Optimization Of Grinding Process Research Articles

English

This paper presents the research on optimization of plunge centerless grinding process when grind 20X - carbon infiltration steel (ГOCT standard - Russia) to achieve minimum of roundness error value. The input parameters are center height angle of the workpiece ( 

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.

Experimental study on single grit grinding of Inconel 718

This article presents an important investigation of material removal mechanism in grinding utilizing single grit scratch tests. The investigation helps people to understand the abrasive cutting behaviour when the abrasive cutting edge shape alters during single grit grinding. The results provide fundamental knowledge of the grinding material removal process, which helps to improve grinding performance and quality. Cubic boron nitride grits of 40/50 mesh size were used to perform scratch tests on the alloy Inconel 718. The concepts of material pile-up ratio and actual material removal area were introduced to measure the material removal efficiency during grinding. It is found that pile-up ratio decreases and actual material removal area increases when the depth of cut increases, albeit the material removal mechanism is highly dependent on the abrasive grit cutting edge shape. The material removal mechanism along the scratch length shows different behaviours at the entrance and exit sides of the scratching passes. When a grit was moving along its scratch path, it pushed material forward resulting in high material accumulation at the exit side of the scratches. Consequently, cutting is more prominent at the entrance side of the scratch, whereas ploughing or pile-up is extremely high at the exit side of the scratches. The research finding provides crucial information for grinding process optimization.

The Efficiency Optimization of the Cylindrical Grinding Based on Acoustic Emission Signals

To improve the efficiency of cylindrical plunge grinding, an optimization of grinding process is developed using the Acoustic emission (AE) signals. The relationship between the grinding feed and grinding depth is established based on the elastic deflection between wheel and workpiece. Acoustic emission (AE) signals are curve fitted through the gauss - Newton method to find the stable point of various grinding processes. Therefore, grinding time of each grinding stage is reduced to improve the grinding efficiency.

High-Speed CNC Grinding Process Optimization of Non-Circular Rotary Parts Based on Particle Swarm Optimization

As for the high-speed CNC grinding process for non-circular rotary parts with a large rate of lift change (such as camshaft of automotive engine), the acceleration of grinding wheel often has a direct impact on the contour grinding precision of the workpiece due to the large acceleration of movement between of the workpiece and the grinding wheel produced from CNC grinding interpolation. Researches show that solving this problem is an important way to improve the precision of high-speed CNC grinding for non-circular rotary parts. In this paper, a high-speed CNC grinding process control and optimization method is presented for non-circular rotary parts based on acceleration interpolation movement. The negative influence of the problem about high acceleration can be reduced effectively and the precision of high-speed CNC grinding for non-circular rotary parts can be improved greatly by the proposed method. Additionally, the simulation shows that the optimization of grinding parameters forgrinding process has a good performance.

Using of Barkhausen Noise Analysis and X-Ray Diffraction for Evaluation Of Surface Integrity

This paper presents results of study of grinded gears in order to describes surface integrity and verify the correlation between Barkhausen noise analysis and X-ray diffraction. Gears are used in the wind power plant and made of case-hardened steel 18CrNiMo7-6. Barkhausen noise analysis and X-ray diffraction was used for measurement. Barkhausen analysis method is one of the fast nondestructive methods used to assess the integrity of surface. For comparison of grinding processes were used different cutting speeds and numbers of material removal. X-ray diffraction method was used also for measurement of residual stresses. Due to correlation between measured values the optimization of grinding process of gears will be done.

Exploitation of the ELID Grinding Parameters Expert System

This paper introduces the ELID grinding parameters expert system. When we use this system, input machining parameters of the machined material and the requirement of processing precision, then it can speculate ELID grinding process parameters and electrolytic parameters for material. This system has a great practical value for the optimization of ELID grinding process, reducing processing cost and improving efficiency.

생체친화형 지르코니아 헤어커터 개발

본 연구에서는 스틸 헤어커터와 비교하여 절삭성, 내식성 내마모성이 우수하고 생체친화성이 뛰어난 소재인 지르코니아( $ZrO_2$ )를 원료로 하는 헤어커터 개발을 위하여 설계, 성형, 소결, 연마가공 최적화를 통해 두께 0.253mm의 초박형 지르코니아 커터를 제작하였다. 커터 시편을 전자주사현미경(SEM)으로 관찰한 결과, 성형성과 절삭면 가공이 양호한 것으로 나타났으며, 절단시험을 통해 상품성이 충분한 것으로 평가되었다. 본 지르코니아 커터 가공기술 연구결과를 이용하여 면도기, 여성용 제모기, 눈썹정리기 등 고부가가치 미용기구 개발에 활용 될 수 있다. 【This study aims to develop hair cutter with zirconia( $ZrO_2$ ) material whose machinability, corrosion resistance, wear resistance and bio-compatibility are excellent by comparing iron hair cutter. Ultra-thin zirconia cutter with the thickness of 0.253mm is manufactured through the optimization of design, molding, sintering, grinding and grinding process. With observation result of cutter specimen by use of scanning electron microscope(SEM), formability and process of cutting face are shown to be good. It is also evaluated with sufficient merchantability by cutting test results. The cutter process technology of thin zirconia can be utilized at the development of high value-added beauty appliances like razor, beauty-razor and eye brows-razor for woman by use of study result.】

The usage of ultra-fine cement as an admixture to increase the compressive strength of Portland cement mortars

The High-Strength Cement (HSC) technology is based on modification of Normal Use Portland Cement (NUPC) by complex admixtures, optimization of mixture design and grinding process. The present work shows the results of preparing and using Ultra-Fine Cement (UFC) as a binder material addition with no extra admixtures of different chemical composition to increase the compressive strength of mortars. The aim of this work is to demonstrate how a NUPC can be converted into a sort of HSC; it was observed that the 70–60wt%NUPC+30–40wt%UFC blend satisfies the goal of increasing mortar’s compressive strength.

Application of Response Surface Methodology and Enhanced Non- dominated Sorting Genetic Algorithm for Optimisation of Grinding Process

Optimisation of grinding process during grinding of Al6061-SiC composites is investigated in this study. Stir cast Al6061-SiC composites with varying volume percentage of SiC reinforcement were ground on a conventional grinding machine with diamond grit grinding wheel. Three grinding variables were studied for simultaneous optimization of material removal rate and surface roughness. Initially, the response surface models for grinding process parameters were developed using response surface methodology. Further, the developed models were optimized using enhanced elitist non-dominated sorting genetic algorithm (enhanced NSGA-II), a time saving algorithm in comparison to conventional NSGA-II. The suitable grinding conditions for multi-objective optimization of the grinding process were obtained from enhanced NSGA-II. Finally the confirmation tests were performed to validate the results obtained from response surface methodology and enhanced NSGA-II. It is observed that, experimental results and the results obtained from enhanced NSGA-II are in close conformance. Hence it is concluded that the developed algorithm can effectively be used for optimization of grinding process.

Precision Grinding Characteristics and Process Optimization on YG3 Cemented Carbide

Precision grinding experiment and process optimization of YG3 cemented carbide materials was conducted with various grinding parameters using resin-bonded diamond grinding wheel. Different rotation speed of workpiece, feed rate and grinding depth were chozen in precision cross grinding. Surface roughness was measured for studying the removal mode and grinding charateristics. A set of ideal grinding parameters of YG3 cemented carbide were obtained by changing the process parameters. The research result has guiding significance for reducing the wheel wear and processing costs, improving grinding quality and efficiency while precision grinding hard and brittle materials.

Predictive modeling of force and power based on a new analytical undeformed chip thickness model in ceramic grinding

The grinding force and power play an important role in ceramic grinding process as they not only have the direct influence on the wheel wear, grinding accuracy, grinding temperature and surface integrity but also have strong influence on local contact deflection and the nature of the contact deflection that has an important effect on the mechanism of material removal. In addition, they are also important to many aspects of ceramic grinding process optimization, monitoring, and control. So the prediction of grinding force and power in ceramic grinding is essential. But, the force and power is governed by many factors and its experimental determination is laborious and time consuming. So the establishment of a model for the reliable prediction of grinding force and power is still a key issue for ceramic grinding. In this study, a new grinding force and power model is developed, for the reliable prediction of grinding force and power in ceramic grinding, based on a new analytical undeformed chip thickness model. This new analytical undeformed chip thickness model is developed on the basis of stochastic nature of the grinding process, governed mainly by the random geometry and the random distribution of cutting edges. The model includes the real contact length that results from combined contact length, due to wheel-workpiece contact zone deflection and the local deflection due to the microscopic contact at the grain level and contact length due to geometry of depth of cut. The proposed model is used to predict the total grinding forces and power in surface grinding. The new model has been validated by conducting experiments on a horizontal surface grinding machine by grinding silicon carbide with diamond grinding wheel. Results indicate that the proposed model shows a good agreement with the experimental data obtained from different kinematic conditions. It also results in a significant reduction in the grinding forces, as compared with that obtained by the force model developed based on the existing undeformed chip thickness model, under the same operating conditions, in silicon carbide grinding.

GICS for grinding process optimization

GICS for grinding process optimization

Grinding Process Parameters Optimization and Surface Performance Analysis of Spiral Bevel Gear Based on the Orthogonal Test

Based on the experimental conditions of generation grinding of spiral bevel gear, the grinding orthogonal test design of L16(45) is made, and the results of grinding orthogonal test are obtained. On this basis, the range and variance analyses are carried out by using an integrated balance method, and the optimizing configuration of grinding process parameters of spiral bevel gear is obtained. The test regression models of three grinding evaluating indicators of spiral bevel gear, such as the grinding surface roughness Ra, metamorphic layer depth h and removal rate Zw, are set up by using a method of multivariate nonlinear least square. The maximal absolute value of relative error between computing values and testing ones is 18.41 percent by test validation, and this shows that the regression models are more effective. Through the test optimization of grinding surface performance, the value of grinding surface roughness Ra is more less 0.1μm than one of the normal grinding process, and other comprehensive performances are better. The pre-control measures of grinding process optimization of spiral bevel gear are also put forward. These provide a basis for the improvement of grinding surface quality of spiral bevel gear.

A Mathematical Model for Predicting the Internal Parameters of Ball Mill

The internal parameters of ball mill are very important in the grinding process and have significant impact on the grinding results. Accurate measurement of the internal parameters of ball mill is crucial and indispensable for optimization and control of grinding process. In this paper, a novel tri-sensor measurement method was used to measure the external signals (i.e., bearing pressure, sound intensity and power consumption) of ball mill. Mathematical models, which link the external signals of ball mill with its internal parameters, were established to predict the internal parameters of ball mill. Experimental results showed that the mathematical models could directly predict the internal parameters of ball mill once the external signals were obtained. The tri-sensor measurement method and the mathematical models proposed in this paper provided a new way and solid basis for optimization and control of ball milling.

Development of the process model for plunge grinding and optimization of grinding process

This article presents improved grinding roughness model established for cylindrical plunge grinding though analysing the existing roughness models. The proposed roughness model is to consider the uncertain effects of grinding, such as changes of grinding conditions and circumstance, as grinding procedure is progressed. In order to consider the uncertain effect of grinding, the time delay between programmed and actual infeed rate of grinding table is selected as weighting factor of the proposed roughness model. The developed roughness model is also used for the optimization algorithm of grinding procedure. Optimization algorithm in this study is constructed to minimize the grinding cost and to obtain the optimized dressing and grinding conditions under grinding constraints such as no-burn condition, limitation of roughness of workpiece, grinding power, etc. The optimized results also give an optimized dressing interval in batch production. The used optimization algorithm is an Evolutionary Strategy algorithm, and performance of the proposed algorithm was evaluated with experiments.

Improved differential evolution approach for optimization of surface grinding process

An improved differential evolution algorithm, named the Taguchi-sliding-based differential evolution algorithm (TSBDEA), is proposed in this work to solve the problem of optimization for the surface grinding process. The purpose of this work is to optimize the grinding variables such as wheel speed, workpiece speed, depth of dressing, and lead of dressing, using a multi-objective function model with a weighted approach, simultaneously subject to a comprehensive set of process constraints. The TSBDEA, a powerful global numerical optimization method, combines the differential evolution algorithm (DEA) with the Taguchi-sliding-level-method (TSLM). The TSLM is used as the crossover operation of the DEA. Then, the systematic reasoning ability of the TSLM is provided to select the better offspring to achieve the crossover, and consequently enhance the DEA. Therefore, the TSBDEA can be statistically sound and quickly convergent. The illustrative cases of both rough-grinding and finish-grinding are given to demonstrate the applicability of the proposed TSBDEA, and the computational results show that the proposed TSBDEA can obtain better results than the methods presented in the literatures.

Fuzzy Ć-Means Clustering Approach for Optimization of Surface Grinding Process with Acoustic Emission

Article Fuzzy Ć-Means Clustering Approach for Optimization of Surface Grinding Process with Acoustic Emission was published on December 1, 2009 in the journal Journal for Manufacturing Science and Production (volume 10, issue 3-4).

Dynamic Optimization of the Grinding Process in Batch Production

This paper presents a novel dynamic optimization framework for the grinding process in batch production. The grinding process exhibits time-varying characteristics due to the progressive wear of the grinding wheel. Nevertheless, many existing frameworks for the grinding process can optimize only 1 cycle at a time, thereby generating suboptimal solutions. Moreover, dynamic scheduling of dressing operations in response to process feedback would require significant human intervention with existing methods. We propose a unique dynamic programming–evolution strategy framework to optimize a series of grinding cycles depending on the wheel condition and batch size. In the proposed framework, a dynamic programming module dynamically determines the frequency and parameter of wheel dressing while the evolution strategy locates the optimal operating parameters of each cycle subject to the constraints on the operating ranges and part quality. Case studies based on experimental data are conducted to demonstrate the advantages of the proposed method over conventional approaches.

Creep feed grinding optimization by an integrated GA-NN system

The present work is aimed to optimize creep feed grinding (CFG) process by an approach using integrated Genetic Algorithm-Neural Network (GA-NN) system. The aim of this creep feed grinding optimization is obtain the maximal metal removal rate (MRR) and the minimum of the surface roughness (R a ). For optimization, metal removal rate is calculated with a mathematic formula and a Back Propagation (BP) artificial neural-network have been used to prediction of R a . The parameters used in the optimization process were reduced to three grinding conditions which consist of wheel speed, workpiece speed and depth of cut. All of other parameters such as workpiece length, workpiece material, wheel diameter, wheel material and width of grinding were taken as constant. The BP neural network was trained using the scaled conjugate gradient algorithm (SCGA). The results of the neural network were compared with experimental values. It shows that the BP model can predict the surface roughness satisfactorily. For optimization of creep feed grinding process, an M-file program has been written in `Matlab' software to integrate GA and NN. After generation of each population by GA, firstly, the BP network predicts R a and then MRR has been calculated with mathematic formula. In this study, the importance of R a and MRR is equal in the optimization process. By using this integrated GA-NN system optimal parameters of creep feed grinding process have been achieved. The obtained results show that, the integrated GA-NN system was successful in determining the optimal process parameters.