Modeling and optimization of burr height in drilling of Al-7075 using Taguchi method and response surface methodology

This investigation presents the use of Taguchi and response surface methodologies for minimizing the burr height and the surface roughness in drilling Al-7075. The Taguchi method, a powerful tool to design optimization for quality, is used to find optimal cutting parameters. Response surface methodology is useful for modeling and analyzing engineering problems. The purpose of this paper was to investigate the influence of cutting parameters, such as cutting speed and feed rate, and point angle on burr height and surface roughness produced when drilling Al-7075. A plan of experiments, based on L₂₇ Taguchi design method, was performed drilling with cutting parameters in Al-7075. All tests were run without coolant at cutting speeds of 4, 12, and 20 m/min and feed rates of 0.1, 0.2, and 0.3 mm/rev and point angle of 90^°, 118°, and 135°. The orthogonal array, signal-to-noise ratio, and analysis of variance (ANOVA) were employed to investigate the optimal drilling parameters of Al-7075. From the analysis of means and ANOVA, the optimal combination levels and the significant drilling parameters on burr height and surface roughness were obtained. The optimization results showed that the combination of low cutting speed, low feed rate, and high point angle is necessary to minimize burr height. The best results of the surface roughness were obtained at lower cutting speed and feed rates while at higher point angle. The predicted values and measured values are quite close to each other; therefore, this result indicates that the developed models can be effectively used to predict the burr height and the surface roughness on drilling of Al-7075.     

Investigation of cutting parameters effect for minimization of sur face roughness in internal turning

Minimizing the surface roughness is one of the primary objectives in most of the machining operations in general and in internal turning in particular. Poor control on the cutting parameters due to long boring bar generates non conforming parts which results in increase in cost and loss of productivity due to rework or scrap. In this study, the Taguchi method is used to minimize the surface roughness by investigating the rake angle effect on surface roughness in boring performed on a CNC lathe. The control parameters included besides tool rake angle were insert nose radius, cutting speed, depth of cut, and feedrate. Slight tool wear was included as a noise factor. Based on Taguchi Orthogonal Array L18, a series of experiments were designed and performed on AISI 1018 steel. Analysis of variance, ANOVA, was employed to identify the significant factors affecting the surface roughness and S/N ratio was used to find the optimal cutting combination of the parameters. It was concluded that tool with a high positive rake angle and smaller insert nose radius produced lower surface roughness value in an internal turning operation. It was also concluded that high feedrate and low cutting speed has produced the lowest surface roughness.     

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.     

Surface roughness and cutting force prediction in MQL and wet turning process of AISI 1045 using design of experiments

This paper presents an investigation into the MQL (minimum quantity lubrication) and wet turning processes of AISI 1045 work material with the objective of suggesting the experimental model in order to predict the cutting force and surface roughness, to select the optimal cutting parameters, and to analyze the effects of cutting parameters on machinability. Fractional factorial design and central composite design were used for the experiment plan. Cutting force and surface roughness according to cutting parameters were measured through the external cylindrical turning based on the experiment plan. The measured data were analyzed by regression analysis and verification experiments were conducted to confirm the results. From the experimental results and regression analysis, this research project suggested the experimental equations, proposed the optimal cutting parameters, and analyzed the effects of cutting parameters on surface roughness and cutting force in the MQL and wet turning processes.     

Influence cutting parameters on the surface quality and corrosion behavior of Ti–6Al–4V alloy in synthetic body environment (SBF) using Response Surface Method

Most of the global manufacturing of titanium alloys is related to produce biphasic structures with grains alpha and beta. The development of modern applications of titanium alloy is a great challenge due to the chemical composition of Ti–6Al–4V alloy and the complexity of the manufacturing technology. This study proposed an optimal investigation of the variation of cutting speed, feed rate, and depth of cut in the turning of Ti–6Al–4V alloy on surface roughness, cutting efforts, and corrosion resistance. Response Surface Method has been established to optimize and model the responses mathematically. The adequacy of the models and a significant contribution of parameters were determined by analysis of variance (ANOVA). The biocompatibility of the machined surface for different cutting parameters was evaluated by the electrochemical polarization in simulated body fluids (SBF). Furthermore, desirability function analysis was used to determine the optimal values for surface quality, the turning force, and the passivation rate. It was clearly noticed that the multi-responses of the desirability function improved the machine process. The feed rate and depth of cut were the most relevant factors to minimize surface roughness and the turning forces. Moreover, the experimental results showed that the corrosion behavior was strongly related to minimal surface roughness. Finally, the optimization reduced the surface roughness Ra and Rz in 5.5% and 11.9%, respectively and increased the corrosion resistance in 18.8%.     

Selection of optimum cutting condition of cobalt-based superalloy with GONNS

Machining of new superalloys is challenging. Automated software environments for determining the optimal cutting conditions after reviewing a set of experimental results are very beneficial to obtain the desired surface quality and to use the machine tools effectively. The genetically optimized neural network system (GONNS) is proposed for the selection of optimal cutting conditions from the experimental data with minimal operator involvement. Genetic algorithm (GA) obtains the optimal operational condition by using the neural networks. A feed-forward backpropagation-type neural network was trained to represent the relationship between surface roughness, cutting force, and machining parameters of face-milling operation. Training data were collected at the symmetric and asymmetric milling operations by using different cutting speeds (V _c), feed rates (f), and depth of cuts (a _p) without using coolant. The surface roughness (Ra_asymt, Ra_symt) and cutting force (Fx_asymt, Fy_asymt, Fz_asymt, Fx_symt, Fy_symt, Fz_symt) were measured for each cutting condition. The surface roughness estimation accuracy of the neural network was better for the asymmetric milling operation with 0.4% and 5% for training and testing data, respectively. For the symmetric milling operations, slightly higher estimation errors were observed around 0.5% and 7% for the training and testing. One parameter was optimized by using the GONNS while all the other parameters, including the cutting forces and the surface roughness, were kept in the desired range.     

Influence of cutting speed on surface integrity for powder metallurgy nickel-based superalloy FGH95

Powder metallurgy (PM) nickel-based superalloy FGH95 has been widely used for components, which requires the greatest service performance. The surface integrity is becoming more and more important in order to satisfy the increasing service demands. However, the machined surface of FGH95 is easily damaged due to its poor machinability. The purpose of this paper is to investigate the effects of dry milling process parameters on the surface integrity of FGH95. Experiments were conducted on a CNC machining center under different cutting speeds. The machined surface is evaluated in terms of surface roughness, microhardness and white layer. Experiments results show milled surface integrity of FGH95 is sensitivity to the cutting speeds. The machined surface roughness decreases with increase of the cutting speed, but with further increase of cutting speed between 80?m/min to 100?m/min an increase in surface roughness appears. For microhardness, it can be seen that the machined workpiece surface hardens seriously. It can also draw the conclusion that cutting speed has the marginal effect on the white layer thickness generated in the machined subsurface.     

Measurement and analysis of surface roughness in turning of aerospace titanium alloy (gr5)

Titanium alloys are extensively used in aerospace, biomedical applications and they are used in corrosive environments. In this study, the effect of cutting parameters on the surface roughness in turning of titanium alloy has been investigated using response surface methodology. The experimental studies were conducted under varying cutting speeds, feed and depths of cut. The chip formation and SEM analysis are discussed to enhance the supportive surface quality achieved in turning. The work material used for the present investigation is commercial aerospace titanium alloy (gr5) and the tool used is RCMT 10T300 – MT TT3500 round insert. The equation developed using response surface methodology is used for predicting the surface roughness in machining of titanium alloy. The results revealed that the feed was the most influential factor which affect the surface roughness.     

Optimizing end-milling parameters for surface roughness under different cooling/lubrication conditions

The effect of cutting parameters on average surface roughness (Ra) in the different cooling/lubrication conditions, including minimal quantity lubrication, wet and dry cutting, was analyzed in this study. Orthogonal arrays were applied in the design of experiments, and Ti6Al4V end-milling experiments were performed on the Daewoo machining center. The white light interferometer (Wyko NT9300) was used to obtain the 3D profile of machined surface and calculate Ra values. Then, exponential model and quadratic model were proposed to fit the experimental data of surface roughness, respectively. Exponential fit model was employed to determine the significant cutting parameters on average surface roughness. Quadratic fit model was used to optimize the cutting parameters when cutting tool and material removal rate were given. The optimal average surface roughnesses were estimated according to the quadratic model. Finally, the verification experiments were performed, and the experimental results showed good agreement with the estimated results.     

Application of Taguchi and response surface methodologies for surface roughness in machining glass fiber reinforced plastics by PCD tooling

This paper discusses the use of Taguchi and response surface methodologies for minimizing the surface roughness in machining glass fiber reinforced (GFRP) plastics with a polycrystalline diamond (PCD) tool. The experiments have been conducted using Taguchi’s experimental design technique. The cutting parameters used are cutting speed, feed and depth of cut. The effect of cutting parameters on surface roughness is evaluated and the optimum cutting condition for minimizing the surface roughness is determined. A second-order model has been established between the cutting parameters and surface roughness using response surface methodology. The experimental results reveal that the most significant machining parameter for surface roughness is feed followed by cutting speed. The predicted values and measured values are fairly close, which indicates that the developed model can be effectively used to predict the surface roughness in the machining of GFRP composites. The predicted values are confirmed by using validation experiments.     

Modelling the effect of surface roughness factors in the machining of 2024Al/Al2O3 particle composites based on orthogonal arrays

This study presents an experimental investigation of the effects of cutting speed, size and volume fraction of particle on the surface roughness in turning of 2024Al alloy composites reinforced with Al₂O₃ particles. A plan of experiments, based on Taguchi method, was performed machining with different cutting speeds using coated carbide tools K10 and TP30. The objective was to establish a correlation between cutting speed, size and volume fraction of particle with the surface roughness in workpieces. These correlations were obtained by multiple linear regression. The analysis of variance was also employed to carry out the effects of these parameters on the surface roughness. The test results revealed that surface roughness increased with increasing the cutting speed and decreased with increasing the size and the volume fraction of particles for both cutting tools. The average surface roughness values of TP30 cutting tools were observed to be lower than those of K10 tools. For the average surface roughness values of TP30 tool, cutting speed was found to be the most effective factor while the volume fraction of particle was the most effective factor for those of K10 tool. A good agreement between the predicted and experimental surface roughness was observed within a reasonable limit.     

An experimental investigation on the process parameters influencing machining forces during milling of carbon and glass fiber laminates

Milling is the most feasible machining operation for producing slots and keyways with a well defined and high quality surface. Milling of composite materials is a complex task owing to its heterogeneity and the associated problems such as surface delamination, fiber pullout, burning, fuzzing and surface roughness. The machining process is dependent on the material characteristics and the cutting parameters. An attempt is made in this work to investigate the influencing cutting parameters affecting milling of composite laminates. Carbon and glass fibers were used to fabricate laminates for experimentations. The milling operation was performed with different feed rates, cutting velocity and speed. Numerically controlled vertical machining canter was used to mill slots on the laminates with different cutting speed and feed combinations. A milling tool dynamo meter was used to record the three orthogonal components of the machining force. From the experimental investigations, it was noticed that the machining force increases with increase in speed. For the same feed rate the machining force of GFRP laminates was observed to be very minimal, when compared to machining force of CFRP laminates. It is proposed to perform milling operation with lower feed rate at higher speeds for optimal milling operation.     

Optimization of turning parameters for surface roughness and tool life based on the Taguchi method

In this study, the effect and optimization of machining parameters on surface roughness and tool life in a turning operation was investigated by using the Taguchi method. The experimental studies were conducted under varying cutting speeds, feed rates, and depths of cut. An orthogonal array, the signal-to-noise (S/N) ratio, and the analysis of variance (ANOVA) were employed to the study the performance characteristics in the turning of commercial Ti-6Al-4V alloy using CNMG 120408-883 insert cutting tools. The conclusions revealed that the feed rate and cutting speed were the most influential factors on the surface roughness and tool life, respectively. The surface roughness was chiefly related to the cutting speed, whereas the axial depth of cut had the greatest effect on tool life.     

Empirical models and optimal cutting parameters for cutting forces and surface roughness in hard milling of AISI H13 steel

In the present research, an attempt has been made to experimentally investigate the effects of cutting parameters on cutting forces and surface roughness in hard milling of AISI H13 steel with coated carbide tools. Based on Taguchi’s method, four-factor (cutting speed, feed, radial depth of cut, and axial depth of cut) four-level orthogonal experiments were employed. Three cutting force components and roughness of machined surface were measured, and then range analysis and analysis of variance (ANOVA) are performed. It is found that the axial depth of cut and the feed are the two dominant factors affecting the cutting forces. The optimal cutting parameters for minimal cutting forces and surface roughness in the range of this experiment under these experimental conditions are searched. Two empirical models for cutting forces and surface roughness are established, and ANOVA indicates that a linear model best fits the variation of cutting forces while a quadratic model best describes the variation of surface roughness. Surface roughness under some cutting parameters is less than 0.25 μm, which shows that finish hard milling is an alternative to grinding process in die and mold industry.     

Optimal cutting condition determination for desired surface roughness in end milling

CNC end milling is a widely used cutting operation to produce surfaces with various profiles. The manufactured parts’ quality not only depends on their geometries but also on their surface texture, such as roughness. To meet the roughness specification, the selection of values for cutting conditions, such as feed rate, spindle speed, and depth of cut, is traditionally conducted by trial and error, experience, and machining handbooks. Such empirical processing is time consuming and laborious. Therefore, a combined approach for determining optimal cutting conditions for the desired surface roughness in end milling is clearly needed. The proposed methodology consists of two parts: roughness modeling and optimal cutting parameters selection. First, a machine learning technique called support vector machines (SVMs) is proposed for the first time to capture characteristics of roughness and its factors. This is possible due to the superior properties of well generalization and global optimum of SVMs. Next, they are incorporated in an optimization problem so that a relatively new, effective, and efficient optimization algorithm, particle swarm optimization (PSO), can be applied to find optimum process parameters. The cooperation between both techniques can achieve the desired surface roughness and also maximize productivity simultaneously.     

A study of Taguchi optimization method for identifying optimum surface roughness in CNC face milling of cobalt-based alloy (stellite 6)

The aim of this work is to develop a study of Taguchi optimization method for low surface roughness value in terms of cutting parameters when face milling of the cobalt-based alloy (stellite 6) material. The milling parameters evaluated are feed rate, cutting speed and depth of cut, a series of milling experiments are performed to measure the surface roughness data. The settings of face milling parameters were determined by using Taguchi experimental design method. Orthogonal arrays of Taguchi, the signal-to-noise (S/N) ratio, the analysis of variance (ANOVA) are employed to find the optimal levels and to analyze the effect of the milling parameters on surface roughness. Confirmation tests with the optimal levels of cutting parameters are carried out in order to illustrate the effectiveness of Taguchi optimization method. It is thus shown that the Taguchi method is very suitable to solve the surface quality problem occurring the face milling of stellite 6 material.     

基于遗传算法的超精密切削表面粗糙度预测模型参数辨识及切削用量优化

建立易于分析各切削用量对粗糙度影响关系的表面粗糙度预测模型和最优的切削用量组合,是超精密切削加工技术的不断发展的需要。针对最小二乘法和传统优化方法的不足,提出了将遗传算法用于超精密切削表面粗糙度预测模型的参数辨识,并用于求解最优切削用量,给出了金刚石刀具超精密切削铝合金的表面粗糙度预测数学模型和切削用量优化结果,进行了遗传算法和常规优化算法的比较,结果表明遗传算法较最小二乘法和传统的优化方法更适合于粗糙度预测模型的参数辨识及保证切削用量的最优。     

Surface roughness model for turning

The effect of the cutting parameters on the surface roughness in a turning operation has been investigated using response surface methodology. The mathematical prediction models for surface roughness have been obtained for a common mild steel. The equation is used for determining the optimal cutting conditions for a required surface roughness.     

Optimization of machining parameters using genetic algorithm and experimental validation for end-milling operations

Optimization of cutting parameters is valuable in terms of providing high precision and efficient machining. Optimization of machining parameters for milling is an important step to minimize the machining time and cutting force, increase productivity and tool life and obtain better surface finish. In this work a mathematical model has been developed based on both the material behavior and the machine dynamics to determine cutting force for milling operations. The system used for optimization is based on powerful artificial intelligence called genetic algorithms (GA). The machining time is considered as the objective function and constraints are tool life, limits of feed rate, depth of cut, cutting speed, surface roughness, cutting force and amplitude of vibrations while maintaining a constant material removal rate. The result of the work shows how a complex optimization problem is handled by a genetic algorithm and converges very quickly. Experimental end milling tests have been performed on mild steel to measure surface roughness, cutting force using milling tool dynamometer and vibration using a FFT (fast Fourier transform) analyzer for the optimized cutting parameters in a Universal milling machine using an HSS cutter. From the estimated surface roughness value of 0.71 μm, the optimal cutting parameters that have given a maximum material removal rate of 6.0×10³ mm³/min with less amplitude of vibration at the work piece support 1.66 μm maximum displacement. The good agreement between the GA cutting forces and measured cutting forces clearly demonstrates the accuracy and effectiveness of the model presented and program developed. The obtained results indicate that the optimized parameters are capable of machining the work piece more efficiently with better surface finish.     

An experimental analysis and measurement of process performances in machining of nimonic C-263 super alloy

Nimonic C-263 alloy is extensively used in the fields of aerospace, gas turbine blades, power generators and heat exchangers because of its unique properties. However, the machining of this alloy is difficult due to low thermal conductivity and work hardening characteristics. This paper presents the experimental investigation and analysis of the machining parameters while turning the nimonic C-263 alloy, using whisker reinforced ceramic inserts. The experiments were designed using Taguchi’s experimental design. The parameters considered for the experiments are cutting speed, feed rate and depth of cut. Process performance indicators, viz., the cutting force, tool wear and surface finish were measured. An empirical model has been created for predicting the cutting force, flank wear and surface roughness through response surface methodology (RSM). The desirability function approach has been used for multi response optimization. The influence of the different parameters and their interactions on the cutting force, flank wear and surface roughness are also studied in detail and presented in this study. Based on the cutting force, flank wear and surface roughness, optimized machining conditions were observed in the region of 210 m/min cutting speed and 0.05 mm/rev feed rate and 0.50 mm depth of cut. The results were confirmed by conducting further confirmation tests.