Mathematical Model For Surface Roughness Research Articles

Improving Surface Roughness of Burnished Components using Abrasive Particles

Roller burnishing process was carried out on free cutting brass materials in the presence of fine silicon carbide abrasives in the form of paste on a pre-machined surface. The results of ‘without-paste’ burnishing (plain burnishing, PB) and ‘with-paste’ burnishing (abrasive assisted burnishing, AAB) processes are compared to examine the effect of abrasive particles in the burnishing process. A 24 full factorial design is adopted to develop the mathematical model for surface roughness regarding four process parameters like burnishing force, burnishing speed, burnishing feed and number of passes for both the cases, i.e. PB and AAB. Analysis of variance (ANOVA) was carried out to find the effect of process parameters and to check the adequacy of the models. The results show that the parameters have a significant effect on the response in PB to improve the surface roughness by 75 % than the turned components. Whereas in AAB, fine abrasive particles as a single entity controlling the response and making other parameter effects as non-significant. Surface roughness further improved by 15 % in AAB process.

A mathematical model for surface roughness of fluidic channels produced by grinding aided electrochemical discharge machining (G-ECDM)

Grinding aided electrochemical discharge machining is a hybrid technique, which combines the grinding action of an abrasive tool and thermal effects of electrochemical discharges to remove material from the workpiece for producing complex contours. The present study focuses on developing fluidic channels on borosilicate glass using G-ECDM and attempts to develop a mathematical model for surface roughness of the machined channel. Preliminary experiments are conducted to study the effect of machining parameters on surface roughness. Voltage, duty factor, frequency and tool feed rate are identified as the significant factors for controlling surface roughness of the channels produced by G-ECDM. A mathematical model was developed for surface roughness by considering the grinding action and thermal effects of electrochemical discharges in material removal. Experiments are conducted to validate the model and the results obtained are in good agreement with that predicted by the model.

Analysis of Surface Roughness during Machining of Hardened AISI 4340 Steel using Minimum Quantity lubrication

Trends of machining hardened steel are increasing in manufacturing industry due to its advantages in surface quality over grinding and other finishing process. One of the major benefits of hard turning is attaining better surface quality with high dimensional accuracy. However, large amount of heat generation during hard turning to affects the machining performance. The application of lubricants during the machining are used to control the temperature. The use of conventional cutting fluids have become more problematic in terms of health and environmental pollution. Minimization of cutting fluid will lead to cost-effectiveness in overall machining. Therefore, the aim of this research work is to investigate the role of minimum quantity lubrication (MQL) technique on surface quality in turning of hardened AISI 4340 steel with CBN tools. The cutting speed, feed rate, and nose radius and workpiece hardness are selected as process parameters during investigations. The mathematical model of surface roughness is developed using second order regression analysis. Analysis of Variance (ANOVA) results show that MQL helps in improving the surface quality as compared to dry and wet conditions in turning.

Effect of Cutting Parameters on Surface Roughness in End Milling of AlSi/AlN Metal Matrix Composite

This paper presents the effects of cutting parameters and the corresponding prediction model on the surface roughness in the machining of AlSi/AlN metal matrix composite (MMC). This new composite material was fabricated by reinforcing smaller sizes of AlN particles at volume fractions of 10%, 15% and 20% with AlSi alloy. The machining experiments involved of uncoated carbide tool and PVD TiAlN coated carbide and conducted at different cutting parameters of cutting speed (240–400m/min), feed rate (0.3–0.5mm/tooth) and depth of cut (0.3–0.5mm) under dry cutting conditions. Taguchi's L18 orthogonal arrays approach was performed to determine the optimum cutting parameters using a signal-to-noise (S/N) ratio according to the stipulation of the smaller-the-better. The test results revealed that the type of cutting tool is the most significant factor contributing to the surface roughness of the machined material. A mathematical model of surface roughness has been developed using regression analysis as a function of all parameters with an average error of 10% can be observed between the predicted and experimental values. Furthermore, the optimum cutting parameters was predicted; A1 (uncoated carbide), B2 (cutting speed: 320m/min), C2 (feed rate: 0.4mm/tooth), D2 (axial depth: 0.4mm) and E1 (10% reinforcement) and validation experiment showed the reliable results.

Prediction Model of Cutting Parameters for Turning High Strength Steel Grade-H: Comparative Study of Regression Model versus ANFIS

The Grade-H high strength steel is used in the manufacturing of many civilian and military products. The procedures of manufacturing these parts have several turning operations. The key factors for the manufacturing of these parts are the accuracy, surface roughness (Ra), and material removal rate (MRR). The production line of these parts contains many CNC turning machines to get good accuracy and repeatability. The manufacturing engineer should fulfill the required surface roughness value according to the design drawing from first trail (otherwise these parts will be rejected) as well as keeping his eye on maximum metal removal rate. The rejection of these parts at any processing stage will represent huge problems to any factory because the processing and raw material of these parts are very expensive. In this paper the artificial neural network was used for predicting the surface roughness for different cutting parameters in CNC turning operations. These parameters were investigated to get the minimum surface roughness. In addition, a mathematical model for surface roughness was obtained from the experimental data using a regression analysis method. The experimental data are then compared with both the regression analysis results and ANFIS (Adaptive Network-based Fuzzy Inference System) estimations.

Effect of machining parameters on surface finish of Inconel 718 in end milling

Surface finish is an important criteria in machining process and selection of proper machining parameters is important to obtain good surface finish. In the present work effects of the machining parameters in end milling of Inconel 718 were investigated. Central composite design was used to design the total number of experiments. A Mathematical model for surface roughness has been developed using response surface methodology. In this study, the influence of cutting parameters such as cutting speed, feed rate and depth of cut on surface roughness was analyzed. The study includes individual effect of cutting parameters on surface roughness as well as their interaction. The analysis of variance (ANOVA) was employed to find the validity of the developed model. The results show that depth of cut mostly affected the surface roughness. It is also observed that surface roughness values are comparable in both dry and wet machining conditions.

Surface quality evaluation of AISI 4340 steel having varying hardness during machining with TiN-coated CBN inserts

Trends of turning hardened steel are increasing in manufacturing industries because it offers many significant benefits over grinding. One of the main advantages is attaining better surface quality along with high dimensional accuracy. However, during hard turning the friction between tool and workpiece is very high, which results in early tool wear that leads to decrease in surface quality and dimensional accuracy of the finished products. The coating on cutting tool plays a vital role in enhancing wear resistance. Therefore, the aim of this research work is to investigate the surface quality during machining hardened AISI 4340 steel with TiN-coated CBN tools. The process variables selected during experimentation are tool nose radius, workpiece hardness, cutting speed and feed rate. The machining is carried out in dry conditions. The second-order mathematical model of surface roughness has been developed using regression analysis. The significance of the model and influence of the process parameters have been carried out using analysis of variance technique. Results indicate that performance of TiN-coated CBN tools is superior when compared to uncoated CBN tools during machining of hardened AISI 4340 steel.

Mathematical Modeling of Surface Roughness during Drilling of Resin Hybrid GFRP Composites

The preparation, characterization and machinability of resin hybrid GFRP composites are presented in this paper. It is made up of Glass fiber and the mixture of epoxy and polyester resin. It was prepared by the traditional layout technique with the usage of Glass fiber. The weight percentages of GFRP are 0, 20 and 40 having the composition of polyester and epoxy resins. The study of ASTM revealed the variation of break loads and shear strength for three different combinations of epoxy and polyester resins. The main disadvantage of this method is that it resulted in poor dimensional stability and surface roughness. Our main goal was to minimize the surface roughness. There are certain drilling parameters such as composite material, point angle, spindle speed and feed rate during drilling of resin hybrid GFRP which are to be focused to minimize the surface roughness. The experiments were conducted and the results were obtained using Taguchi’s L9 orthogonal array design. It was further analyzed using Signal-to-noise Ratio. Investigating the cutting characteristics of Resin hybrid GFRP proved to be mandatory. This was done by using the Analysis of variance (ANOVA). The Minitab 15 software was used to determine the correlation by linear Regression methods. The value obtained was subjected to a recheck to check the percentage error. The percentage error between the experimental and theoretical value was minimal.

Mathematical modelling of surface roughness for vapour processing of ABS parts fabricated with fused deposition modelling

Fused deposition modelling (FDM) has number of applications in the field of metal casting but the poor surface finish acts as major obstruction against rapid investment casting of the replicas prepared by this process. The vapour smoothing (VS) has proved to be promising technique as fine details of parts are preserved due to absence of tool-work piece contact and mechanical force. In the present study, the outcome of Taguchi and ANOVA analysis has been used to develop a mathematical model for average surface roughness of vapour treated parts prepared by FDM. The relationship between six input parameters, two of FDM and four of VS has been derived using Buckingham's Pi theorem. The study explored the effect of smoothing time and number of cycles on surface roughness and as confirmed by statistical analysis. The model gives output in form of percentage change in surface roughness as a function of smoothing time and number of cycles where final roughness of vapour exposed FDM parts can be predicted.

Optimization of surface roughness in ball-end milling using teaching-learning-based optimization and response surface methodology

Surface roughness is one of the most important requirements of the finished products in machining process. The determination of optimal cutting parameters is very important to minimize the surface roughness of a product. This article describes the development process of a surface roughness model in high-speed ball-end milling using response surface methodology based on design of experiment. Composite desirability function and teaching-learning-based optimization algorithm have been used for determining optimal cutting process parameters. The experiments have been planned and conducted using rotatable central composite design under dry condition. Mathematical model for surface roughness has been developed in terms of cutting speed, feed per tooth, axial depth of cut and radial depth of cut as the cutting process parameters. Analysis of variance has been performed for analysing the effect of cutting parameters on surface roughness. A second-order full quadratic model is used for mathematical modelling. The analysis of the results shows that the developed model is adequate enough and good to be accepted. Analysis of variance for the individual terms revealed that surface roughness is mostly affected by the cutting speed with a percentage contribution of 47.18% followed by axial depth of cut by 10.83%. The optimum values of cutting process parameters obtained through teaching-learning-based optimization are feed per tooth ( fz) = 0.06 mm, axial depth of cut ( Ap) = 0.74 mm, cutting speed ( Vc) = 145.8 m/min, and radial depth of cut ( Ae) = 0.38 mm. The optimum value of surface roughness at the optimum parametric setting is 1.11 µm and has been validated by confirmation experiments.

Mathematical Modelling of Surface Roughness on Tropical Wood Machining Using Response Surface Methodology

Surface roughness is an important indicator to assess the surface processing quality and has a decisive impact on the furniture finishing effects. In this research, the application of response surface methodology (RSM) has been carried out for modelling and analysing of influences in the sanding process on wood materials. Surface roughness parameterRashowed surface characteristics of Tembesu, Jati and Petanang. This study is aimed to observe the effect of feed rate and grit size onRa. The central composite design (CCD) was used as a design of experiment (DOE). There were 8 runs at factorial points and additional 5 replicated runs at the centre point. The sanding process was done using a modified horizontal milling machine. The results are statistically analysed by using Design Expert software. It was found that increasing of feed rate had a positive effect on the roughness value ofRaand greater feed rates increased the surface roughness. On the other hand, grit size influenced negative effect. Larger grit size affected the smoother surface roughness. At the end of this study, it was also revealed that the optimum machining conditions in terms of feed rate and grit size were 17 mm/min and 240 for Tembesu and Jati, while Petanang was 18.63 mm/min and 226.52.

Mathematical modeling of surface roughness in magnetic abrasive finishing of BK7 optical glass.

Magnetic abrasive finishing (MAF) is one of the advanced machining processes efficiently used to finish hard-to-machine materials. Simulation and modeling of the process is of particular importance to understand the mechanics of material removal and consequently achieve a high-quality surface with a minimum of surface defects. Hence, in this paper, we performed a numerical-experimental study to mathematically model the surface roughness during the MAF of BK7 optical glass. For this purpose, the initial roughness profile was estimated using fast Fourier transform (FFT) and a Gaussian filter. We obtained the final surface profile based on the material removal mechanisms and the corresponding chipping depth values evaluated by finite element analysis. We then validated experimentally the simulation results in terms of the arithmetic average surface roughness (R(a ). The comparison between the obtained results demonstrates that the theoretical and experimental findings are in good agreement when predicting the parameters' effect on surface roughness behavior.

Surface Roughness Prediction Model using Zirconia Toughened Alumina (ZTA) Turning Inserts: Taguchi Method and Regression Analysis

In the present study, an attempt has been made to apply the Taguchi parameter design method and regression analysis for optimizing the cutting conditions on surface finish while machining AISI 4340 steel with the help of the newly developed yttria based Zirconia Toughened Alumina (ZTA) inserts. These inserts are prepared through wet chemical co-precipitation route followed by powder metallurgy process. Experiments have been carried out based on an orthogonal array L9 with three parameters (cutting speed, depth of cut and feed rate) at three levels (low, medium and high). Based on the mean response and signal to noise ratio (SNR), the best optimal cutting condition has been arrived at A3B1C1 i.e. cutting speed is 420 m/min, depth of cut is 0.5 mm and feed rate is 0.12 m/min considering the condition smaller is the better approach. Analysis of Variance (ANOVA) is applied to find out the significance and percentage contribution of each parameter. The mathematical model of surface roughness has been developed using regression analysis as a function of the above mentioned independent variables. The predicted values from the developed model and experimental values are found to be very close to each other justifying the significance of the model. A confirmation run has been carried out with 95 % confidence level to verify the optimized result and the values obtained are within the prescribed limit.

Optimization of Surface Roughness during Nd:YAG Laser Micro-Turning Process of Alumina

Laser micro-turning process is one of the newly developed laser material processing technologies to generate micro-turned groove or surface on cylindrical workpiece. In the present paper, an attempt has been made to investigate the laser micro-turning operation on cylindrical shaped aluminium oxide (Al 2 O 3 ) ceramic to study the influence of different laser micro-turning process parameters namely, laser average power, pulse frequency, workpiece rotational speed, assist air pressure and Y feed rate using response surface methodology (RSM) experimental plan. The mathematical model for surface roughness (Ra) has been developed and the adequacy of the model has been tested through analysis of variance (ANOVA) results. Average power and pulse frequency are the most significant process parameters. Optimal parametric combination for minimum value of surface roughness has been achieved as 5.23μm at parametric setting of average power of 9.04 W, pulse frequency of 4108.18 Hz, workpiece rotational speed of 451.16 rpm, air pressure of 1.44 kgf/cm 2 and Y feed rate of 0.37 mm/s. Confirmation experimental results show a good agreement between RSM based optimal and experimental value of response criterion.

Experimental investigation into machinability of hardened AISI 4140 steel using TiN coated ceramic tool

The concerns with some machinability aspects on surface roughness, flank wear and chip morphology in hard turning of AISI 4140 steel using PVD-TiN coated Al2O3+TiCN mixed ceramic inserts under dry environment. The machined surface characterization, tool wear mechanism and chip morphology are investigated in this study, along with optimization and development of mathematical models for surface roughness and flank wear. By adopting combined techniques such as orthogonal array and analysis of variance, the consequences of cutting parameters (cutting speed, feed and depth of cut) on surface roughness (Ra, Rq and Rz) and flank wear (VB) are explored. The results show that feed is the principal cutting parameter influencing surface roughness, followed by cutting speed. However, flank wear is affected by the cutting speed and interaction of feed-depth of cut, although depth of cut has not been found statistical significant, but the flank wear is an increasing function of depth of cut. Thereafter, observations are made on the machined surface, worn tool and the generated chips by scanning electron microscope (SEM) to establish the process. Abrasion was the major wear mechanism found during hard turning within the studied range. Chip morphology indicates the formation saw-tooth/serrated chips at higher feed due to reduction of chip thickness, results in degradation of surface finish. Additionally, effect of tool wear on surface roughness has been studied. The experimental data were further analyzed to predict the optimal range of surface roughness and flank wear. Finally, based on response surface methodology (RSM) mathematical models are developed for surface roughness (Ra, Rq and Rz) and flank wear (VB) with 95% confidence level. Effectiveness, adequacy, statistical significance and validity, and fit of data of the developed model has been checked using ANOVA analysis (depending on P value, F value and R2 value), Anderson–Darling test and normal probability plot. The proposed experimental and statistical techniques initiate authentic methodologies to optimize, model and improve the hard turning process, which can be prolonged effectively to analyse other machining processes.

Mathematical Modeling of Surface Roughness of Castings Produced Using ZCast Direct Metal Casting

Aim of this investigation is to develop a mathematical model for predicting surface roughness of castings produced using ZCast process by employing Buckingham’s π-theorem. A relationship has been proposed between surface roughness of castings and shell wall thickness of the shell moulds fabricated using 3D printer. Based on model, experiments were performed to obtain the surface roughness of aluminium, brass and copper castings produced using ZCast process based on 3D printing technique. Based on experimental data, three best fitted third-degree polynomial equations have been established for predicting the surface roughness of castings. The predicted surface roughness values were then calculated using established best fitted equations. An error analysis was performed to compare the experimental and predicted data. The average prediction errors obtained for aluminium, brass and copper castings are 10.6, 2.43 and 3.12 % respectively. The obtained average surface roughness (experimental and predicted) values of castings produced are acceptable with the sand cast surface roughness values range (6.25–25 µm).

Investigation of surface roughness in micro-electro discharge machining of nonconductive ZrO2 for MEMS application

Micro-electro discharge machining technique, a noncontact machining process, is applied for drilling blind hole on nonconductive ZrO2 ceramic for MEMS application. A conductive layer of adhesive copper is applied on the workpiece surface to initiate the sparks. Kerosene is used as dielectric for creation of continuous conductive pyrolytic carbon layer on the machined surface. Experiments are conducted by varying the voltage (V), capacitance (C) and rotational speed (N). Correlating these variables a mathematical model for surface roughness (SR) is developed using Taguchi method. The results showed that the V and C are the significant parameters of SR in micro-EDM for nonconductive ZrO2 ceramic. The model also showed that SR increases with the increase of V and C.

Prediction of Surface Roughness in Turning of EN 353 Using Response Surface Methodology

In the present work, an attempt has been made to investigate the influence of cutting speed, feed, depth of cut and nose radius on surface roughness during wet turning of EN 353 steel using tungsten carbide inserts. Surface roughness prediction models in terms of speed, feed, depth of cut and nose radius is developed by using response surface methodology based on center composite rotatable design. A comparison of first order models with quadratic model was carried out on the basis of percentage mean absolute error and mean square error. The results clearly reveals that the predicted data using quadratic model is in close agreement to the experimental surface roughness values as compared to predicted data using first order model. In addition to this, it has been revealed that the speed is the main influencing factor affecting the surface roughness. The depth of cut has no significant influence on the roughness. Mathematical model for surface roughness shows that surface roughness decreases with increase in speed and nose radius, but increases with increase in feed. The percentage variation between the predicted and experimental values of the surface roughness during the confirmation experiments was found within 5 %. An attempt has also been made to obtain optimum cutting conditions for minimum surface roughness.

Optimization and Analysis for Surface Roughness of SiCp/Al Metal Matrix Composite by Micro-WEDM

The material removal process of SiC/Al particulate (SiCp/Al) metal matrix composite by micro-wire electrical discharge machining (micro-WEDM) is influenced by many factors interaction, which makes the mathematical model of surface roughness (SR) be difficult to obtain effectively. To solve this problem, experimental study method is conducted under the constraint conditions. In this paper, a central composite design (CCD) testing with 3-factor and 5-level is carried out and SiCp/Al metal matrix composite machining test scheme is designed, and then second-order relational model is established between SR and main power parameters (open-circuit voltage, capacitance, and pulse duration) by using response surface methodology. Through multiple quadratic fitting, the quadratic regression mathematical model of SR is obtained. Constrains of actual machining condition upon the parameters are analyzed further. With the goal of reducing SR of SiCp/Al metal matrix composite by micro-WEDM, the parameters optimization model is established. Particle swarm optimization (PSO) algorithm and its procedure are designed to solve the model. Test proves that the algorithm could achieve optimized process parameters which satisfy multiple constraints rapidly and effectively.

Optimization of ANN models using different optimization methods for improving CO2 laser cut quality characteristics

Determination of optimal laser cutting parameter settings for obtaining high cut quality in CO2 laser cutting process is of great importance. In this paper an attempt has been made to apply different optimization methods for determining of optimal values of laser power, cutting speed, assist gas pressure and focus position with the purpose of improving the cut quality characteristics obtained in the CO2 laser cutting of stainless steel. The laser cutting experiment was planned and conducted according to the Taguchi’s L27 orthogonal array and the experimental data were used for developing mathematical models for surface roughness, kerf width and width of heat affected zone based on artificial neural networks (ANNs). Mathematical models of the cut quality characteristics were developed using single hidden layer ANN trained with Levenberg–Marquardt algorithm. This paper compares the quality of solutions obtained when optimizing ANN models using the real coded genetic algorithm (RCGA), simulated annealing (SA) and recently developed improved harmony search algorithm (IHSA). The computer code was written in MATLAB to integrate the ANN-based process models and the RCGA, SA and IHSA algorithms. For the purpose of comparison, some performance criteria were used. The merits and the limitations of the selected optimization methods were discussed.