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Online monitoring method of non-cylindrical wheel wear for gear grinding based on dynamic force model
Grinding is a precision machining method widely used in the precision manufacturing. Wears of the grinding wheel are common during the grinding process that would lead to the decrease of manufacturing precision. To improve grinding precision, a grinding wheel wear prediction method for multi-axis grinding is presented in this paper. Due to the complex shape of grinding wheel and the surface-to-grind, they are represented on a group of virtual planes. In terms of the kinematics of five-axis machine tool, the simplified engagements between the grinding wheel and the workpiece are calculated on these planes. By composing these scattered engagements, the spatial instantaneous engagements are obtained. Next, the material volume removed by the infinitesimal on the profile of the grinding wheel is calculated accurately. Inversely, the abrasion loss of each infinitesimal can be obtained based on the grinding ratio. The abrasion loss distribution is determined by composing all the infinitesimals on the wheel’s profile. Finally, a free-form surface is ground by a cylindrical wheel to verify the proposed method. It shows that the wear prediction of the grinding wheel can provide a basis for predicting the tool’s time of failure.
The service performance of the turbine blade root of an aero-engine depends on the microstructures in its superficial layer. This work investigated the surface deformation structures of turbine blade root of single crystal nickel-based superalloy produced under different creep feed grinding conditions. Gradient microstructures in the superficial layer were clarified and composed of a severely deformed layer (DFL) with nano-sized grains (48-67 nm) at the topmost surface, a DFL with submicron-sized grains (66-158 nm) and micron-sized laminated structures at the subsurface, and a dislocation accumulated layer extending to the bulk material. The formation of such gradient microstructures was found to be related to the graded variations in the plastic strain and strain rate induced in the creep feed grinding process, which were as high as 6.67 and 8.17 × 107 s-1, respectively. In the current study, the evolution of surface gradient microstructures was essentially a transition process from a coarse single crystal to nano-sized grains and, simultaneously, from one orientation of a single crystal to random orientations of polycrystals, during which the dislocation slips dominated the creep feed grinding induced microstructure deformation of single crystal nickel-based superalloy.
Compared with Conventional Grinding (CG), the Ultrasonic Vibration-Assisted Grinding (UVAG) is more competitive for the machining of difficult-to-cut materials. In this article, a novel ultrasonic vibration plate sonotrode that enables the special longitudinal full-wave and transverse half-wave vibration modes was proposed. The vibration characteristics of the proposed sonotrode was theoretically studied based on the apparent elasticity and Rayleigh methods, achieving a single longitudinal workpiece ultrasonic vibration with an amplitude of 7.6 μm. A superior vibration uniformity was achieved, as indicated by the coupling coefficient of 0.5. Based on the proposed method, the normal and tangential grinding forces decreased by 35 % and 39 % in comparison with the CG, and an improved machined surface was obtained in the UVAG of Ti-6Al-4V, with the following characteristics: vibration amplification of 7.6 μm; a cut depth of 0.1 mm; a workpiece feed rate of 100 mm/min; and a grinding speed of 30 m/s.
Grinding wheel wear adversely affects the quality of machining surface, the working ability of grinding wheel as well as the grinding machine during the machining process. Challenges in the machining process, especially in the process of grinding Ti-6Al-4V alloy, which is a material with high adhesion, durability, and toughness in combination with poor thermal conductivity that leads to low economic - technical indicators in the grinding process, susceptibility to disabilities of the grinding details, and fast wear of the grinding wheel. Therefore, the precise prediction of the grinding wheel wear and surface roughness in the machining process is a prerequisite to minimize the damages caused by the grinding wheel wear when grinding Ti-6Al-4V alloy. This work presents a model for monitoring grinding wheel wear conditions using the grinding force signal obtained at the processing time in combination with the adaptive neural fuzzy inference system - Gaussian process regression and Taguchi analysis to predict the abrasive wear in the different stages of grinding process. Experimental results show the ability to accurately predict the amount of grinding wheel wear and surface roughness from the proposed model when grinding Ti-6Al-4V alloy. With the ability to accurately predict the indicators of surface roughness and abrasive wear with a corresponding average error of 0.31% and the reliability percentage of the measurement of 98%, the proposed model can be used in the industry for the online forecast of surface roughness and the time to repair the grinding wheel directly in the grinding process.
Cemented carbides are brittle-hard materials. Their properties are adjusted by the chemical composition, especially the binder fraction and the average hard phase grain size. This research work focuses on grinding of cemented carbides with tungsten carbide (WC) as hard phase and cobalt (Co) as binder phase material. Previous work showed that the WC-Co cemented carbide specification influences the grinding behavior. This study focuses on the influence of the cemented carbide specification on the grinding wheel wear. For this purpose, grinding tests were carried out with cemented carbides that differ in cobalt content and average WC grain size. Based on the thermo-mechanical load collective and the grinding wheel wear, conclusions were drawn about the influence of the cemented carbide specification on the wear behavior of the grinding wheel and the wear mechanisms.
Wear and life expectancy of a nickel-electroplated monolayer of cubic boron nitride grinding wheels are characterized based on the wheel surface topological evolution, observed after grinding Inconel 718 super-alloys. The wheel is for surface or cylindrical grinding, and having 250 mm diameter, 10mm thickness and B40/50 coarse grit size. A unique grit-workpiece interaction process, leading to a non-uniform spatial distribution of the grit wear has been identified. Largest grits have been observed to pullout rapidly, resulting in load redistribution to their surroundings, and leading to the attritious and fracture wear phase. The detailed analysis showed that the stresses on the cutting grits arising from the thermal shock are 3-5 folds those arising from mechanical cutting forces, and reach an order of magnitude differences for the high efficiency deep grinding (HEDG) process. It is also found that the grit wear rate is primarily dependent on the workpiece feed rate rather than the grinding wheel speed. The total wheel life is then constructed as the sum of pullout life (Phase-I) and attritious and fracture wear life (Phase-II). Model predictions for the total wheel life compare well to the experimental observations. This facilitates comparisons of different types of grinding configurations and design space exploration. As an example, the HEDG process is compared to a regular high speed grinding, and it is observed that HEDG configuration can deliver much higher material removal for the same amount of wheel wear.
By using depth from focus (DFF) alone to obtain the 3D wear area reconstruction of grinding wheel, the depth values in the 3D wear area image are not continuous while by using shape from shading (SFS) alone, the 3D wear area cannot be fully obtained. A novel method that uses Fourier transform to fuse the methods of DFF and SFS is proposed in this paper. The proposed method can achieve depth continuity in tool wear images. After performing high-pass filtering and low-pass filtering for the 3D reconstructed images of DFF and SFS, respectively, in frequency domain, the 3D wear area reconstruction image of grinding wheel can then be obtained by Fourier inversion. Experimental results showed that the 3D wear area reconstruction image obtained by the proposed method can achieve better accuracy for the tool wear volume than using DFF or SFS alone. And the efficiency of the proposed method is proved to be better than microscopes.
A novel grinding wheel wear monitoring system based on discrete wavelet decomposition and support vector machine is proposed. The grinding signals are collected by an acoustic emission (AE) sensor. A preprocessing method is presented to identify the grinding period signals from raw AE signals. Root mean square and variance of each decomposition level are designated as the feature vector using discrete wavelet decomposition. Various grinding experiments were performed on a surface grinder to validate the proposed classification system. The results indicate that the proposed monitoring system could achieve a classification accuracy of 99.39% with a cut depth of 10 μm, and 100% with a cut depth of 20 μm. Finally, several factors that may affect the classification results were discussed as well.
Sharp abrasion of the abrasive belt always causes surface quality to deteriorate in the robotic grinding of thin-walled blades, which has stymied the further advancement of robot machining technology. A novel belt wear prediction approach based on the acoustic emissions (AE) signal is presented in this study to monitor belt wear conditions to achieve the expected machining result. A time-varying model of belt wear height and tangential force is initially established by employing the force analysis for various belt wear states. Next, a relationship between AE signal power and belt wear height is established utilizing the energy principle. Finally, robotic machining experiments on Ti-6Al-4V alloy workpieces are performed to achieve accurate belt wear prediction of an average error of approximately 10%, and the influence analysis of belt wear on the surface quality is further investigated to extend effective service life of abrasive belt by about 20% and realize the desired blade’s machining quality of surface roughness Ra<0.4 μm and contour accuracy ≤ ±0.17mm.
Ultrasonic vibration-assisted grinding (UVG) is a promising precision machining method for difficult-to-cut materials. This study investigated the intermittent cutting behavior in UVG of Inconel718 nickel-based superalloy and its effect on the grinding temperature, grinding force and workpiece surface quality. A novel material removal probability model was established to analyze the impact depth and the contact length between the abrasive grain and the workpiece considering the interference of grinding trajectories in UVG. Results indicated that the intermittent cutting behavior in UVG caused a decrease in the contact length by 42%, resulting in a significant reduction of grinding temperature and grinding force by 40% and 41%, respectively. It was suggested that the increase in the cutting depth was better than the increase in the workpiece infeed speed for enabling the lager material removal rate in the tangential UVG due to the lower grinding force ratio and the less change of specific grinding energy. A specific surface texture was generated on the ground surface due to the phenomenon of frequent contact and separation, enabling the reduction of workpiece surface roughness by maximum 20%.
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.
Gear profile grinding is more complex than surface grinding owing to the specific shape of the grinding wheel and geometric characteristics of the gear tooth, resulting in difficulties in modeling grinding components. Consequently, conventional grinding models tend to fail. To address this issue, a novel semi-analytical modeling method of the material removal mechanism and surface generation for gear profile grinding is proposed. Firstly, the grinding wheel model is represented by a multi-information fusion matrix that contains kinds of information about grinding grits. Compared with the traditional models that require several matrices to represent respective information, the proposed wheel model incorporates multi-dimensional information without extending the original matrix size and data length, which is more efficient. Then, a numerical model for the grinding process is developed by analyzing grit kinematics and grit-workpiece contact to reveal the material removal mechanism, and it is found that the cross-section shape of 3D cutting chips changes in the direction of the contact line. Furthermore, a surface topography model is established considering the specific pile-up effect. In this process, based on the length conservation of the tooth profile curve after straightening, the body Boolean operation is replaced by matrix calculation which reduces the dimension of data from 3D to 2D. Thus, computing time is reduced greatly, and this method does not require knowledge of the generation mechanism for tooth profile. Finally, experiments are conducted and the effects of grinding parameters on surface topography are investigated. The experimental results match well with model predictions, proving the model is effective. This work provides further understanding of the material removal mechanism for gear profile grinding. Meanwhile, it can also be extended and applied to other grinding processes for workpieces with complex geometry.
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.
Blade root with a special fir-tree shape is a critical connection part in an aero-engine. This part has high requirements on the surface finish and geometrical accuracy. At present, the tangential ultrasonic vibration-assisted profile grinding (UVAPG) is an effective method for machining the blade roots. During UVAPG, the wheel wear will degrade the surface finish and geometrical accuracy. However, the characteristics of the wheel wear in UVAPG are not fully revealed. This study proposes an analytical wear model by considering the intermittent grinding behavior of a grinding edge, the number of active grinding edges, and the effects of wheel profile on the tool wear. The model is experimentally demonstrated to be applicable to predicting the wear volume in UVAPG with a maximum error of 4.2%. Based on the model, the tangential ultrasonic vibration increases the average unchanged chip thickness and the number of active grinding edges. UVAPG causes an approximately 22% increase in wear volume at the steady wear stage compared with the conventional grinding. The wear speeds vary at different regions of a profile wheel. The convex regions represent higher wear speeds than the concave regions on the wheel.
Modelling of grinding force and material removal rate (MRR) has been widely investigated for wheel grinding which often has a preset cutting depth, but is rather lacking for sand belt grinding. For robotic belt grinding where the normal force often remains constant, the cutting depth of individual grain varies as the abrasive grains wear with grinding time increasing. It is, therefore, a challenge to accurately predict the tangential force and resulted MRR, and subsequently control the finish profile. This paper develops grinding force model and material removal rate model based on single grain force for robotic belt grinding. It divides the whole grinding process into three stages: initial stage, steady stage and accelerated stage, based on the degree of grain wear, analyses the grinding force of rubbing, ploughing and cutting effects and MRR at each stage. By studying the distribution of grains and penetration depth of each grain, the grinding force and MRR are calculated. Experimental work on stainless steel 304 shows that the maximum errors of the tangential force prediction is 10.9% and that of MRR is 14.4%. The proposed models not only reveal the grinding mechanism but also predict the grinding force and MRR.
The machining-induced residual stress has a pivotal impact on the corrosion resistance, fatigue, and functionality of manufactured components. Tool wear process induces the tool geometrical alterations, thereby affects the thermo-mechanical loads and residual stress distribution in the manufactured components. This work proposed a multi-physics model with the considerations of wear induced tool geometrical alterations to predict the residual stresses distribution for orthogonal turning Ti-6Al-4 V. These tool geometrical alterations included the flank wear, cutting edge radius, and rake angle. The proposed model framework involved five steps: (i) cutting force, (ii) temperature distribution, (iii) mechanical stress, (iv) thermal stress, and (v) calculation residual stresses by relaxation procedure. The experimental results verified that this model could effectively evaluate the residual stress distribution characteristics under tool wear conditions. Based on the prediction results, the response relationship analysis between the residual stresses and tool wear conditions was developed. This work could provide a novel method to control the residual stress distribution through monitoring of the tool wear morphology.
Although many advanced signal processing techniques and novel machine learning algorithms have been applied to the monitoring of grinding processes in the literature, most of these techniques and algorithms are only effective under specific conditions and are unusable under other grinding conditions, such as varying wheel types or workpiece materials. This paper proposes a robust grinding wheel wear monitoring system to eliminate these restrictions. Physical information generated during the grinding process is collected by a power sensor, accelerometers and acoustic emission sensors. After the signals are preprocessed, features are extracted via different signal processing techniques, and a novel normalization scheme is applied to make these features independent of the wheel type, workpiece material and grinding parameters. The features that are most related to wheel wear are selected according to the statistical criterion. An interval type-2 fuzzy basis function network (FBFN) is adopted to develop a wheel wear monitoring model, which is capable of predicting wheel wear under various grinding conditions and generating upper and lower prediction bounds according to the fluctuation of features. Based on the model, a robust wheel wear monitoring scheme could be established.
The non-uniform grinding conditions due to the tool and workpiece geometries lead to the complexities of the chip geometry and topography characteristic in gear profile grinding. As a result, the previous surface prediction models that have been extensively studied for surface grinding are not able to reveal the surface generation mechanism of gear profile grinding. In this work, a comprehensive model is presented to calculate the surface topography and the chip geometry considering the non-uniformity of geometric contact and grain-workpiece interaction along the tooth profile. In this process, the geometric conditions are characterized by the radius of local tool circumference and the included angle between the plane of grain rotation and the surface normal. Experimental results show reasonable consistency with numerical simulations that validate the proposed model. Based on the model, the effect of geometric conditions on the texture pattern and roughness distribution is studied to give an in-depth analysis of the process characteristics by comparing the simulated results under actual and ideal conditions. Besides, the optimization method is proposed to obtain a more uniform tooth surface. This work provides new insight into the fundamental understanding of profile grinding of gears and can be utilized to guide the machining process for higher tooth surface integrity.
In this study, the machined surface quality of powder metallurgy nickel-based superalloy FGH96 (similar to Rene88DT) and the grinding characteristics of brown alumina (BA) and microcrystalline alumina (MA) abrasive wheels were comparatively analyzed during creep feed grinding. The influences of the grinding parameters (abrasive wheel speed, workpiece infeed speed, and depth of cut) on the grinding force, grinding temperature, surface roughness, surface morphology, tool wear, and grinding ratio were analyzed comprehensively. The experimental results showed that there was no significant difference in terms of the machined surface quality and grinding characteristics of FGH96 during grinding with the two types of abrasive wheels. This was mainly because the grinding advantages of the MA wheel were weakened for the difficult-to-cut FGH96 material. Moreover, both the BA and MA abrasive wheels exhibited severe tool wear in the form of wheel clogging and workpiece material adhesion. Finally, an analytical model for prediction of the grinding ratio was established by combining the tool wear volume, grinding force, and grinding length. The acceptable errors between the predicted and experimental grinding ratios (ranging from 0.6 to 1.8) were 7.56% and 6.31% for the BA and MA abrasive wheels, respectively. This model can be used to evaluate quantitatively the grinding performance of an alumina abrasive wheel, and is therefore helpful for optimizing the grinding parameters in the creep feed grinding process.
The investigation of grit-workpiece interaction in micro scale is an important issue in abrasive processes as it sheds light on the chip formation process. As a practical condition, grinding is accompanied by vibration because of the wheel run out and wheel-workpiece interaction. The initial condition of the wheel also changes because of wear mechanisms. Considering these situations, the number of engaged grits called active grits has been determined in this study by developing the kinematic equations of the process. Grit properties especially grit height distribution, grinding conditions and wheel vibration are influential parameters determining the active number of grits. Its effect on the undeformed chip thickness frequency, surface roughness and grinding force has been discussed in detail and experimentally investigated by conducting grinding tests at various conditions using different CBN electroplated wheels including fresh, trued and worn wheels. In addition, a normality analysis has been performed for different wheels used in this study indicating a non-normal height distribution for the trued and worn wheel and a normal distribution for the fresh wheels. The active number of grits has been validated by comparing the surface roughness obtained from the model and the experiments in parallel and perpendicular to the cutting direction and a good agreement has been found between the results and the model.
In the present work, the wear of monolayer electroplated cubic boron nitride wheel during grinding of Inconel 718 super alloy is investigated, and the influence of wear process on wheel topography is reported. The radial wear of the wheel was characterized by an early transient state followed by a steady state wear regime and finally a steel rise in wear rate. The initial transient region progressed up to radial wear of 21 μm. During the steady state region, the radial wear was in the range of 21 μm to 25.5 μm. Beyond the steady state region, there was a sudden increase of 8.5 μm radial wear depicting the progress of wear towards the failure region. The normal and tangential forces remained approximately constant up to about 21 μm wheel wear, and subsequently progressively increased to a maximum of 34 μm wear. The specific energy began approximately at about u = 240 J/mm ³ and started progressing beyond radial wear of 21 μm and increased up to 540 J/mm ³ at the wear of 34 μm. There was high initial roughness value with the fresh wheel with a progressive decrease with continued grinding due to the increase in active grains.
Fast on-machine measurement of grinding wheel profiles is imperative for monitoring grinding wheel wear and wheel dressing accuracy. However, directly measurement of wheel profiles is considerably challenging due to harsh machining conditions. In this study, a novel method for fast vision based on-machine characterization of a grinding wheel profile was investigated based on a self-developed profile grinding machine. A new vision system was developed for the on-machine measurement of grinding wheel profiles and a calibration method for the vision system was introduced. A methodology for precisely measuring wheel profiles and for quantifying wheel wear was developed. The reliability and measurement accuracy were testified. Finally, segmented representation and compensation method were developed for wheel dressing. The experimental results revealed that the proposed method can achieve fast on-machine characterization of profile errors of the grinding wheel, and the wheel dressing accuracy was effectively improved based on the in-situ monitoring of the wheel dressing error.
A targeted adjustment of the dressing results and the methodological influence of the dressing process on the non-stationary wear of a grinding wheel after dressing increases the productivity and the reproducibility of grinding processes. Despite the great economic importance of grinding processes with vitrified corundum grinding wheels and the great relevance of the dressing process for the application behavior of these grinding wheels, quantitative models are missing for the purposeful design of the dressing process. In previous studies, a dressing model was successfully developed which predicts the dressing force in the dressing process as well as the workpiece roughness and the grinding wheel wear behavior in a grinding process for a specific grinding wheel and form roller specification. However, a transferability of this model to other grinding wheel and form roller specifications is not possible because the influence of the grain size and the hardness of the grinding wheel as well as the dressing tool topography on the grinding wheel wear and thus on parameters of the dressing model are not known. The objective of this work was to extend the model to additional grinding wheel and form roller specifications to ensure a broad applicability of the model. © 2018 German Academic Society for Production Engineering (WGP)
The article presents the results of calculating the blunting area of abrasive grains of grinding wheels, determined in accordance with the previously developed model. The mathematic model of the size of the blunting area of an abrasive grain considers the main mechanisms of its wear-mechanical and physicochemical. These mechanisms are taken into account in the model. For the first time, the kinetic theory of strength was used for determining the mechanical wear of abrasive grain. The mass transfer theory was used to study the physicochemical wear: coefficients of chemical affinity with the abrasive material are experimentally defined for the assortment of workpiece materials. The developed mathematic model is a multiple-factor one and this will allow to predict the size of wear of the abrasive wheel for different technological conditions. Also, the article presents the experimental method for determining the blunting area of abrasive grains of grinding wheels, which allows making a direct measurement of wear parameters of grinding wheels. The main parameter of grinding wheel wear is the length of the blunting area of the grain, which was measured out in the direction of the cutting speed vector. The grinding wheels of different graininess were studied-F60 and F46. The grinding wheel working surface was studied by numerical photos and microscope. The results of these experiments have confirmed the adequacy of the design model.
In order to explore the effect of grain wear on material removal behavior during grinding nickel-based superalloy Inconel 718, the grinding experiment with a single diamond grain was carried out. The variations of grain wear, grinding force and force ratio, and pile-up ratio were investigated under the conditions of undeformed chip thickness (UCT) ranging from 0.2 to 1 μm. The results show that a critical UCT value, such as 0.3 μm, could be determined according to the pile-up ratio and could also be used to quantify the material removal process. The wear behavior of a diamond grain shows four types, such as crescent depression on the rake face, abrasion on the flank face, grain micro-fracture, and grain macro-fracture. Furthermore, these classifications were determined by the dwell time of rubbing, ploughing and cutting at different UCT values applied. The grinding force ratio increased with increasing of the negative rake angle of a diamond grain. In the rubbing and ploughing stages, the material removal efficiency is proportional to the wear width on the rake face. However, in the cutting stage, the material removal efficiency is diminished in the absence process of crescent depression.
An investigation of attritious and fracture wear of grinding wheels in precision grinding is described in a two paper sequence. Attritious wear, the subject of this first paper, refers to the dulling of the abrasive grain due to rubbing against the workpiece surface. The amount of dulling, measured by the area of the wear flats on the surface of the wheel, is found to be directly related to the grinding forces. In general, both the vertical and horizontal grinding force components increase linearly with the wear flat area. This is explained by considering the grinding force as the sum of a cutting force due to chip formation and a sliding force due to rubbing between the wear flats and workpiece. Related studies of wheel dressing, surface finish, and workpiece burn are also presented.
The generation of the grinding wheel topography is described in many different models and approaches. These models do not consider the influence of the dressing process on the wear of the grinding wheel. In particular the prediction of this wear dependent on the dressing process parameters is not possible with the currently available models. This article describes a novel model for the initial wear of vitrified bonded grinding wheels on the basis of Linke’s dressing model. Therefore, the load of the grinding wheel in dressing process is depicted using the mean dressing chip cross section, which is then used to model the wear of the grinding wheel. An analytical-empirical model for the initial radial grinding wheel wear in dependence of the load in dressing process is presented. Furthermore, the influence of the load in the dressing process on the wear mechanisms of the grinding wheel, in particular on the relative frequency of the fracture phenomenon grain break-out, is shown. The new model allows the prediction of the wear of the grinding wheel as a function of the geometric-kinematic engagement in dressing processes using the mean dressing chip cross section.
This paper presents an elastic-plastic asperity microcontact model for contact between two nominally flat surfaces. the transition from elastic deformation to fully plastic flow of the contacting asperity is modeled based on contact-mechanics theories in conjunction with the continuity and smoothness of variables across different modes of deformation. The relations of the mean contact pressure and contact area of the asperity to its contact interference in the elastoplastic regime of deformation are respectively modeled by logarithmic and fourth-order polynomial functions. These asperity-scale equations are then used to develop the elastic-plastic contact model between two rough surfaces, allowing the man surface separation and the real area of contact to be calculated as functions of the contact load and surface plasticity index. Results are presented for a wide range of contact load and plasticity index, showing the importance of accurately modeling the deformation in the elastoplastic transitional regime of the asperity contacts. The results are also compared with those calculated by the GW and CEB models, showing that the present model is more complete in describing the contact of rough surfaces.
Grinding is a time-varying process affected by factors such as wheel construction, dressing parameters, operating parameters, workpiece material, and cooling. As the grinding wheel wears, flat areas form on the wheel surface. Wear flat measurement provides information on the condition of the wheel and is, therefore, an essential tool to study the grinding process. Measuring wear flat area can be a tedious, time-consuming, or expensive process. In this paper, a new system has been developed to measure wear flat area. This system is mounted on the grinding machine and automates wear flat measurement by using computer control to automatically position the wheel and capture digital images of the wheel between grinding cycles. Image processing software is used to automatically analyze the digital images and measure wear flat area in the images. The proposed measurement system was validated using a scanning electron microscope. Experiments were performed on a Brown & Sharpe Micromaster 824 surface grinder to examine the relationship between wear flat area and normal force. The results agree with the literature.
Micro-grinding with small-scale grinding wheels is a micro-machining process in precision manufacturing of miniature part features such as those in micro sensors and micro actuators. Modeling of micro-grinding is necessary to understand the effects of process conditions, micro-grinding wheel properties, and material microstructure on the integrity of the parts produced, thereby allowing for process planning, optimization, and control. In this paper, a predictive model for the micro-grinding process was developed by combined consideration of mechanical and thermal effects within a single grit interaction model at the microscale level of material removal while the size effect of micro-machining was incorporated. To assess the thermal effects, a heat transfer model based on the moving heat source analysis is integrated into the developed model. This model quantitatively predicts micro-grinding forces based on micro-grinding wheel topography and material properties including crystallographic effects. Experimental testing in a micro-grinding configuration has been pursued to validate the predictive model by comparing measurements to analytical calculations in the context of orthogonal micro-grinding forces. The analytical model is seen to capture the main trend of the experimental results, while smaller deviations were found over larger depths of cut range.
An upper bound method was used to study surface ploughing by a rigid pyramidal indenter. The normal and tangential forces, the geometrical parameters of the track, the strain and the strain rate of the ploughed material are calculated. The model is compared with experience and is applied to the calculation of scratch hardness.
A slip-line field analysis is given for the deformation of a soft asperity by a hard one and equations are derived for the corresponding coefficients of friction and wear rates. Three main models are proposed. For smooth surfaces the first model gives low coefficients of friction and shows how plastic deformation of the asperity can occur without removal of material. The second model shows how wear and high coefficients of friction can occur for such surfaces. For rougher surfaces a cutting model applies with a chip (wear particle) being produced. In this way an explanation is offered of why “lubrication” is observed to inhibit wear for smooth surfaces and to encourage it for rougher surfaces. A possible explanation is also given of why the actual wear for engineering surfaces under normal working conditions is many orders of magnitude less than that calculated by assuming that all of the plastically deformed material is removed.