Surface roughness modelling in hard turning operation of AISI 4140 using CBN cutting tool

In this study, the influence of hardness (H) and spindle speed (N) on surface roughness (Ra) in hard turning operation of AISI 4140 using CBN cutting tool has been studied. A multiple regression analysis using analysis of variance is conducted to determine the performance of experimental values and to show the effect of hardness and spindle speed on the surface roughness. Artificial neural network (ANN) and regression methods have been used for modelling of surface roughness in hard turning operation of AISI 4140 using CBN cutting tool. The input parameters are selected to be as hardness and spindle speed and the output is the surface roughness. Regression and artificial neural network optimum models have been presented for predicting surface roughness. The predicted surface roughness by the employed models has been compared with the experimental data which shows the preference of ANN in prediction of surface roughness during hard turning operation. Finally, a reverse ANN model is constructed to estimate the hardness and spindle speed from surface roughness values. The results indicate that the reverse ANN model can predict hardness for the train data and spindle speed for the test data with a good accuracy but the predicted spindle speed for the train data and the predicted hardness for the test data don’t have acceptable accuracy.     

Surface Roughness Analysis in Machining of Titanium Alloy

The use of response surface methodology for minimizing the surface roughness in machining titanium alloy, a topic of current interest, has been discussed in this article. The surface roughness model has been developed in terms of cutting parameters such as cutting speed, feed, and depth of cut. Machining tests have been carried out using CVD (TiN-TiCN-Al₂O₃-TiN) coated carbide insert under different cutting conditions using Taguchi's orthogonal array. The experimental results have been investigated using analysis of variance (ANOVA). The results indicated that the feed rate is the main influencing factor on surface roughness. Surface roughness increased with increasing feed rate, but decreased with increasing cutting speed and depth of cut. The predicted results are fairly close to experimental values and hence, the developed models can be used for prediction satisfactorily.     

Studies on Machining Parameters While Milling Particle Reinforced Hybrid (Al6061/Al2O3/Gr) MMC

In this article, response surface methodology has been used for finding the optimal machining parameters values for cutting force, surface roughness, and tool wear while milling aluminum hybrid composites. In order to perform the experiment, various machining parameters such as feed, cutting speed, depth of cut, and weight (wt) fraction of alumina (Al₂O₃) were planned based on face-centered, central composite design. Stir casting method is used to fabricate the composites with various wt fractions (5%, 10%, and 15%) of Al₂O₃. The multiple regression analysis is used to develop mathematical models, and the models are tested using analysis of variance (ANOVA). Evaluation on the effects and interactions of the machining parameters on the cutting force, surface roughness, and tool wear was carried out using ANOVA. The developed models were used for multiple-response optimization by desirability function approach to determine the optimum machining parameters. The optimum machining parameters obtained from the experimental results showed that lower cutting force, surface roughness, and tool wear can be obtained by employing the combination of higher cutting speed, low feed, lower depth of cut, and higher wt fraction of alumina when face milling hybrid composites using polycrystalline diamond insert.     

Development of ANFIS model and machinability study on dry turning of cryo-treated PH stainless steel with various inserts

This present investigation deals about the machinability comparison of cryogenically treated 15-5 PH stainless steel with various cutting tools such as uncoated tungsten carbide, cryogenic-treated tungsten carbide and wiper geometry inserts. Cryo-treated PH stainless steel is considered as the work material in this investigation and experimental trials were performed under dry turning condition. The machinability aspects considered for evaluation are cutting force (F_z), surface roughness (R_a) and tool wear. The outcomes of experimentation reveal that the tungsten carbide inserts which are cryogenically treated provide improved performance in machining while comparing with conventional and wiper geometry inserts at all machining conditions. The measured cutting force and the observed flank wear were less for the cryo-treated inserts. However, wiper tool produces a better surface finish during machining. An artificial intelligence decision-making tool named Adaptive Neuro Fuzzy Inference System has been evolved to determine the relation among the considered input machining variables and output measures, namely cutting force and surface roughness of the machined surface. An analysis has been performed to compare the results obtained from developed models and experimental results.     

Prediction and optimisation models for turning operations

Selection of process parameters has very significant impact on product quality, production costs and production times. The quality and cost are much related to tool life, surface roughness and cutting forces which they are functions of process parameters (cutting speed, feed rate, depth of cut and tool nose radius). In this paper, empirical models for tool life, surface roughness and cutting force are developed for turning operations. The process parameters (cutting speed, feed rate, depth of cut and tool nose radius) are used as inputs to the developed machineability models. Two data mining techniques are used; response surface methodology and neural networks. The data of 28 experiments have been used to generate, compare and evaluate the proposed models of tool life, cutting force and surface roughness for the selected tool/material combination. The resulting models are utilized to formulate an optimisation model and solved to find optimal process parameters, when the objective is minimising production cost per workpiece, taking into account the related boundaries and limitation of this multi-pass turning operations. Numerical examples are given to demonstrate the suggested optimisation models.     

Correlation between edge radius of the cBN cutting tool and surface quality in hard turning

cBN cutting tools with superior mechanical properties are widely used in machining various hard materials. The microgeometry of cBN cutting tools, such as the edge radius, has great influence on the surface quality of components and tool life. For optimized tool geometry, it is crucial to understand the influence of the cBN cutting tool microgeometry on the machined surface quality. In this study, the attempt has been made to investigate the correlation between the cutting tool edge radius and surface quality in terms of the surface roughness and subsurface deformation through a FE simulation and experiment. Machining tests under different machining conditions were also conducted and the surface roughness and subsurface deformation were measured. Surface roughness and subsurface deformation were produced by the cutting tools with different edge radii under various cutting parameters. Both results from the FE simulation and machining tests confirmed that there was a significant influence on the surface quality in terms of both the surface roughness and subsurface quality from the edge radius. There is a critical edge radius ofcBN tools in hard turning in terms of surface quality generated.     

Investigating the Tool Life, Cutting Force Components, and Surface Roughness of AISI 302 Stainless Steel Material Under Oblique Machining

The aim of this work is to investigate the machinability of austenitic AISI 302 stainless steel under oblique cutting. This can be achieved by studying the cutting forces, analysis of tool life, and investigation of the surface roughness at different cutting conditions and nose radius. A factorial experiment and analysis of variance technique are used in which several factors are evaluated for their effects on each level. The machinability experiments are based on design of experiments to obtain empirical equations for machinability values for machining conditions such as speed, feed, depth of cut, and nose radius. The parameters considered in the experiments were optimized to attain maximum tool life using a response graph and a response table. Based on the response models, dual response contours (tool life and surface roughness as a response and metal removal rate) have been plotted in cutting speed-feed planes. Evaluating the effect of the predominant variables influencing the value of tool life is very important for improving the machined product quality.     

Optimization for Turning of Al-6061-SiC-Gr Hybrid Nanocomposites Using Response Surface Methodologies

Metal matrix composites have cemented their applicability in industrial sector by virtue of their excellent mechanical properties. However, work has largely been done on the studies related to macro/microsize particles. This work has been aimed to evaluate the influence of input parameters in turning of Al-6061-SiC-Gr hybrid nanocomposites. This article evaluates the effect of process parameters on the cutting force and average roughness of the machined surface in turning of Al-6061-SiC-Gr nanocomposites. The experiments were designed using CCD, and cutting force and roughness were evaluated using response surface methodology. Statistical models were generated. The results of the study indicated that feed rate and depth of cut are the major influencing factors in descending order for the cutting force. The analysis of surface roughness revealed that both these factors are having identical effect. The cutting speed had little effect on cutting force and an improvement is seen in surface finish. The experiments also revealed that tool wear is negligible for nanocomposites. The software-predicted values and the experimentally obtained values of the responses were acceptably close to each other with an error percentage of less than 5%. Using response surface optimization, optimal combinations of machining parameters are also obtained.     

Process Optimization of Finish Turning of Hardened Steels

Process planning and optimization is crucial to help establish the economic and quality viability of hard turning processes with the presence of a wide spectrum of tooling and process parameters. A systematic methodology is discussed in this paper to design the optimal tool geometry and cutting conditions for hard turning, incorporating the consideration of part finish, tool wear, and material removal rate. Experimental demonstration of the optimization scheme is presented at two levels: the first level is to validate the process prediction results and the second is to validate the optimization results. Hardened AISI 1053 steel was selected as the workpiece material in this study and its material property related parameters, including the Johnson-Cook constants and wear coefficients, were determined based on the machining tests. It is seen that the cutting force and tool wear progression agrees well with the predictions from 3-D oblique cutting model, and the machined surface roughness can be predicted with a surface kinematic model incorporating the plowing effect. The experimental results also showed that the process configuration as derived from the analytical optimization procedure lends itself to superior results in comparison to other experimental results under non-optimal configurations.     

超精密车削表面粗糙度预测模型的建立

介绍了一种利用回归分析法来建立单点金刚石刀具超精车削表面粗糙度预测模型的新方法，并通过建立的粗糙度预测模型，研究了铝合金超精密车削过程中切削速度，进给量和切削深度等参数对表面粗糙度的影响。通过实验分析表明：二次预测方程比一次预测方程更有效，而且适用范围比一次模型大。利用优化设计中的约束变尺度法对所建立的表面粗糙度预测方程进行了优化，可以实现对切削参数的优选，从而达到加工前在特定的条件下预测和控制表     

Fuzzy Modeling and Analysis of Machining Parameters in Machining Titanium Alloy

Titanium alloys are utilized in many engineering fields such as chemical, industrial, marine, and aerospace due to their unique properties. Machining of these materials causes severe problems. At high temperatures, they become chemically active and tend to react with tool materials. In the present study, fuzzy logic (a tool in artificial intelligence) is used for the prediction of cutting parameters in turning titanium alloy (Ti-6Al-4V). The parameters considered in this study are cutting speed, feed, and the depth of cut. Fuzzy rule-based modeling is employed for prediction of tool flank wear, surface roughness, and specific cutting pressure in machining of titanium alloy. These models can be effectively used to predict the tool flank wear, surface roughness, and specific cutting pressure in machining of titanium alloys. Analysis of the influences of the individual important machining parameters on the responses have been carried out and presented in this study.     

Optimal cutting conditions in turning of particulate metal matrix composites based on experiment and a genetic search model

The results of an experimental study concerned with the evolution of cutting forces, tool wear and surface roughness, as functions of time when turning the particulate metal matrix composite A356/20/SiCp-T6 are presented. Inserts with polycrystalline diamond (PCD) were tested. Cutting forces were measured using a piezoelectric dynamometer. The wear type was identified and its evolution with cutting time was measured. To model the phenomena a hybrid technique based on an evolutionary search over the design space defined by the experimental results is considered. Optimal cutting conditions are searched using a genetic algorithm based on an elitist strategy. The chromosomes composed by random keys represent cutting conditions defined according to a temporal scale.     

Finite element analysis of bending occurring while cutting with high speed steel lathe cutting tools

The bending which occurs on a cutting tool during machining on a lathe affects tool life, surface roughness and dimension correctness. In this research, the bending which has been calculated by Castigliano theorem has been compared with the bending obtained by finite element method. Under the constant cutting conditions, material Ç1060 has been machined with high speed steel (HSS) lathe cutting tools having 60°, 75° and 90° of cutting edge angle. It was determined by using ANSYS finite elements program that the bending of the cutting tool generated by the forces, which varied between 1360 N and 1325 N and occurred during cutting, varied between 0.039958 and 0.04373 mm. According to the results, it has been observed that the bending that was calculated by Castigliano theorem and that varied between 0.03542 and 0.034505 mm was almost the same with the bending determined by finite elements method. In other words, it was seen that the calculated values approach to the analysed results up to 0.4% .     

Study of Factors Affecting the Surface Quality in Ultra-Precision Diamond Turning

This paper deals with an investigation of the process factors and the material factors affecting the surface roughness in ultra-precision diamond turning. The process factors involve cutting conditions, tool geometry, and relative tool-work vibration which are related to the cutting geometry and the dynamic characteristics of the cutting process. The material factors considered are material anisotropy, swelling, and crystallographic orientation of the work materials. Experimental results indicate that the influence due to the process factors can be minimized through a proper selection of operational settings and better control of dynamic characteristics of the machine. The material factors, on the other hand, exert consistent influence on the surface roughness which can not be minimized solely by an optimization of process parameters and machine design. Based on these findings, some suggestions are proposed for the optimization of the surface quality in ultra-precision diamond turning.     

Experimental investigation of cutting parameters in machining of 100Cr6 with tangential turn-milling method

The turn-milling methods for machining operation have been developed to increase efficiency of conventional machines recently. These methods are used especially by coupling some apparatuses on the computer numerical control (CNC) machine to decrease the production time and machine costs, ensure the maximum production and increase the quality of machining. In this study, 100Cr6 bearing steel extensively used in industry has been machined by tangential turn-milling method. This paper presents an approach for optimization of the effects of the cutting parameters including cutter speed, workpiece speed, axial feed rate, and depth of cut on the surface roughness in the machining of 100Cr6 steel with tangential turn-milling method by using genetic algorithm (GA). Tangential turning-milling method has been determined to have optimum effects of cutting parameters on the machining of 100Cr6 steel. The experimental results show that the surface roughness quality is close to that of grinding process.     

Modelling and optimization of turning processes for slender parts

In this paper, an abductive network is adopted in order to construct a prediction model for surface roughness and error-of-roundness in the turning operation of slender parts. The abductive network is composed of a number of functional nodes. These functional nodes are self-organized to form an optimal network architecture by using a predicted square error (PSE) criterion. Once the process parameters (workpiece length L, spindle speed n, feed rate f and depth of cut t) are given, the surface roughness and error-of-roundness can be predicted by this developed network. To verify the feasibility of the abductive network, regression analysis has been adopted to develop a second prediction model for surface roughness and error-of-roundness. Comparison of the two models indicates that the prediction model developed by the abductive network is more accurate than regression analysis. It can be found that the use of the abductive network for surface roughness and error-of-roundness is feasible. A simulated annealing optimization algorithm with a performance index is then applied to the developed network for searching the optimal process parameters. The optimal cutting condition can be obtained with the object of maximizing the metal removal rate and minimizing the surface roughness and error-of-roundness to the lowest/smallest extent permissible.     

Modeling of Residual Stress Profile in Finish Hard Turning

Mechanical components shaped by hard turning processes are commonly used under high stress and repeated loading conditions. The physical strength and fatigue life of these components is known to be significantly affected by the residual stress distributions induced by finish hard turning. A thorough understanding of the residual stress profile including both magnitude and direction along the depth of the hard turned workpiece is therefore very important to maximize component life and improve its performance. Many studies have been conducted to determine the effect of cutting tool geometry, cutting parameters, and workpiece material on residual stress distribution in hard turning. However, due to the complexity of hard turning processes, the effect factors considered in these studies are typically insufficient. Knowledge of the most important effect factors such as cooling type, insert grade and tool geometry, tool wear, cutting conditions, and workpiece material has been limited. Very few analytical models are available and accurate enough to predict residual stress profiles in hard turning. In this paper, a series of experiments were designed based on the factorial robust engineering method to explore the full factors affecting the residual stress profiles, and an intelligent model based on back-propagation neural network (BPNN) was developed to predict circumferential and longitudinal residual stress profiles in hard turning. The prediction results match the experimental results well, and much higher performance relative to conventional linear regression method has been achieved.     

Surface roughness predictive modeling: neural networks versus regression

Surface roughness plays an important role in product quality and manufacturing process planning. This research focuses on developing an empirical model for surface roughness prediction in finish turning. The model considers the following working parameters: work-piece hardness (material), feed, cutter nose radius, spindle speed and depth of cut. Two competing data mining techniques, nonlinear regression analysis and computational neural networks, are applied in developing the empirical models. The values of surface roughness predicted by these models are then compared with those from some of the representative models in the literature. Metal cutting experiments and tests of hypothesis demonstrate that the models developed in this research have a satisfactory goodness of fit. It has also presented a rigorous procedure for model validation and model comparison. In addition, some future research directions are outlined.     

Formulation of Surface Roughness Models for Machining Nickel Super Alloy with Different Tools

This paper presents a study of the development of surface roughness models for turning supermet 718 nickel super alloy (300 BHN), using different tool materials namely; CBN (SANDVIK CB50), Carbide (SANDVIK HIP k10), and ceramic (SANDVIK CC680) under dry cutting conditions and a constant nose radius. The models are developed in terms of cutting speed, feed rate, and depth of cut. These variables were investigated using design of experiments and utilization of the response surface methodology (RSM). A separate surface roughness model corresponding to each tool material is established, tested and reported.     

Prediction of cutting force based on machining parameters on AL7075-T6 aluminum alloy by response surface methodology in end milling

Cutting forces modeling is the basic to understand the cutting process, which should be kept in minimum to reduce tool deflection, vibration, tool wear and optimize the process parameters in order to obtain a high quality product within minimum machining time. In this paper a statistical model has been developed to predict cutting force in terms of geometrical parameters such as rake angle, nose radius of cutting tool and machining parameters such as cutting speed, cutting feed and axial depth of cut. Response surface methodology experimental design was employed for conducting experiments. The work piece material is Aluminum (Al 7075-T6) and the tool used is high speed steel end mill cutter with different tool geometry. The cutting forces are measured using three axis milling tool dynamometer. The second order mathematical model in terms of machining parameters is developed for predicting cutting forces. The adequacy of the model is checked by employing ANOVA. The direct effect of the process parameter with cutting forces are analyzed, which helps to select process parameter in order to keep cutting forces minimum, which ensures the stability of end milling process. The study observed that feed rate has the highest statistical and physical influence on cutting force.