Desired Surface Roughness Research Articles

A Neuro-fuzzy Approach to Select Cutting Parameters for Commercial Die Manufacturing

Surface roughness is a quality index for machined surfaces. In this study an algorithm has been developed to determine the feasible solutions for cutting parameters in order to obtain desired surface roughness for three dimensional dies. Here the average surface roughness values for a commercial die material EN24 after ball end milling operation have been measured after experiments with different cutting parameters. These datasets have been used for training and testing different prediction models like artificial neural network (ANN), response surface methodology (RSM), adaptive neuro-fuzzy inference system (ANFIS) and mathematical equation based on machining theories. ANFIS model has been selected as better prediction model because it has shown minimum value of root mean square error (RMSE) and mean absolute percentage error (MAPE) for training and testing datasets. This ANFIS model has been used further for predicting surface roughness of a typical die made of EN24 after ball end milling operation.

Modelling and optimization of Nd:YAG laser micro-turning process during machining of aluminum oxide (Al2O3) ceramics using response surface methodology and artificial neural network

Pulsed Nd:YAG laser has high intensity and high quality beam characteristics, which can be used to produce micro-grooves and micro-turning surface on advanced engineering ceramics. The present research attempts to develop mathematical models by using response surface methodology approach for correlating the machining process parameters and the process responses during laser micro-turning of aluminum oxide (Al2 O3 ) ceramics. The process parameters such as laser average power, pulse frequency, workpiece rotating speed, assist air pressure and Y feed rate were varied during experimentation. The rotatable central composite design experimental planning has been used to design the experimentation. The performance measures considered are surface roughness (Ra ) and micro-turning depth deviation. Multi-objective optimization has been carried out for achieving the desired surface roughness as well as minimum depth deviation during laser micro-turning operation. Further, an artificial neural network (ANN) model has been developed to predict the process criteria. Levenberg-Marquadt training algorithm is used for multilayer feed forward backpropagation neural network. The developed ANN model has 5-10-2 feed forward network. There are 5 neurons in the input layer, 10 neurons in the hidden layer and 2 neurons in the output layers corresponding to two output responses, respectively. The developed ANN model has been validated using data obtained by conducting additional set of experiments. It was found that the developed ANN model can predict the process criteria more accurately than response surface methodology (RSM) based developed models.

Surface Roughness Control Based on Digital Copy Milling Concept to Achieve Autonomous Milling Operation

An autonomous and intelligent machine tool that performs machining operations by referring to CAD product data was developed in our previous study to solve fundamental issues with the conventional command method, which uses NC programs. A system, Digital Copy Milling (DCM), digitizing the principle of copymilling, was developed to generate tool paths during milling operations for dynamic tool motion control. In the DCM, the cutting tool is controlled dynamically to follow the surface of a CAD model corresponding to product shape, eliminating the need for the preparation of NC programs. Active tool motion controls were also realized to enhance the function of DCM. In this study, surface roughness control of the finished surface is realized as an additional enhanced function of DCM to achieve autonomous milling operations. This function allows the DCM to select cutting conditions and generates tool paths dynamically to produce the desired surface roughness: from rough, through semi-finished, to finished. The verification experiment is successfully carried out.

Process Parametric Optimization of CNC Vertical Milling Machine Using Taguchi Technique in Varying Condition

AbstractAn experiment was conducted to perform the parametric optimization of CNC end milling machine tool in varying condition. The tool used for experiment was of Solid Carbide and the Mild Steel work piece was used during experiment. The experiment has been taken place efficiently and completes its all objective of optimization. The practical result can be used in industry to get the desirable Surface Roughness and Material Removal Rate for the work piece by using suitable parameter combination.

Multi Quality Characteristics Of WEDM Process Parameters with RSM

Wire electric discharge machine (WEDM) is a spark erosion non conventional machining method to cut hard and conductive material with the help of a wire electrode. High strength low alloy steel (HSLA) is a hard alloy with high hardness and wear resisting property. The purpose of this study is to investigate the effect of parameters on metal removal rate for WEDM using HSLA as work-piece and brass wire as electrode. HSLA used in cars, trucks, cranes, bridges, roller coasters and other structures that are designed to handle large amounts of stress. It is observed that metal removal rate and surface roughness increases with increase in pulse on time and peak current. Metal removal rate and surface roughness decreases with increase in pulse off time and servo voltage. Wire mechanical tension has no significant effect on metal removal rate and surface roughness. Response Surface methodology (RSM) is used to optimize the process parameter for metal removal rate and surface roughness. Response Surface Methodology is formulating a mathematical model which correlates the independent process parameters with the desired metal removal rate and surface roughness. The central composite rotatable design (CCRD) has been used to conduct the experiments.

Obtaining desired surface roughness of castings produced using ZCast direct metal casting process through Taguchi's experimental approach

PurposeThe purpose of this paper is to investigate experimentally the effect of volume of casting, pouring temperature of different materials and shell mould wall thickness on the surface roughness of the castings obtained by using ZCast direct metal casting process.Design/methodology/approachTaguchi's design of experiment approach was used for this investigation. An L9 orthogonal array (OA) of Taguchi design which involves nine experiments for three factors with three levels was used. Analysis of variance (ANOVA) was then performed on S/N (signal‐to‐noise) ratios to determine the statistical significance and contribution of each factor on the surface roughness of the castings. The castings were obtained using the shell moulds fabricated with the ZCast process and the surface roughness of castings was measured by using the surface roughness tester.FindingsTaguchi's analysis results showed that pouring temperature of materials was the most significant factor in deciding the surface roughness of the castings and the shell mould wall thickness was the next most significant factor, whereas volume of casting was found insignificant. Confirmation test was also carried out using the optimal values of factor levels to confirm the effectiveness of this approach. The predicted optimal value of surface roughness of castings produced by ZCast process was 6.47 microns.Originality/valueThe paper presents experimentally investigated data regarding the influence of various control factors on the surface roughness of castings produced by using ZCast process. The data may help to enhance the application of ZCast process in traditional foundry practice.

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.

Turning CFRP Composites with Ceramic tool for Surface Roughness Analysis

This experimental study concentrates on the understanding of machining process in turning of carbon fiber reinforced polymeric composites using ceramic cutting tool. Carbon fiber reinforced polymer material has started replacing conventional materials a long back due to the excellent properties they possess for various applications in engineering and technology. Though they are produced to near net shape, often they are in need of machining. But it contains matrix and fiber reinforcement, which are soft and hard in nature respectively, resulting in anisotropic and non-homogeneous properties. Due to these properties of composite material, machining of these materials is completely different than that of the conventional materials such as metals. To understand the machinability of these materials, this experimental work has been undertaken. The experimentation was carried out using three machining parameters namely cutting speed, feed and depth of cut. The motivation of this experimental study was towards identification of machining parameter that plays a dominant role on surface roughness, because surface roughness affects various properties of material and strength. Further to identify the combination of machining parameters that provides desirable surface roughness. It used Taguchi's orthogonal array for easy conduction of experimentation and analysis of variance for analyzing the machining parameters. It was observed that the ceramic cutting tool offers satisfactory level of surface roughness.

Application of Particle Swarm Optimization technique for achieving desired milled surface roughness in minimum machining time

Face milling is a widely used machining operation to produce various components. The finished component depends not only on the dimensional accuracy but also on the surface finish. The present method of selection of machining parameters by trial and error, previous work experience of the process planner and machining hand books are time consuming and very tedious. There is a need to develop a technique that could able to find the optimal machining parameters for the required surface roughness in machining. In this work, experimental investigations are carried out on aluminium material to study the effect of machining parameters such as cutting speed, feed, and depth of cut on the surface roughness and to obtain the desired surface roughness on face milling process. Mathematical model has been developed for surface roughness prediction using Particle Swarm Optimization (PSO) on the basis of experimental results. The model developed for optimization has been validated by confirmation experiments. Physical constraints for both experiment and theoretical approach are the proposed machining parameters and surface roughness. It has been found that the predicted roughness using PSO is in good agreement with the actual roughness.

Single Input Multi Output Adaptive Network Based Fuzzy Inference System for Machinability Data Selection in Turning Operations

The selection of machining parameters needs to be automated, according to its important role in machining process. This paper proposes a method for cutting parameters selection by fuzzy inference system generated using fuzzy subtractive clustering method (FSCM) and trained using an adaptive network based fuzzy inference system (ANFIS). The desired surface roughness (Ra) was entered into the first step as a reference value for three fuzzy inference system (FIS). Each system determine the corresponding cutting parameters such as (cutting speed, feed rate, and depth of cut). The interaction between these cutting parameters were examined using new sets of FIS models generated and trained for verification purpose. A new surface roughness value was determined using the cutting parameters resulted from the first steps and fed back to the comparison unit and was compared with the desired surface roughness and the optimal cutting parameters ( which give the minimum difference between the actual and predicted surface roughness were find out). In this way, single input multi output ANFIS architecture presented which can identify the cutting parameters accurately once the desired surface roughness is entered to the system. The test results showed that the proposed model can be used successfully for machinability data selection and surface roughness prediction as well.

An experimental investigation of surface generation using an integrated ultra-precision polishing process and different polishing trajectories

Ultra-precision freeform polishing (UPFP) is an emerging technology for the machining of ultra-precision freeform surfaces with submicrometre form accuracy and surface roughness in the nanometre range. However, our understanding of the surface generation and process planning for UPFP is still far from complete. This paper presents an experimental study of the surface generation for different polishing processes, which include mechanical polishing (MP) and fluid jet polishing (FJP). In addition, the generation of desirable structured surfaces of UPFP has also been experimentally investigated to examine if different trajectory planning affects surface hydrophobicity. The experimental results show that both MP and FJP can produce similar surface roughness but differences in surface texture after polishing. Desirable surface roughness and structured surfaces can be produced by appropriate planning and control of the combination of MP and FJP, and tool path trajectories in UPFP. The results also prove that tool path trajectories affect the hydrophobicity of polished surfaces. The results provide an important means for the development of an integrated polishing process to achieve the desirable surface roughness and surface texture based on process planning and control in UPFP.

A Comparative Investigation on Surface Roughness and Residual Stress during End-Milling AISI H13 Steel with Different Geometrical Inserts

In the present study, an attempt has been made to experimentally investigate the effects of cutting parameters (cutting speed, feed, radial depth of cut, and axial depth of cut) on surface roughness and residual stress during end-milling AISI H13 steel with the different geometrical inserts, i.e., the parallelogram inserts and the round inserts. Seventeen single-factor experiments were conducted on the Daewoo machining center. The white light interferometer was used to obtain the two-dimensional (2D) and three-dimensional (3D) profile of the milled surface and to calculate Ra value, and the residual stress analyzer was used to measure the surface residual stress along the step-over and feed directions, respectively. It is noticed that the surface milled with the round inserts has far more superior surface finish than that milled with the parallelogram inserts, while the residual stress generated with the round inserts is more compressive than that generated with the parallelogram inserts. “Best” combination of surface roughness and residual stress of the milled surface are not achieved with the same geometrical inserts and cutting parameters. Therefore, a compromise in selecting the geometry of the inserts and the cutting parameters is necessary to achieve the desired surface roughness and residual stress when end-milling AISI H13 steel.

Surface modification on a glass surface with a combination technique of sol–gel and air brushing processes

This study fabricated the large area and optically transparent superhydrophobic silica based films on glass surface with optimized hardness. A silane coupling agent, tetraethoxysilane (TEOS), effectively bonds silica particles onto the glass substrate. Desired surface roughness was obtained by adjusting nano silica particles concentration of the precursors prepared by the sol–gel process. Silica suspension was coated onto the glass substrate by the air brushing methods. This method can deposit a uniform, transparent coating on the glass substrate efficiently. Diluting the precursor by adding ethanol or a mixture of D.I. water and ethanol further improved the transmittance and superhydrophobicity efficiency. The results showed that as the silica particle concentration and the thickness of the coating were increased, the surface roughness was enhanced. Rougher surface displayed a higher superhydrophobicity and lower transmittance. Therefore, the concentration of silica particle, volume of coatings, and the ratio of ethanol and D.I. water are of great importance to deposit a transparent, superhydrophobic coating on glass.

Optimization of Cutting Parameters for Desirable Surface Roughness in End-Milling Hardened AISI H13 Steel under a Certain Metal Removal Rate

In this paper, the orthogonal experiments and the optimization experiments with the same metal removal rate are designed to investigate the main effects and primary interaction of cutting parameters on surface roughness and to search the optimal cutting parameter under a certain removal rate when end-milling hardened AISI H13 steel with the PVD coated carbide insert. The empirical model for surface roughness based on the orthogonal experiments and the optimization experiments with the same metal removal rate and the optimal cutting parameter were all verified. Under a certain metal removal rate, the combination of high cutting speed, small axial depth of cut and high feed, small radial depth of cut generates the best surface roughness in hard milling of AISI H13.

Optimization of Machining Parameters on Surface Roughness in EDM of Ti-6Al-4V Using Response Surface Method

This paper presents the influence of EDM parameters in terms of peak ampere, pulse on time and pulse off time on surface roughness of titanium alloy (Ti-6Al-4V). A mathematical model for surface finish is developed using response surface method (RSM) and optimum machining setting in favor of surface finish are evaluated. Design of experiments (DOE) techniques is implemented. Analysis of variance (ANOVA) has been performed to verify the fit and adequacy of the developed mathematical models. The acquired results yield that the increasing pulse on time causes fine surface till a certain value and then deteriorates the surface finish. It is investigated that about 200 µs pulse off time produce superior surface finish. These results lead to desirable surface roughness and economical industrial machining by optimizing the input parameters.

Cutting parameters identification using multi adaptive network based Fuzzy inference system: An artificial intelligence approach

The influences of the machine parameters on machined parts are not always precisely known and hence, it becomes difficult to recommend the optimum machinability data for machine process. This paper proposes a method for cutting parameters identification using Multi adaptive Network based Fuzzy Inference System (MANFIS). Three Adaptive Network based Fuzzy Inference System (ANFIS) models were used in the first step to identify the initial values for the cutting parameters (cutting speed, feed rate, and depth of cut) using surface roughness as a single input, in the next step these parameters were modified and verified using another set of ANFIS models. Then, workpiece surface temperature is used as input for another set of ANFIS models to amend the final values of the cutting parameters. In this way, multi-input-multi-output ANFIS structure presented, which can identify the cutting parameters accurately once the desired surface roughness and in-process measured surface temperature were entered to the system. The test results showed that the proposed MANFIS model can be used successfully for machinability data selection.

Parametric optimisation of surface roughness on wire-EDM using Taguchi method

The main aim of this paper is to select the most suitable machining parameters (pulse peak current, pulse-ON time, pulse-OFF time, wire feed, wire tension and flushing pressure) combination for the wire-EDM process in order to get the desired surface roughness (SR) for the M2-high speed steel machined material. Analysis of variance (ANOVA) is used to investigate the machining parameters affecting the SR. The result shows that pulse peak current, pulse-OFF time and wire feed are the significant parameters to minimise the surface roughness. The machined surfaces of HSS were examined by scanning electron microscope (SEM) and Taylor Hobson Talysurf Range to determine the surface roughness. The surface texture was found to have greater dependency on pulse peak current and pulse-OFF time.

Statistical analysis of process parameters in drilling of AL/SICP metal matrix composite

This paper presents a statistical analysis of process parameters for surface roughness in drilling of Al/SiCp metal matrix composite. The experimental studies were conducted under varying spindle speed, feed rate, drill type, point angle of drill, and heat treatment. The settings of drilling parameters were determined by using Taguchi experimental design method. The level of importance of the drilling parameters is determined by using analysis of variance. The optimum drilling parameter combination was obtained by using the analysis of signal-to-noise ratio. Through statistical analysis of response variables and signal-to-noise ratios, the determined significant factors were the feed rate and tool type. Confirmation tests verified that the selected optimal combination of process parameter through Taguchi design was able to achieve desired surface roughness. The optimal drilling performance for the surface roughness was obtained at 0.16 mm/rev feed rate, 260 rev/min spindle speed, 130° drill point angle, carbide drill type, and as-received heat treatment settings.

Particle swarm optimization technique for determining optimal machining parameters of different work piece materials in turning operation

Empirical models for machining time and surface roughness are described for exploring optimized machining parameters in turning operation. CNC turning machine was employed to conduct experiments on brass, aluminum, copper, and mild steel. Particle swarm optimization (PSO) has been used to find the optimal machining parameters for minimizing machining time subjected to desired surface roughness. Physical constraints for both experiment and theoretical approach are cutting speed, feed, depth of cut, and surface roughness. It is observed that the machining time and surface roughness based on PSO are nearly same as that of the values obtained based on confirmation experiments; hence, it is found that PSO is capable of selecting appropriate machining parameters for turning operation.

An approach to the economic manufacture of an aerostatic lead screw for micro-scale machine tools

A new manufacturing method is presented for an aerostatic lead screw actuator (ALSA) designed for use in a hard-turning micro-scale machine tool. The actuator is designed for a travel of 20 mm and to achieve a static stiffness of 50 N/mm and dynamic stiffness of 40–60 N/mm for a range of frequencies from 50 Hz to 500 Hz, respectively. The design of the aerostatic bearing is based on a orifice-restricted groove-fed collar bearing and uses replication of the lead screw geometry by casting with a low-shrink epoxy to produce the nut. The nut is designed to achieve a static stiffness of 50 N/μm and dynamic stiffness of 40–60 N/μm with friction below 0.1 N m. Equations for the aerostatic bearing are adopted from a mathematical model of an aerostatic collar bearing and used to determine the screw size, orifice diameter and air gap. The design evaluation led to the selection of an air gap of 9 μm and an orifice diameter of 1 mm. By using a two-revolution nut, the stiffness requirements of the ALSA are achieved. Methods to produce the orifices, grooves, nut housing and desired surface roughness on the nut are presented. Several alternative nut designs used to construct the ALSA are evaluated according to stability and friction characteristics. Stability is found to be improved by using a single revolution nut due to better control of the orifice size, but at the expense of some system stiffness. Potential methods to control the air gap size are also presented.