Surface Roughness In Face Milling Research Articles

Investigation of cutting force components and surface roughness in face milling with different cutting ratios

During the face milling process, the choice of technological parameters plays an important role. The two most important parameters are the feed and the depth of cut, the ratio of which is also called the cutting ratio. The main topic of the paper is to examine how different values of the chip ratio affect the components of the cutting force and the roughness of the machined surface. It is also shown how important it is to know the exact geometry of the cutting insert, as well as - in the case of cutting with multiple inserts - the knowledge of the run-out between the individual inserts. Based on the performed investigations, it was determined that the use of a chip ratio close to 1 is not recommended either in terms of cutting force components or surface roughness.

The effect and optimization of cutting parameters of Vanadis 4E powder metallurgical tool steel on tool wear: surface roughness in face milling

The effect and optimization of cutting parameters of Vanadis 4E powder metallurgical tool steel on tool wear: surface roughness in face milling

Comparative Analysis of Face Milling in Dry and Wet Condition of Al 8011 For Minimum Surface Roughness in Face Milling

Abstract: The quality of machined components is evaluated in respect of how closely they adhere to set product finish, and reflective properties. Dimensional accuracy, tool wear and quality of surface finish are three factors that manufacturers must be able to control at the machining operations to ensure better performance and service life of engineering component. Aluminum alloys are extensively used as a main engineering material in various industries such as automotive industries, the mould and the die components manufacture and the industry in which weight is the most important factor. The purpose of this research is to investigate the effect of the main factors of the surface roughness and flatness in aluminum 8011 face milling with dry and wet condition. This experimental investigation to be conducted by using VMC controlled milling machine with 30mm face milling cutter with carbide multiple inserts. The same parameters were executed during the operation only difference minimum quantity lubricant for one set of operation and another set of operation without lubricant. The experimental results were evaluated using Taguchi technique minimum surface finish obtained at medium speed and maximum feed and depth of cut 0.222 µm in the dry condition during the face milling operation. The minimum flatness was obtained 0.012 mm at wet condition. minimum surface finish obtained through dry condition. Optimal control factor and ANNOVA calculations were found for face milling operation of AA8011.

GRA based optimization of tool vibration and surface roughness in face milling of hardened steel alloy

In current study, an experimental research has been carried out for the enhancement in process variables during the machining of EN31 steel. Although the effect of the process parameters such as cutting speed, feed rate and depth of cut on responses like surface roughness has been presented by many researchers, still there is a scope of improvement in determining effect of these process variables using an appropriate multi-response optimization technique. Along with the machining parameters, cooling concentration in terms of minimum quantity lubrication (MQL) is also considered. The output responses viz. surface finish achieved during machining and the effect of tool vibration are evaluated. A comprehensive analysis between the input parameters and output responses by using hybrid optimization technique consisting of Taguchi based orthogonal array (TGRA) and grey relational analysis has been accomplished.

Cutting Forces and Surface Roughness in Face-Milling of SKD61 Hard Steel

Cutting Forces and Surface Roughness in Face-Milling of SKD61 Hard Steel

Models for Prediction of Surface Roughness in a Face Milling Process Using Triangular Inserts

Research was carried out to develop a mathematical model based on the cutting tool surface profile geometry to predict surface roughness in face milling. Previous models were derived using either the simple assumption of a perfectly round tool nose or statistical analysis based on a large number of experiments. In this research, three milling cases were defined based on the magnitude of the feed rate using a triangular insert with a round corner. In case 1, the machine marks only consisted of a series of arcs. In case 2, the machine marks included a series of an arc and one straight line. In case 3, the machine marks consisted of a series of an arc and two straight lines. Three different equations for surface roughness prediction were obtained based on each of the three cases. Experiments were done to validate the models, and the results showed that the mathematical models had good correlation with experimental results.

Optimizing Cutting Conditions for Minimum Surface Roughness in Face Milling of High Strength Steel Using Carbide Inserts

A full factorial design technique is used to investigate the effect of machining parameters, namely, spindle speed(N), depth of cut(ap),and table feed rate(Vf),on the obtained surface roughness (RaandRt) during face milling operation of high strength steel. A second-order regression model was built using least squares method depending on the factorial design results to approximate a mathematical relationship between the surface roughness and the studied process parameters. Analysis of variance was conducted to estimate the significance of each factor and interaction with respect to the surface roughness. ForRa, the results show that spindle speed, depth of cut, and table feed rate have a significant effect on the surface roughness in both linear and quadratic terms. There is also an interaction between depth of cut and feed rate. It also appears that feed rate has the greatest effect on the data variation followed by depth of cut. ForRt, the results show that the table feed rate is the most effective factor followed by the depth of cut, while the spindle speed had a significant small effect only in its quadratic term. The conditions of minimumRaandRtare identified through least square optimization. Moreover, multiobjective optimization for minimizingRaand maximizing metal removal rateQis conducted and the results are presented.

Comparison of Theoretical and Real Surface Roughness in Face Milling with Octagonal and Circular Inserts

Various modeling techniques have applied by researchers to predict values of surface roughness parameters in different metal cutting processes. Some examples of these methods are briefly introduced. After this an analytical model and investigation method are presented which were used to make predictions of the expected values of surface roughness parameters. Cutting experiments were performed to obtain real roughness data and to validate the proposed and realised model by face milling of 42CrMo4 specimens with two different insert shapes. Conditions and results of these experimental investigations are presented in the subsequent part of the paper, and finally the determined approximation relations are introduced that can be used to calculate the expected roughness values from theoretical data.

Modelling and Simulation of Surface Roughness in Face Milling

The paper presents a research on machined surface roughness in face milling of aluminium alloy on a low power cutting machine. Based on the results of the central-composite plan of experiment with varying machining parameters (number of revolutions - spindle speed n, feed rate f and depth of cut a) and the roughness observed as output variable, two models have been developed: a regression model and a model based on the application of neural networks (NN model). The regression model (coefficient of determination of 0.965 or 0.952 adjusted) with insignificant lack of fit, provides a very good fit and can be used to predict roughness throughout the region of experimentation. Likewise, the model based on the application of neural networks approximates well the experimental results with the level of RMS (Root Mean Square) error in the phase of validation of 4.01%.

Application of GONNS to predict constrained optimum surface roughness in face milling of high-silicon austenitic stainless steel

Surface roughness is a technical requirement for machined products and one of the main product quality specifications. In the present research, a genetically optimized neural network system (GONNS) is proposed for prediction of constrained optimal cutting conditions in face milling of a high-silicon austenitic stainless steel (UNS J93900) in order to minimize surface roughness. In order to attain minimum operation numbers and decrease the cost of machining, an experimental scheme was arranged by using Taguchi method. The considered parameters were cutting speed, feed, depth of cut, and engagement. Cutting force components and surface roughness were measured, and then analysis of variance is performed. The results show that the feed is the dominant factor affecting the surface roughness. Backpropagation artificial neural network was utilized to create predictive models of surface roughness and cutting forces exploiting the experimental data, and the genetic algorithm was employed to find the constrained optimum of surface roughness. Finally, in order to validate the method, an experiment with the obtained optimal cutting condition was carried out, and the results were compared with the predicted value of surface roughness. The corresponding results show the capability of GONNS to predict constrained surface roughness.

Optimum surface roughness prediction in face milling by using neural network and harmony search algorithm

This paper presents a new approach to determine the optimal cutting parameters leading to minimum surface roughness in face milling of X20Cr13 stainless steel by coupling artificial neural network (ANN) and harmony search algorithm (HS). In this regard, advantages of statistical experimental design technique, experimental measurements, analysis of variance, artificial neural network and harmony search algorithm were exploited in an integrated manner. To this end, numerous experiments on X20Cr13 stainless steel were conducted to obtain surface roughness values. A predictive model for surface roughness was created using a feed forward neural network exploiting experimental data. The optimization problem was solved by harmony search algorithm. Additional experiments were performed to validate optimum surface roughness value predicted by HS algorithm. The obtained results show that the harmony search algorithm coupled with feed forward neural network is an efficient and accurate method in approaching the global minimum of surface roughness in face milling.

Regression analysis, support vector machines, and Bayesian neural network approaches to modeling surface roughness in face milling

This study examines the influence of cutting speed, feed, and depth of cut on surface roughness in face milling process. Three different modeling methodologies, namely regression analysis (RA), support vector machines (SVM), and Bayesian neural network (BNN), have been applied to data experimentally determined by means of the design of experiment. The results obtained by the models have been compared. All three models have the relative prediction error below 8%. The best prediction of surface roughness shows BNN model with the average relative prediction error of 6.1%. The research has shown that, when the training dataset is small, both BNN and SVR modeling methodologies are comparable with RA methodology and, furthermore, they can even offer better results. Regarding the influence of the examined cutting parameters on the surface roughness, it has been shown that the feed has the largest affect on it and the depth of cut the least.

Influence of radial and axial runouts on surface roughness in face milling with round insert cutting tools

In face milling processes, the surface quality of the machined part depends on many factors, including feed, cutting tool geometry and tool errors. In this work, a numerical model for predicting the surface profile and surface roughness as a function of these factors is presented, incorporating a random values generation algorithm that makes it possible to determine the variation in surface roughness from the values that can be adopted by tool errors. This work is focused on round insert cutting tools and the influence of tool errors such as radial and axial runouts. The results that correspond to a number of teeth equal to 4, insert diameter of 12 mm, depth of cut of 0.5 mm, cutting speed of 120 m/min and feed of 0.4–1.4 mm/rev are analysed. Milling experiments are made to verify the validity of the model and the discrepancies between the experimental and theoretical surface profiles are assumed to be a consequence of different factors such as the variation in undeformed chip thickness along the surface profile.