Diamond Face Grinding Research Articles

Fabrication of PCD face grinding wheel with positive rake angle by femtosecond laser and its performance evaluation in cemented carbide grinding

Super-hard abrasive grinding is considered to be the main approach to realize precision and ultra-precision machining of difficult-to-machine materials such as cemented carbide, engineering ceramics, titanium alloys and superalloy materials encountered. However, heavy grinding force, high grinding temperature and poor surface integrity are prone to be encountered in conventional negative rake angle grinding of difficult-to-machine materials. In response to these problems, a novel concept of positive rake angle grinding is first proposed and an abrasive grain regularly arranged binder-less polycrystalline diamond face grinding wheel with positive rake angle has been designed and fabricated by femtosecond laser ablation. To evaluate the grinding performance of the face grinding wheel with positive rake angle, grinding experiments of YG8 cemented carbide are conducted and compared with the traditional electroplated diamond grinding tool with equivalent abrasive grain dimension and distribution. The results show that compared with the conventional negative rake angle grinding, the normal and tangential forces in positive rake angle face grinding are reduced by 30.3 % ∼ 36.4 % and 21.1 % ∼ 29.3 %, respectively, and the ratio of normal to tangential force is reduced by 12.6 % ∼ 20.3 %. The surface roughness and average depth of subsurface metamorphic layer are also significantly smaller. The laser fabricated polycrystalline diamond face grinding wheel also has better wear resistance in the grinding of cemented carbide. Therefore, it can be concluded that better ground surface quality is obtained by the novel grinding wheel with positive rake angle. The innovative grinding method can fill the research gap on the grinding mechanism of positive rake angle grinding and further enrich the grinding theory of difficult-to-machine materials.

Evaluating the performance of electrical discharge face grinding on super alloy monel 400

Conventional machining of super alloy Monel 400 is difficult due to its high strength, low heat conductivity, and gummy nature. In this work, the performance of the electrical discharge diamond face grinding (EDDFG) while processing supper alloy Monel 400 is assessed using the super abrasive diamond-coated grinding wheels of three different grit numbers. Preliminary experiments highlighted the superior performance of the EDDFG as compared to that of electrical discharge machining (EDM) and electrical discharge face grinding (EDFG); in terms of material removal rate (MRR), average surface roughness (Ra), and microscopic images of surfaces using scanning electron microscope (SEM). This work aimed to assess the influence of the diamond grit number (DG n ) of the grinding wheel along with other process parameters, viz. grinding wheel speed (GWS), peak current (I p ), and pulse-on-time (T on ) on Ra and MRR. The analysis of variance (ANOVA) confirmed that DG n and GWS significantly affect the response variables. Finally, the multi-objective genetic algorithm (GA) optimization approach was used to determine the optimal parametric settings of the EDDFG process.

Femtosecond pulsed laser fabrication of a novel SCD grinding tool with positive rake angle

Ablating a single-crystal diamond (SCD) plate with a femtosecond laser to fabricate a novel abrasive regularly arranged binder-less face grinding tool with acute inclination angle at the edge of the micro grains was proposed in this paper. A Ti:Sapphire femtosecond laser with a wavelength of 1030 nm and a pulse width of 250 fs was used for the ablation experiments. The effects of processing parameters, especially the ablation track pitch on the ablation surface quality of the diamond, were investigated in advance. Based on the optimal processing parameters, an array of square frustum microabrasive with grain inclination angle of approximately 100° was fabricated on the surface of SCD according to the predesigned laser scanning path. The microabrasive array was subsequently ablated to reduce the inclination angle at the edge of the micrograins to approximately 80°, and a novel abrasive regularly arranged binder-less diamond face grinding tool with positive rake angle which was expected to decrease the grinding force and improve the surface quality of hard and brittle materials had been fabricated for the first time.

Application of Soft Computing Techniques for Modeling and Optimization of Slotted-Electrical Discharge Diamond Face Grinding Process

An innovative hybrid machining called as slotted-electrical discharge diamond face grinding (S-EDDFG) has been developed by comprising the electrical discharge face grinding and diamond face grinding. Due to complexity in S-EDDFG process, the selection of suitable combinations of input parameters is a challenging task for manufacturers. In recent, soft computing techniques are gaining more attention of researchers for modeling and optimization for the process parameters of complex manufacturing processes. In the present paper, two soft computing techniques known as artificial neural network (ANN) and non-dominated sorting genetic algorithm-II (NSGA-II) have been applied for modeling and optimization of process parameters (pulse current, pulse on-time, pulse off-time, wheel RPM and grit number) during machining of Aluminium–Silicon Carbide–Graphite (Al/SiC/Gr) composite. The results shown that two hidden layer ANN architecture was more appropriate to predict the material removal rate and average surface roughness during S-EDDFG of Al/SiC/Gr workpiece. The optimal results obtained by combined approach of ANN–NSGA-II are experimentally validated and found that optimal data are under acceptable limits.

Application of non-dominated sorting genetic algorithm for multi-objective optimization of electrical discharge diamond face grinding process

Hybrid machining processes (HMPs), having potential for machining of difficult to machine materials but the complexity and high manufacturing cost, always need to optimize the process parameters. Our objective was to optimize the process parameters of electrical discharge diamond face grinding (EDDFG), considering the simultaneous effect of wheel speed, pulse current, pulse on-time and duty factor on material removal rate (MRR) and average surface roughness (Ra). The experiments were performed on a high speed steel (HSS) workpiece at a self developed face grinding setup on an EDM machine. All the experimental results were used to develop the mathematical model using response surface methodology (RSM). The developed model was used to generate the initial population for a genetic algorithm (GA) during optimization, non-dominated sorting genetic algorithm (NSGA-II) was used to optimize the process parameters of EDDFG process. Finally, optimal solutions obtained from pareto front are presented and compared with experimental data.

Application of response surface methodology and genetic algorithm for optimisation of electro-discharge diamond face grinding of tungsten carbide-cobalt composite

The objective of this paper is to optimise the electrical discharge diamond face grinding (EDDFG) process. The experiments were performed on a self developed face grinding setup, which is attached with vertical column of EDM machine. The experiments were conducted on tungsten carbide-cobalt (WC-Co) composite workpiece considering the simultaneous effect of wheel speed, pulse current, pulse on-time and duty factor on material removal rate (MRR) and average surface roughness (Ra). The experiments were conducted according to the central composite rotatable design (CCRD) and developed a mathematical model to predict the relation between input-output parameters using response surface methodology (RSM). The developed RSM model is coupled with non-dominated sorting genetic algorithm (NSGA-II) to optimise the process parameters. Finally optimal solution obtained from Pareto front is presented and compared with experimental results.

Modelling and simulation of spark assisted diamond face grinding of tungsten carbide-cobalt composite using ANN

The aim of this study is to develop an artificial neural network (ANN) model for spark assisted diamond face grinding (SADFG) of cobalt bonded tungsten carbide (WC-Co) composite to predict the material removal rate (MRR) and average surface roughness (Ra). The experiments were conducted on a self-developed face grinding setup, which is attached with EDM machine. A bronze metal bonded diamond wheel is used for experimentations. All the experiments were performed according to the central rotatable design. The current, pulse on-time, duty factor and wheel speed were taken as input process parameters and responses are measured in terms of MRR and Ra. Central rotatable design is used for experimentation. The obtained experimental data set was used to train the ANN model. The ANN architecture with back propagation algorithm has been used for modelling of process parameters of SADFG process. It has been found that the developed ANN model is capable to predict the MRR and Ra with absolute average percentage error of 10.40% and 6.81%, respectively. It has been also found that wheel speed at 1,300 RPM is suitable for achieving of the better surface finish while duty factor at 0.70 has been found more appropriate for higher MRR.

Preliminary Study on Slotted-Electrical Discharge Diamond Face Grinding of Metal Matrix Composite

Metal matrix composites (MMCs) are highly required in field of advanced engineering. Due to the abrasive nature of the reinforcements into matrix, machining of the MMCs is yet a challenge for manufacturing industries. In this paper, a new hybrid machining process named as slotted-electrical discharge diamond face grinding (S-EDDFG) has been proposed for machining of the end surface of the electrically conductive hard and brittle materials. The performance of the developed process has been tested on EDM machine with application of the compound grinding wheel. The role of discharge voltage, current, pulse on-time, pulse off-time and wheel RPM has been investigated on MRR and Ra during machining of the Al/SiC composite. The experimental results show that MRR increases and Ra value decreases with increase of the wheel RPM due to enhances in the flushing efficiency of the dielectric.

Experimental modeling and optimization of electric discharge diamond face grinding of metal matrix composite

Machining of metal matrix composites (MMCs) has been a big challenge for manufacturing industries due to its superior mechanical properties. Unconventional machining methods have become an alternative to give desired shapes with intricate profiles and stringent design requirements. The present research investigates the grinding performance of copper–iron–graphite MMC using electric discharge diamond face grinding (EDDFG), which is electric discharge machining-based hybrid machining process. Experiments have been performed on a self-developed experimental setup of EDDFG with scientifically designed experiments. Effects of process input parameters on two important performances, material removal rate (MRR) and surface roughness (SR), have been analyzed. Genetic algorithm-based optimization of MRR and SR models show considerable improvements in both characteristics, as confirmed by verification experiments. Results reveal that peak current is a common significant factor for both MRR and SR.

Multi-objective optimization of process parameters in Electro-Discharge Diamond Face Grinding based on ANN-NSGA-II hybrid technique

The effective study of hybrid machining processes (HMPs), in terms of modeling and optimization has always been a challenge to the researchers. The combined approach of Artificial Neural Network (ANN) and Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) has attracted attention of researchers for modeling and optimization of the complex machining processes. In this paper, a hybrid machining process of Electrical Discharge Face Grinding (EDFG) and Diamond Face Grinding (DFG) named as Electrical Discharge Diamond face Grinding (EDDFG) have been studied using a hybrid methodology of ANN-NSGA-II. In this study, ANN has been used for modeling while NSGA-II is used to optimize the control parameters of the EDDFG process. For observations of input-output relations, the experiments were conducted on a self developed face grinding setup, which is attached with the ram of EDM machine. During experimentation, the wheel speed, pulse current, pulse on-time and duty factor are taken as input parameters while output parameters are material removal rate (MRR) and average surface roughness (R a). The results have shown that the developed ANN model is capable to predict the output responses within the acceptable limit for a given set of input parameters. It has also been found that hybrid approach of ANN-NSGAII gives a set of optimal solutions for getting appropriate value of outputs with multiple objectives.

EXPERIMENTAL STUDY ON THE CUTTING FORCE DIFFERENCE BETWEEN ROTARY ULTRASONIC MACHINING AND CONVENTIONAL DIAMOND GRINDING OF K9 GLASS

A comprehensive experimental study on the cutting force differences between rotary ultrasonic machining (RUM) and conventional diamond side grinding, drilling and face grinding is conducted on K9 glass. By analyzing the experimental results and the kinematics of diamond grits, it can be seen that the reasons for the cutting force differences between with and without ultrasonic vibration are not same for the three machining processes. The cutting force difference of conventional diamond side grinding and RUM can be explained by analyzing the trajectory of diamond grits, while the cutting force difference of conventional diamond drilling and RUM is mostly dependent upon the difficulty level of removing chips. As a transitive form, the cutting force difference between conventional diamond face grinding and RUM is affected by both the diamond grits' trajectory and the difficulty level for removing chips. In addition, it is also noticed that different cutting tools have different reduction percentages of cutting forces in RUM compared with conventional diamond grinding. The findings are helpful for researchers to have a further understanding in RUM.

Modelling and optimisation of electro-discharge diamond face grinding of cemented carbide-cobalt composite

Electro-discharge diamond grinding is a hybrid process of electrodischarge machining (EDM) and diamond grinding which is obtained by replacing the tool electrode of conventional EDM by a metal bonded diamond wheel. When face of the wheel is used for machining then the process is termed as electro-discharge diamond face grinding (EDDFG). In this study, modelling and optimisation of EDDFG of cemented carbide–cobalt composite has been done using a hybrid methodology comprising of Taguchi methodology (TM) and response surface methodology (RSM) with material removal rate (MRR), wheel wear rate (WWR) and average surface roughness (ASR) as the objective functions. The approach first uses the TM for finding the optimum level of input machining parameters such as wheel speed, current, pulse on-time and duty factor. The optimum input parameter values are further used as the central values in the RSM to develop second-order response model for MRR, WWR and ASR after conducting experiments using central composite rotatable design matrix. The simultaneous optimisation of weighted response has been obtained using MINITAB software. The results show considerable improvement in three quality characteristics when the hybrid approach is used, as compared with the results of only a TM approach.

Study of the Parameters in Electro-Discharge Diamond Face Grinding through Response Surface Methodology Approach

The present study investigates the relationship of process parameters in electro-discharge diamond face grinding (EDDFG) of tungsten carbide and cobalt composite (WC-Co). The central composite rotatable design had been utilized to plan the experiments and response surface methodology (RSM) was employed for developing experimental models. Analysis on machining characteristics of EDDFG was made based on the developed models. In this study, wheel RPM, current, pulse on-time, and duty factor are considered as input process parameters. The process performances such as material removal rate (MRR) and average surface roughness (Ra) were evaluated. Analysis of variance test had also been carried out to check the adequacy of the developed regression models. The observed optimal process parameter settings are wheel RPM of 1500, current of 6.9029 A, pulse on-time of 137. 8208 µs, and duty factor of 0.79 for achieving maximum MRR and minimum Ra; finally, the results were experimentally verified. A good agreement is observed between the results based on the RSM model and the actual experimental observations. The error between experimental and predicted values at the optimal combination of parameter settings for MRR and Ra lie within 6.18% and 12.33%, respectively.

Experimental study and parameter optimisation of electro-discharge diamond face grinding

Electro-discharge diamond face grinding (EDDFG), a hybrid machining process comprising diamond face grinding (DFG) and electro-discharge machining (EDM), has been developed for machining of electrically conductive difficult-to-machine very hard materials. The process employs simultaneous synergetic interactive effect of abrasion action and electro-discharge action. A face grinding setup for electro-discharge diamond grinding (EDDG) process is developed. The effect of wheel RPM, current, pulse on-time and duty factor on material removal rate (MRR), wheel wear rate (WWR) and average surface roughness (Ra), while machining high speed steel (HSS) workpiece, are investigated. It is observed that the MRR increase with increasing wheel RPM, current and pulse on-time, while it decreases with an increase in duty factor. The most significant factor affecting the EDDFG robustness has been identified as pulse on-time and duty factor.

Diamond face grinding of WC-Co composite with spark assistance: Experimental study and parameter optimization

Electro-discharge machining (EDM) characteristics of tungsten carbide-cobalt composite are accompanied by a number of problems such as the presence of resolidified layer, large tool wear rate and thermal cracks. Use of combination of conventional grinding and EDM (a new hybrid feature) has potential to overcome these problems. This article presents the face grinding of tungsten carbide-cobalt composite (WC-Co) with electrical spark discharge incorporated within face of wheel and flat surface of cylindrical workpiece. A face grinding setup for electro- discharge diamond grinding (EDDG) process is developed. The effect of input parameters such as wheel speed, current, pulse on-time and duty factor on output parameters such as material removal rate (MRR), wheel wear rate (WWR) and average surface roughness (ASR), are investigated. The present study shows that MRR increases with increase in current and wheel speed while it decreases with increase in pulse on-time for higher pulse on-time (above 100 µs). The most significant factor has been found as wheel speed affecting the robustness of electro- discharge diamond face grinding (EDDFG) process.

Multiresponse Optimization of Electro-Discharge Diamond Face Grinding Process Using Robust Design of Experiments

This article presents an investigation of the machining parameters design for electro-discharge diamond face grinding (EDDFG). The setup for EDDFG was developed, and experiments were conducted on tungsten carbide cobalt (WC-10wt%Co) composite workpiece. The input machining parameters used in the present study were wheel speed, current, pulse on time, and duty factor. The settings of machining parameters were determined by using Taguchi's robust design approach. The machining parameters were optimized with multiresponse characteristics of material removal rate (MRR), wheel wear rate (WWR), and average surface roughness (ASR). Multiresponse signal-to-noise (MSNR) ratio was applied to measure the performance characteristics deviating from the actual value. Analysis of variance (ANOVA) was employed to identify the level of importance of the machining parameters on the multiple performance characteristics considered. Finally, experimental confirmation was carried out to identify the effectiveness of this proposed method.

English

Electro-Discharge Diamond Grinding (EDDG), a hybrid machining process comprising diamond grinding and electrodischarge grinding, has been developed for machining of electrically conductive difficult-to-machine very hard materials. The process employs simultaneous synergetic interactive effect of abrasion action and electro-discharge action. A self-designed face grinding setup is attached on ELEKTRA PULS EDM machine to use it as Electro-Discharge Diamond Face Grinding (EDDFG). This paper investigates optimization design of an EDDFG process performed on high speed steel (HSS).The major performance characteristics selected to evaluate the processes are material removal rate (MRR) and wheel wear rate (WWR), and the corresponding EDDFG parameters are wheel RPM, current, pulse ontime and duty factor. In this study, since the process is with multiple-performance characteristics, therefore, the grey relational analysis that uses grey relational grade as performance index is specially adopted to determine the optimal combination of EDDFG parameters. Moreover, the principal component analysis is applied to evaluate the weighting values corresponding to various performance characteristics so that their relative importance can be properly and objectively described. The results of confirmation experiments reveal that grey relational analysis coupled with principal component analysis can effectively be used to obtain the optimal combination of EDDFG parameters. Hence, this confirms that the proposed approach in this study can be a useful tool to improve the machining performance of EDDFG process.