technology was used to extract the spiral groove end section contour coordinates of the grinding wheel and fit them. The worn sand profile was solved based on the contact line principle, and the grinding wheel wear amount was obtained. The traditional reconstruction method was used to verify the algorithm. The results show that the accuracy of the reverse algorithm for the wear profile of the grinding wheel is relatively high.

Implementation of a Fatigue Fracture Approach to Grinding Wheel Wear Using Numerical Simulation

The paper proposes an approach to assessing the grinding wheel wear, which includes the wear of grains and pulling them out of the bond, based on fatigue failure under cyclic loads caused by cutting forces, with a period of the order of microseconds. This approach is implemented on the basis of stress life calculation in the ANSYS Fatigue module using loading data obtained from the numerical simulation of grain-workpiece interaction. The simulation results allows to obtain the grain wear area, the level of bond fatigue fracture and the possibility of self-sharpening of the grinding wheel for a given grain size and operation conditions.

Analysis of the grinding wheel wear and machining result during cutting edge preparation with elastic bonded grinding wheels

Cutting edge preparation can be considered as one of the most effective methods for optimizing the cutting tool life and the machining result. In this article, a preparation technique using elastic bonded superabrasive grinding wheels is applied. This method proved to be highly flexible in terms of the achievable cutting edge geometry. However, very little knowledge exists about the grinding wheel wear behavior and therefore the process reliability during cutting edge preparation. In this article, the impact of the process kinematics and parameters on the grinding wheel wear and preparation result is investigated and discussed. For this purpose, the impact of the depth of cut (20, 40 and 60 μm) was investigated, because it serves as the major setting parameter for the size of the cutting edge rounding. However, it also impacts the contact conditions and thermo-mechanical loads in the grinding wheel. The experimental results show that this preparation method achieves a high preparation accuracy and repeatability at small and medium depths of cut (20 and 40 μm). For a depth of cut of 60 μm, the preparation result became more inappropriate (increased deviation of the cutting edge shape from an ideal radius) with an increasing number of prepared cutting edges. Here, the soft grinding wheel exhibited a higher process stability in terms of repeatable machining results. The machining results correspond to the experimental findings from the grinding wheel radial wear Δrs. At a depth of cut of 20 and 40 μm, the grinding wheel radial wear for the hard bond was lower than for the soft bond. At a depth of cut of 60 μm, the grinding wheel radial wear for the hard bond exceeds the value for the soft bond by 10%. However, the grinding (G) ratios for the hard bond were higher for all investigated depths of cut. The highest difference was observed at a depth of cut of 20 μm. Here, the G ratio for the soft grinding wheel was 7.92 and for the hard one 18.53. Furthermore, an application scenario was introduced using suitable dressing cycles, so that up to 138,000 cutting edges (equals 34,500 indexable inserts) could be prepared with one grinding wheel using this preparation technique in industrial tool manufacturing applications.

Prediction of un-uniform grinding wheel wear based on instantaneous engagement of multi-axis grinding

Prediction of un-uniform grinding wheel wear based on instantaneous engagement of multi-axis grinding

Study on Wear of Grinding Wheel in Ultrasonic Assisted Dry Side Grinding of SiCf/SiC Composites

Study on Wear of Grinding Wheel in Ultrasonic Assisted Dry Side Grinding of SiC_f/SiC Composites

A lapping-based test method to investigate wear behaviour of bonded-abrasive tools

Grinding-wheel wear is a critical factor affecting grinding performance and tool cost. Unfortunately, wear tests – particularly with superabrasives – can be notoriously time-consuming. Therefore, a novel lapping-based method is proposed for investigating wear behaviour of the grit-bond system. Wear tests were performed in (i) lapping, (ii) surface grinding, and (iii) cylindrical grinding for a range of grit-shape aspect ratios and grit-toughness values for the same grit-bond systems. Results showed that all three methods yielded similar trends. This indicates that the lapping tests could be a viable substitute for lengthy grinding tests, resulting in shorter testing times and smaller specimen sizes.

Novel comparison concept between CBN and Al2O3 grinding process for eco-friendly production

Minimal Quantity of Lubrication was developed to achieve green manufacturing in machining process. In this study, parameters of cutting fluid flow, grinding wheel type and wheel cleaning system were evaluated to achieve the maximum potential of this technique in grinding process. The comparison of super abrasive (CBN) and conventional (Al2O3) grinding wheel, the cutting fluid flow (30 ml/h, 60 ml/h and 120 ml/h), and the wheel cleaning system were analyzed based on: surface roughness, roundness errors, grinding wheel wear, confocal of grounded surface, grinding power, acoustic emission and workpiece microscopy. In a novel way, this study compared the costs per piece and the carbon footprint in terms of CO2 emission for both abrasive tool application. For all the parameters analyzed, the CBN grinding with MQL outperformed the Al2O3 grinding, showing a superior performance not only in terms of surface finishing, on average, 29% lower surface roughness and dimensional deviation 9% lower but also regarding tool efficiencies, such as 127% lower diametrical wear, 42% lower acoustic emission, and 13% lower grinding power. Despite that, the cost per piece was also evaluated for the different lubri-cooling conditions and tools, and the cost per piece recorded for Al2O3 grinding was substantially lower than CBN grinding, irrespective of the lubri-cooling method employed. The cost of grinding for all MQL techniques analyzed with aluminum oxide wheel was, on average, 72% less than grinding with CBN wheel. Besides, the conventional method with the CBN wheel was 7% more expensive than the aluminum oxide wheel.