Minimum Surface Roughness Value Research Articles

The Optimization System Design and Experimental Study of Process Parameters Based on LabVIEW

With putting the response surface methodolody and design theory into the programming of LabVIEW8.6, the process parameters optimization system is designed. The system can realize the function of test project design,online data acquisition, response surface model building and process parameters' optimization, which can greatly reduce the calculated amount of modeling and optimization. Taking 45 steel milling experiment for example, the models of surface roughness and cutting force are established by using the optimization system, taking the minimum surface roughness value as target and optimizing the process parameters, which provide practical support tools for further analysis of process parameters on the effect of processing quality

Molecular Dynamic Simulation for Co Cluster Deposition on Si Substrate

Molecular dynamic simulation for Co cluster deposition on Si substrate was investigated in this work. The surface roughness and the interface mixing will be evaluated for the deposited film quality under different incident energies and substrate temperatures. The effect of thermal annealing on the ability of gap filling will be discussed by a slip vector. The results indicate that the incident energy has dominant effect on the surface roughness, and there is a minimum surface roughness value around the incident energy of 8 eV. However, the substrate temperature has little effect on the surface roughness. For interface mixing, the simulation indicates the easy diffusion of Co atoms into Si substrate. However, increasing either the incident energy or the substrate temperature could not change much the mixing conditions. As for the ability of gap filling, it is clear that the thermal annealing does improve this ability and obtains better surface roughness and interface mixing.

Optimal Machining Parameters for Achieving the Desired Surface Roughness in Turning of Steel

Due to the widespread use of highly automated machine tools in the metal cutting industry, manufacturing requires highly reliable models and methods for the prediction of output performance in the machining process. The prediction of optimal manufacturing conditions for good surface finish and dimensional accuracy plays a very important role in process planning. In the steel turning process the tool geometry and cutting conditions determine the time and cost of production which ultimately affect the quality of the final product. In the present work, experimental investigations have been conducted to determine the effect of the tool geometry (effective tool nose radius) and metal cutting conditions (cutting speed, feed rate and depth of cut) on surface finish during the turning of EN-31 steel. First and second order mathematical models are developed in terms of machining parameters by using the response surface methodology on the basis of the experimental results. The surface roughness prediction model has been optimized to obtain the surface roughness values by using LINGO solver programs. LINGO is a mathematical modeling language which is used in linear and nonlinear optimization to formulate large problems concisely, solve them, and analyze the solution in engineering sciences, operation research etc. The LINGO solver program is global optimization software. It gives minimum values of surface roughness and their respective optimal conditions.

Enabling and Understanding Ultrasonic Machining of Engineering Ceramics Using Parametric Analysis

Ultrasonic machining (USM) on ceramics is highly demanded in the present industry because of its wide and potential uses in various fields in modern industries. Experiments based on central composite second-order rotatable design have been conducted on ultrasonic drilling of hexagonal shaped hole on high alumina ceramic. RSM was employed for developing empirical models. Analysis on machining characteristics of ultrasonic drilling operation was made based on the developed models. In this study, abrasive grit size, slurry concentration, power rating, tool feed rate, and slurry flow rate are considered as USM process parameters. The process performances on workpiece material with respect to material removal rate (MRR) and surface roughness are evaluated by using analysis of variance (ANOVA). The optimum parametric combination of USM process parameters for maximum MRR and minimum value of surface roughness have been obtained through multiobjective optimization.

GA Approach for Optimization of Surface Roughness Parameters in Machining of Al Alloy SiC Particle Composite

Experiments were carried out using carbide turning inserts on AA7075/10 wt.% SiC (particle size 10-20 μm) composites to get actual input values to the optimization problem, so that the optimized results are realistic. By using experimental data, the regression model was developed. This model was used to formulate the fitness function of the genetic algorithm (GA). This investigation attempts to perform the application of GA for finding the optimal solution of the cutting conditions minimum value of surface roughness. The analysis of this investigation shows that the GA technique is capable of estimating the optimal cutting conditions that yield the minimum surface roughness value. With the highest speed, the lowest feed rate, the lowest depth of cut, and the highest nose radius of the cutting conditions' scale, the GA technique recommends 1.039 μm as the best minimum predicted surface roughness value. This means that the GA technique has decreased the minimum surface roughness value of the experimental sample data, regression modeling and desirability analysis by about 3%, 1%, and 2.8%, respectively.

Effect of radial depth on vibration and surface roughness during face milling of austenitic stainless steel

This paper studies the influence of radial depth on vibration, chip formation and surface roughness during face milling of AISI304 austenitic stainless steel with indexable cemented carbide milling cutters. The amplitude of vibration acceleration increased with the increasing radial depth up to 80 mm. And the domain vibration frequency varied with the radial depth. In this paper, three types of chips were found: C shape, long shape and spiral shape. The minimum surface roughness value occurred when the radial depth equalled 40 mm in the experiment. Irregular changes of chip curl radius and chip thickness could be attributed to different numbers of alternately engaged teeth when the feed and speed were fixed. Surface roughness is related to forced vibration and chip formation. Radial depth with different numbers of alternately engaged teeth could significantly influence the forced vibration, chip formation, and surface roughness.

Optimization of Precision Grinding Parameters of Silicon for Surface Roughness Based on Taguchi Method

Silicon being a typical hard-brittle material is difficult to machine to a good surface finish. Although ductile-mode machining (DMM) is often employed to machine this advanced material but this technique requires the use of expensive ultra-precision machine tools therefore limiting its applicability. However, by proper selection of grinding parameters, precision grinding which can be performed on conventional machine tools can be used to generate massive ductile surfaces thereby reducing the polishing time and improving the surface quality. Precision grinding should be planned with reliability in advance and the process has to be performed with high rates of reproducibility. Therefore, this study investigated the effect and optimization of grinding parameters using Taguchi optimization technique during precision grinding of silicon. Experimental studies were conducted under varying depths of cut, feed rates and spindle speeds. An orthogonal array (OA), signal-to-noise (S/N) ratio and the analysis of variance (ANOVA) were employed to find the minimum surface roughness value and to analyze the effect of the grinding parameters on the surface roughness. Confirmation tests were carried out in order to illustrate the effectiveness of the Taguchi method. The results show that feed rate mostly affected the surface roughness. The predicted roughness (Ra) of 34 nm was in agreement with the confirmation tests. Massive ductilestreaked surface was also found corresponding to the minimal surface finish determined from the optimal levels.

Improvement of Surface Roughness in End Milling of Ti6Al4V by Coupling RSM with Genetic Algorithm

Titanium alloys are being widely used in the aerospace, biomedical and automotive industries because of their good strength-to-weight ratio and superior corrosion resistance. Surface roughness is one of the most important requirements in machining of Titanium alloys. This paper describes mathematically the effect of cutting parameters on Surface roughness in end milling of Ti6Al4V. The mathematical model for the surface roughness has been developed in terms of cutting speed, feed rate, and axial depth of cut using design of experiments and the response surface methodology (RSM). Central composite design was employed in developing the surface roughness models in relation to primary cutting parameters. The experimental results indicate that the proposed mathematical models suggested could adequately describe the performance indicators within the limits of the factors that are being investigated. The developed RSM is coupled as a fitness function with genetic algorithm to predict the optimum cutting conditions leading to the least surface roughness value. MATLAB 7.0 toolbox for GA is used to develop GA program. The predicted results are in good agreement with the experimental one and hence the model can be efficiently used to achieve the minimum surface roughness value.

Application of GA to observe the optimal effect of the radial rake angle for minimising surface roughness in end milling

This study is carried out to observe the optimal effect of the radial rake angle of the tool, combined with speed and feed rate cutting conditions in influencing the surface roughness result. In machining, the surface roughness value is targeted as low as possible and is given by the value of the optimal cutting conditions. By looking at previous studies, as far as they have been reviewed, it seems that the application of GA optimization techniques for optimizing the cutting conditions value of the radial rake angle for minimizing surface roughness in the end milling of titanium alloy is still not given consideration by researchers. Therefore, having dealt with radial rake angle machining parameter, this study attempts the application of GA to find the optimal solution of the cutting conditions for giving the minimum value of surface roughness. By referring to the real machining case study, the regression model is developed. The best regression model is determined to formulate the fitness function of the GA. The analysis of this study has proven that the GA technique is capable of estimating the optimal cutting conditions that yield the minimum surface roughness value. With the highest speed, lowest feed rate and highest radial rake angle of the cutting conditions scale, the GA technique recommends 0.138 μm as the best minimum predicted surface roughness value. This means the GA technique has decreased the minimum surface roughness value of the experimental sample data, regression modelling and response surface methodology technique by about 27%, 26% and 50%, respectively.

Application of GA to optimize cutting conditions for minimizing surface roughness in end milling machining process

This study is carried out to observe the optimal effect of the radial rake angle of the tool, combined with speed and feed rate cutting conditions in influencing the surface roughness result. In machining, the surface roughness value is targeted as low as possible and is given by the value of the optimal cutting conditions. By looking at previous studies, as far as they have been reviewed, it seems that the application of GA optimization techniques for optimizing the cutting conditions value of the radial rake angle for minimizing surface roughness in the end milling of titanium alloy is still not given consideration by researchers. Therefore, having dealt with radial rake angle machining parameter, this study attempts the application of GA to find the optimal solution of the cutting conditions for giving the minimum value of surface roughness. By referring to the real machining case study, the regression model is developed. The best regression model is determined to formulate the fitness function of the GA. The analysis of this study has proven that the GA technique is capable of estimating the optimal cutting conditions that yield the minimum surface roughness value. With the highest speed, lowest feed rate and highest radial rake angle of the cutting conditions scale, the GA technique recommends 0.138 μm as the best minimum predicted surface roughness value. This means the GA technique has decreased the minimum surface roughness value of the experimental sample data, regression modelling and response surface methodology technique by about 27%, 26% and 50%, respectively.

DEPOSITION AND CHARACTERIZATION OF TiN-COATED STEELS AT VARIOUS N2 GAS FLOW RATES WITH CONSTANT ETCHING BY USING CAPVD TECHNIQUE

Cathodic Arc Physical Vapor Deposition (CAPVD), a technique used for the deposition of hard coatings, for tooling applications, has many advantages. The main drawback of this technique is the formation of macrodroplets (MDs) during deposition, resulting in films with rougher morphology. Constant etching, by increasing nitrogen gas flow rate up to 200 sccm, helped in reducing the MD size and number; at higher rates, of say 300 sccm, the behavior was reversed. Minimum value of surface roughness recorded at 200 sccm was measured via both surface roughness tester and atomic force microscopy (AFM). Micro-Vickers hardness of TiN -coated tool showed about 564% times increase in hardness than the uncoated one. Scratch tester was used to study the critical loads for the coating and the excellent adhesion achievable, of say 200 sccm, was demonstrated, with relevance to the various modes.

Optimization of Tool Geometry and Cutting Parameters for Hard Turning

In the hard-turning process, tool geometry and cutting conditions determine the time and cost of production which ultimately affect the quality of the final product. So reliable models and methods are required for the prediction of the output performance of the process. In the present work, experimental investigation has been conducted to see the effect of the tool geometry (effective rake angle and nose radius) and cutting conditions (cutting speed and feed) on the surface finish during the hard turning of the bearing steel. First- and second-order mathematical models were developed in terms of machining parameters by using the response surface methodology on the basis of the experimental results. The surface roughness prediction model has been optimized to obtain the surface roughness values by using genetic algorithms. The genetic algorithm program gives minimum values of surface roughness and their respective optimal conditions.

The Effect of Hydroxy Ethyl Cellulose (HEC) and Chloride Ions on the Surface Morphology and Mechanical Characteristics during Copper Electrodeposition

The effect of additives on the surface morphology and mechanical characteristics during copper electrodeposition on the polyimide(PI) film was investigated. Two kinds of additives such as hydroxy ethyl cellulose (HEC) and chloride ions were used in this study. Electrodeposition was carried out with the constant current density of 200 mA/cm2 for 68 seconds. Electrochemical experiments, in conjunction with SEM, XRD, AFM and four-point probe, were performed to characterize the morphology and mechanical characteristics of copper electrodeposited in the presence of the additives. The minimum surface roughness of copper deposit was obtained when deposited with 10 ppm of chloride ions. The minimum value of surface roughness (rms value) was 97.05 nm. And the optimum amount of additives to improve the electromigration resistance and ductility of the deposit was a combination of 1 ppm HEC and 10 ppm chloride ions.

The Effect of Hydroxy Ethyl Cellulose(HEC) on the Surface Morphology and Mechanical Characteristis of Copper Electrodeposition

The purpose of this study is to identify the effect of additives and composition on copper surface morphology and mechanical characteristics by copper electrodeposition. Additives such as hydroxy ethyl cellulose(HEC), chloride ion were used in this study. Electrochemical experiments allied to SEM, XRD, AFM and four- point probe were performed to characterize the morphology and mechanical characters of copper in the presence of additives. Among various electrodeposition conditions, the minimum surface roughness of copper foil was obtained when electrodeposited at the current density of 200 mA/<TEX>$cm^2$</TEX> for 68 seconds with 2 ppm of HEC. The minimum value of surface roughness(Rms) was 107.6 nm. It is copper foil is good for electromigration inhibition due to preferential crystal growth of Cu (111) deposited in the electrolyte containing chloride ions(10 ppm) and HEC(1 ppm).

The statistical modeling of surface roughness in high-speed flat end milling

Surface roughness is one of the most important requirements in machining process. The surface roughness value is a result of the tool wear. When tool wear increase, the surface roughness also increases. The determination of the sufficient cutting parameters is a very important process obtained by means of both minimum surface roughness values and long tool life. The statistical models were developed to predict the surface roughness. This paper presents the development of a statistical model for surface roughness estimation in a high-speed flat end milling process under wet cutting conditions, using machining variables such as spindle speed, feed rate, depth of cut, and step over. First- and second-order models were developed using experimental results of a rotatable central composite design, and assessed by means of various statistical tests. The highest coefficient of correlation ( R adj 2) (88%) was obtained with a 10-parameter second-order model. Meanwhile, a time trend was observed in residual values between model predictions and experimental data, reflecting the probable effect of the tool wear on surface roughness. Thus, in order to enhance the estimation capability of the model, another independent variable was included into the model to account for the effect of the tool wear, and the total operating time of the tool was selected as the most suitable variable for this purpose. By inserting this new variable as a linear term into the model, R adj 2 was increased to 94% and a good fit was observed between the model predictions and supplementary experimental data. In this study, it was observed that, the order of significance of the main variables is as X 5 > X 3 > X 4 > X 1 > X 2 (total machining time, depth of cut, step over, spindle speed and feed rate, respectively).

Surface modification of exchange-coupled Co/NiO x magnetic bilayer by bias sputtering

We have investigated the effect of bias voltage on sheet resistance, surface roughness and surface coverage of Co/NiO x magnetic bilayer. In addition, interface topography and corrosion resistance of the Ta/Co/Cu/Co/NiO x/Si(1 0 0) system have been studied for Co layers deposited at an optimum bias voltage. Atomic force microscopy (AFM) and four point probe sheet resistance ( R s ) measurement have been used to determine surface and electrical properties of the sputtered Co layer at different bias voltages ranging from 0 to − 80 V. The Co/NiO x bilayer exhibits a minimum surface roughness and low sheet resistance value with a maximum surface coverage at V b = − 60 V resulted in a slight increase of magnetic resistance and its sensitivity for the Co/Cu/Co/NiO x/Si(1 0 0) magnetic multilayers, as compared with the same magnetic multilayers containing unbiased Co layers. The presence of Ta protection layer improves the corrosion resistance of the multilayers by three orders of magnitude in a humid environment.

Selection of optimum tool geometry and cutting conditions using a surface roughness prediction model for end milling

Influence of tool geometry on the quality of surface produced is well known and hence any attempt to assess the performance of end milling should include the tool geometry. In the present work, experimental studies have been conducted to see the effect of tool geometry (radial rake angle and nose radius) and cutting conditions (cutting speed and feed rate) on the machining performance during end milling of medium carbon steel. The first and second order mathematical models, in terms of machining parameters, were developed for surface roughness prediction using response surface methodology (RSM) on the basis of experimental results. The model selected for optimization has been validated with the Chi square test. The significance of these parameters on surface roughness has been established with analysis of variance. An attempt has also been made to optimize the surface roughness prediction model using genetic algorithms (GA). The GA program gives minimum values of surface roughness and their respective optimal conditions.