Roughness Prediction Model Research Articles

An improved surface roughness prediction model using Box-Cox transformation with RSM in end milling of EN 353

In the present work, an attempt has been made to use Box-Cox transformation with response surface methodology to develop improve surface roughness prediction model in end milling of EN 353 steel using carbide inserts. The analysis has been carried out in two stages. In the first stage quadratic model has been developed in terms of feed, speed, depth of cut and nose radius using response surface methodology (RSM) based on center composite rotatable design (CCRD). The quadratic model, thus developed predicts the surface roughness with 92% accuracy. In the second stage, the improved quadratic model has been developed using Box-Cox transformation with RSM based on CCRD. The prediction ability of this develop model has been found more accurate (mean absolute error 4.7%) than previous one. An attempt has also been made to investigate the influence of cutting parameters on surface roughness. The result shows that the machining speed is the main influencing factor on the surface roughness while the depth of cut has no significant influence.

Cutting force-based adaptive neuro-fuzzy approach for accurate surface roughness prediction in end milling operation for intelligent machining

End milling is one of the most common metal removal operations encountered in industrial processes. Product quality is a critical issue as it plays a vital role in how products perform and is also a factor with great influence on manufacturing cost. Surface roughness usually serves as an indicator of product quality. During cutting, surface roughness measurement is impossible as the cutting tool is engaged with the workpiece, chip and cutting fluid. However, cutting force measurement is easier and could be used as an indirect parameter to predict surface roughness. In this research work, a correlation analysis was initially performed to determine the degree of association between cutting parameters (speed, feed rate, and depth of cut) and cutting force and surface roughness using adaptive neuro-fuzzy inference system (ANFIS) modeling. Furthermore, the cutting force values were employed to develop an ANFIS model for accurate surface roughness prediction in CNC end milling. This model provided good prediction accuracy (96.65 % average accuracy) of surface roughness, indicating that the ANFIS model can accurately predict surface roughness during cutting using the cutting force signal in the intelligent machining process to achieve the required product quality and productivity.

Study on Cutting Parameters Optimization of Ball-End Cutters Milling for 2A70 Aluminum Alloy

Based on experiments of ball-end cutters milling for 2A70 aluminum alloy, the prediction model of surface roughness for 2A70 aluminum alloy is established by using of regression analysis method of least square. Aiming at the actual milling problem in the enterprise the cutting parameters are optimized by using of optimization tool-box of MATLAB software, in the process of solving optimized parameters the machining efficiency is set as the objective function and surface roughness prediction model is set as the constraint condition. The optimized cutting parameters can greatly improve the machining efficiency in the premise of ensuring the quality of machined surface, and it provides the important theory evidence and case reference for NC machining enterprises to reduce production costs.

Investigation into a geometry-based model for surface roughness prediction in vibratory finishing processes

The machining process vibratory finishing is used to improve the topology of workpiece surfaces. Typically, surface roughness improves until a steady-state is reached. In this state, material will be removed but roughness remains relatively stable. Consequently, the focus of this paper is set on the so-called transient period of vibratory finishing. A new approach is presented to predict the roughness change after a given process time. In contrast to past approaches concentrating on mass or diameter loss of the workpiece, the model is based on geometric changes of the roughness-profile during the transient period of vibratory finishing. This model can be used to estimate process times needed to achieve a desired roughness of a workpiece.

Prediction of Surface Roughness Based on Least Square Support Vector Machine in Low-Frequency Vibration Cutting Process

A prediction method based on least square support vector machine is introduced into the surface roughness prediction model in low-frequency vibration cutting. The model is created with low-frequency vibration cutting experiment for the corresponding relationship between vibration parameters and cutting parameters and the workpiece surface roughness. The training sample set is constructed to train regression models of least square support vector machine through experimental data. Identification of training sample set is done to gain the regression parametersaandb. The amplitude ofA, vibration frequencyf, feedf1and spindle speednare used as the input variable in Xi. Predicted values of surface roughness are forecasted with the model. Evaluation is made with the difference between the predicted value and experiment. Comparison with BP neural network and support vector machine method has shown that the least square support vector machine prediction model works faster than SVM method, the prediction error is about 29% of that by support vector machine, and the prediction accuracy is higher than the BP model.

An investigation on surface finishing in ultra-precision raster milling of aluminum alloy 6061

The technology of ultra-precision machining with single crystal diamond tool produces advanced components with a higher dimensional accuracy and a better surface quality. This article develops a three-dimensional surface topography simulation model for ultra-precision raster milling process by considering the effect of cutting parameters, tool geometry and tool interference as well as the tool–workpiece relative movement on surface generation. Based on the developed surface topography simulation model, a prediction model for the surface roughness in ultra-precision raster milling is built. The effects of depth of cut and surface topography on the cutting-induced heat generation in ultra-precision milling of aluminum alloy 6061 are investigated. Experiment is conducted to verify the developed surface topography simulation model by raster milling of aluminum alloy 6061 under different feed rates and depths of cut. The experimental results show that the surface topography simulation model can properly simulate the surface profile in the raster milling process and the predicted surface roughness agrees well with the measured results of the machined workpiece. Heat generation in horizontal cutting is less than that in vertical cutting and a larger depth of cut generates more heat on the machined workpiece. The cutting-induced heat generates precipitate on the machined aluminum alloy 6061 which results in a worse surface finish in ultra-precision raster milling.

A geometrical model for surface roughness prediction when face milling Al 7075-T7351 with square insert tools

Surface quality is important in engineering and a vital aspect of it is surface roughness, since it plays an important role in wear resistance, ductility, tensile, and fatigue strength for machined parts. This paper reports on a research study on the development of a geometrical model for surface roughness prediction when face milling with square inserts. The model is based on a geometrical analysis of the recreation of the tool trail left on the machined surface. The model has been validated with experimental data obtained for high speed milling of aluminum alloy (Al 7075-T7351) when using a wide range of cutting speed, feed per tooth, axial depth of cut and different values of tool nose radius (0.8mm and 2.5mm), using the Taguchi method as the design of experiments. The experimental roughness was obtained by measuring the surface roughness of the milled surfaces with a non-contact profilometer. The developed model can be used for any combination of material workpiece and tool, when tool flank wear is not considered and is suitable for using any tool diameter with any number of teeth and tool nose radius. The results show that the developed model achieved an excellent performance with almost 98% accuracy in terms of predicting the surface roughness when compared to the experimental data.

Research on Surface Roughness in High Speed Face Milling Part 2: Experimental Validation of Prediction Model

The paper presents rigorous experimental validation results of the algorithm to predict work-piece surface roughness in face milling operation as developed in the Part 1. The experimental verification system consisting of various devices was established according to the given experimental conditions. Experimental parameters are set for steady face milling. Experimental data are collected through four experiments. These experimental data include the axial and radial run-out errors of each square insert with flat edge, the modal parameters of the face milling system, the Z-axial milling force and the measured surface contour of the milled work-piece. The trajectory of cutting teeth is calculated by the MATLAB software based on the static surface roughness model. Z-axial dynamic relative displacement between the tooth and the work-piece is obtained as the predicted dynamic surface roughness. By integrating the prediction results of static and dynamic models, the surface contour is predicted. Predicted and measured results are compared in the same figure and basically consistent. The work-piece surface roughness prediction model will be useful and valid in high-speed face milling.

Surface roughness prediction and parameters optimization in grinding and polishing process for IBR of aero-engine

In order to improve surface quality and reduce surface roughness of integrally bladed rotors (IBRs), the optimizations of grinding and polishing process parameters, such as abrasive size, contact force, belt linear velocity, and feed rate, are carried out in this study. Firstly, the optimal range of each factor is obtained by single-factor experiment, and a surface roughness prediction model is generated according to the central composite design experiments and quadratic regression design technology. Secondly, the optimal level of each factor is confirmed through the surface roughness prediction model, and the response surface of the optimal level of each factor is drawn using Matlab. Then, the optimum process parameters are determined by analyzing the response surface. Finally, the optimum process parameters are selected in the grinding and polishing experiments for IBR. The experimental results show that grinding and polishing with the optimum parameters obtained in this study enhances the machining quality significantly. The surface roughness of IBR improved greatly (nearly 25 %) compared with grinding using normal parameters.

Experimental Prediction Model for Roughness in the Turning of UNS A97075 Alloys

In this work, a study of the influence of the cutting parameters on superficial quality of dry-turned UNS A97075 test bars has been carried out. The superficial quality has been evaluated trough the arithmetical average roughness,Ra. In addition, the evolution ofRaas a function of the axial machining length has been analyzed. In order to do this, a set of machining tests has been performed under different combinations of cutting speed and feed. The experimental data have revealed a high sensitivity to change ofRawith feed, whereas this sensitivity is lower with cutting speed. On the other hand, a tendency to decreaseRawith the axial machining length has been found. Finally, an experimental prediction model forRahas been developed. This model allows predicting the value ofRaas a function of the cutting parameters and the machining time.

Research on Prediction Method of Profile Grinding Surface Roughness Based on PSO-LS SVM Algorithm

In order to improve the prediction accuracy of the grinding surface roughness, this paper presents a prediction algorithm based on Least Squares Support Vector Machine with Particle Swarm Optimization (PSO-LS SVM). Chose the radial basis function as the kernel function of this new algorithm in where the regularization parameter and kernel function parameter of LS SVM were optimized by PSO, then the grinding surface roughness prediction model was established with the parameters optimized before. Through experimental analysis, it can be realized that compared to the conventional prediction methods, the prediction results of the proposed algorithm are closer to the actual roughness and the relative errors are smaller. As a result, the proposed algorithm is more acceptable.

The Prediction of Surface Roughness of Parallel Machine Tools Based on the Neural Network

Because the parallel machine tool have high speed, high precision, high rigidity and the mechanical structure is simple, the parallel machine tool have been used in many field widely. In the research and application of parallel machine, the effect of processing parameter on machine processing is the most important problem. In the paper the improved artificial neural network is applied to the roughness prediction model of parallel machine tools, the model predict the roughness when the feed rate, spindle speed, processing angle and machining force are changed effectively.

Optimization of surface roughness in turning of GFRP composites using genetic algorithm

Glass fiber reinforced polymer composites are finding its increased applications in variety of engineering applications such as aerospace, automobile, electronics and other industries. However, the users of FRP are facing difficulties to machine it, because of fiber delamination, fiber pull out, short tool life, matrix debonding and formation of powder like chips. The present investigation focuses on the optimization of process parameters for surface roughness of glass fiber reinforced polymer (GFRP) composites using Genetic Algorithm (GA). Experiments are conducted based on the established Taguchi’s L25 orthogonal array in Design of Experiments (DOE) on an all-geared lathe using poly-crystalline diamond (PCD) tool. The process parameters considered were cutting speed, feed, depth of cut, and work piece (fiber orientation angle). A second order mathematical model was developed for surface roughness prediction using Response Surface Methodology (RSM). An attempt has also been made to optimize the surface roughness prediction model coupling with Genetic Algorithm (GA) to optimize the objective function. Validation of the optimized results was also performed by confirmation experiments.Keywords: GFRP composites, Response surface methodology (RSM), Poly crystalline diamond (PCD), surface roughness (Ra), Genetic Algorithm (GA).International Journal of Engineering, Science and Technology Vol. 6, No. 1, 2014, pp. 49-57

Investigation of Material Removal and Surface Topography Formation in Vibratory Finishing

In this work investigations on vibratory finishing and an approach towards a process model combining the Discrete Element Method (DEM) with experimental results will be presented. Based on experimental data from vibratory finishing and drag finishing experiments, material removal is considered on a surface roughness level, leading to a new model for surface roughness prediction. The influence of several abrasive particles and process parameters on the surface topography formation was investigated using steel rods with different topographies. Core findings include differing material removal mechanisms depending on initial surface roughness and a first description of the influence of particle shape on surface topography formation. To develop a comprehensive process model for vibratory finishing, DEM is used to model the motion of the bulk of abrasive particles and its contacts with the workpiece. Given the results from the simulation such as number, intensity and location of contacts for a set of boundary conditions, such as workpiece speed and bowl excitation, experimental findings can be linked to these computable results.

Surface Roughness Optimization of Polyamide-6/Nanoclay Nanocomposites Using Artificial Neural Network: Genetic Algorithm Approach

During the past decade, polymer nanocomposites attracted considerable investment in research and development worldwide. One of the key factors that affect the quality of polymer nanocomposite products in machining is surface roughness. To obtain high quality products and reduce machining costs it is very important to determine the optimal machining conditions so as to achieve enhanced machining performance. The objective of this paper is to develop a predictive model using a combined design of experiments and artificial intelligence approach for optimization of surface roughness in milling of polyamide-6 (PA-6) nanocomposites. A surface roughness predictive model was developed in terms of milling parameters (spindle speed and feed rate) and nanoclay (NC) content using artificial neural network (ANN). As the present study deals with relatively small number of data obtained from full factorial design, application of genetic algorithm (GA) for ANN training is thought to be an appropriate approach for the purpose of developing accurate and robust ANN model. In the optimization phase, a GA is considered in conjunction with the explicit nonlinear function derived from the ANN to determine the optimal milling parameters for minimization of surface roughness for each PA-6 nanocomposite.

Validation of a prediction model for tangent rail roughness and noise growth

Railways can inevitably cause railway rolling noise, which is induced by both wheel and rail roughness. Due to the deformation and wear between the wheel/rail at the contact patch, the rail roughness may grow in amplitude after a number of wheelset passages. This results in increasing railway rolling noise. Rail roughness is a common aspect in all transit systems and much research has been pursued to predict and mitigate its growth on track but prediction of resultant noise growth has not been a focus. This paper provides the experimental validation of the modified Railway Rolling Noise Prediction Software (RRNPS) model for the prediction of rail roughness growth and corresponding noise growth. The model is a new framework to enable noise growth predictions due to rail roughness growth mechanics. It is validated by means of several experiments that have been performed along a straight railway line. Through comparisons between predictions and measurements, it is shown that the RRNPS model gives reliable predictions on rail roughness growth and corresponding noise growth. Subsequently this model is used to predict how speed, normal force, wheelset traffic and ballast vertical stiffness affect rail roughness growth and corresponding noise growth.

Abrasive flow machining applied to plastic gear matrix polishing

The topic of this paper is the application of abrasive flow machining (AFM) to gear tool inserts polishing. Polished surface on plastic gear teeth improves surface geometry stability, and it increases the lifespan, which was proved on the gear testing rig. Experiments have shown that it is an efficient alternative to the hand polishing procedure. Besides significant cost and processing time savings, AFM generates constant surface quality. The achieved roughness is homogeneous on the entire machined surface; it is reduced from Ra = 0.68 μm to Ra = 0.08 μm in 120 s. At the same time, the tooth geometry profile is not damaged. The first time, surface polishing should be done at request because of individually manufactured tool inserts. Processing parameters depend on the type of the abrasive machine, the polishing paste and part geometry. Computer-aided abrasive flow analyses and practical experiments assist in setting optimum AFM process parameters. The paper presents a working set of parameters and a detailed report on machined surface measurement data. On the base of better understanding of AFM process, the surface roughness prediction model and thickness of removed material model was setup. It has high accuracy and reliability for specific applications. The use of plastic gears for various applications is widespread; the presented process improvement is an important innovation for injection molding tools manufacturers.

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.

Modeling and evaluating of surface roughness prediction in micro-grinding on soda-lime glass considering tool characterization

The current research of micro-grinding mainly focuses on the optimal processing technology for different materials. However, the material removal mechanism in micro-grinding is the base of achieving high quality processing surface. Therefore, a novel method for predicting surface roughness in micro-grinding of hard brittle materials considering micro-grinding tool grains protrusion topography is proposed in this paper. The differences of material removal mechanism between convention grinding process and micro-grinding process are analyzed. Topography characterization has been done on micro-grinding tools which are fabricated by electroplating. Models of grain density generation and grain interval are built, and new predicting model of micro-grinding surface roughness is developed. In order to verify the precision and application effect of the surface roughness prediction model proposed, a micro-grinding orthogonally experiment on soda-lime glass is designed and conducted. A series of micro-machining surfaces which are 78 nm to 0.98 μm roughness of brittle material is achieved. It is found that experimental roughness results and the predicting roughness data have an evident coincidence, and the component variable of describing the size effects in predicting model is calculated to be 1.5×107 by reverse method based on the experimental results. The proposed model builds a set of distribution to consider grains distribution densities in different protrusion heights. Finally, the characterization of micro-grinding tools which are used in the experiment has been done based on the distribution set. It is concluded that there is a significant coincidence between surface prediction data from the proposed model and measurements from experiment results. Therefore, the effectiveness of the model is demonstrated. This paper proposes a novel method for predicting surface roughness in micro-grinding of hard brittle materials considering micro-grinding tool grains protrusion topography, which would provide significant research theory and experimental reference of material removal mechanism in micro-grinding of soda-lime glass.

Surface roughness (Ra) prediction model for turning of AISI 1019 steel using response surface methodology and Box–Cox transformation

In this present work, an attempt has been made to develop a more accurate surface roughness prediction model using response surface methodology based on center composite rotatable design with Box–Cox transformation in turning of AISI 1019 steel. The analysis has been carried out in three stages. In the first stage, a quadratic model has been developed in terms of feed, speed, depth of cut and nose radius. In the second stage, an improved prediction model has been developed by improving the normality, linearity and homogeneity of the data using a Box–Cox transformation. This improved model has been found to yield good prediction accuracy when compared to the previous one. In the third stage, confirmation experiments have been carried out, which clearly show that the Box–Cox transformation has a strong potential to improve the prediction capability of empirical models. An attempt has also been made to investigate the influence of cutting parameters on surface roughness. The result shows that the feed is the main influencing factor on the surface roughness while the depth of cut has no significant influence.