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.     

Grey–Taguchi method to optimize the milling parameters of aluminum alloy

The grey–Taguchi method was adopted in this study to optimize the milling parameters of A6061P-T651 aluminum alloy with multiple performance characteristics. A grey relational grade obtained from the grey relational analysis is used as the performance characteristic in the Taguchi method. Then, the optimal milling parameters are determined using the parameter design proposed by the Taguchi method. Experimental results indicate that the optimal process parameters in milling A6061P-T651 aluminum alloy can be determined effectively; the flank wear is decreased from 0.177 mm to 0.067 mm and the surface roughness is decreased from 0.44 μm to 0.24 μm, leading to a multiple performance characteristics improvement in milling qualities through the grey–Taguchi method.     

Influence of slab milling process parameters on surface integrity of HSLA: a multi-performance characteristics optimization

An attempt has been made in this paper to determine the optimal setting of slab milling process parameters. Four process parameters, i.e. cutting fluid, cutting speed, feed and depth-of-cut each at three levels except the cutting fluid at two levels, were considered. The multi-performance characteristics of the process were measured in terms of surface integrity defined by surface roughness, surface strain and micro-hardness of the work-piece. Eighteen experiments, as per Taguchi’s?L₁₈ orthogonal array, were performed on high-strength low-alloy steel. Grey relational analysis, being a widely used technique for multi-performance optimization, was used to determine Grey relational grade. Subsequently, Taguchi response table method and ANOVA were used for data analysis. Confirmation experiment was conducted to determine the improvement in the surface integrity using this approach. Results revealed that machining done in the presence of cutting fluid, at a cutting speed of 1,800 r.p.m. with a feed of 150?mm/min and depth-of-cut of 0.23?mm, yielded the optimum multi-performance characteristics of the slab milling process. Further, the results of ANOVA indicated that all four machining parameters significantly affected the multi-performance with maximum contribution from depth-of-cut (33.76%) followed by feed (24.02%), cutting speed (16.29%) and cutting fluid (13.21%).     

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.     

基于近似模型的铣削工艺参数可靠性设计优化

针对现有铣削工艺参数优化方法未考虑设计参数不确定性,导致优化结果难以满足实际产品性能要求的问题,引入近似模型对铣削工艺参数进行可靠性设计优化。以铣削加工表面粗糙度为目标函数,以最大铣削力小于给定值的可靠度作为约束,综合考虑铣削加工过程中铣削速度和每齿进给量的变动,建立了铣削工艺参数可靠性优化模型,并分别采用Kriging近似和径向基函数近似对铣削表面粗糙度、铣削力与设计变量之间的隐式关系进行近似替代,最后采用Monte Carlo仿真-序列近似规划对模型进行了寻优求解,通过试验对可靠性优化的结果进行了验证。结果表明,该方法可有效地降低铣削加工表面粗糙度,并且可保证加工过程中最大铣削力的可靠度要求。     

Application of Taguchi-grey multi responses optimization on process parameters in electro erosion

Convention Taguchi method deals with only single response optimization problems. Since the electrical discharge machining process involved with many response parameters, Taguchi method alone cannot help to obtain optimal process parameters in such process. In the present work, an endeavor has been made to derive optimal combination of electrical process parameters in electro erosion process using grey relational analysis with Taguchi method. This multi response optimization of the electrical discharge machining process has been conducted with AISI 202 stainless steel with different tool electrodes such as copper, brass and tungsten carbide. Gap voltage, discharge current and duty factor have been used as electrical excitation parameters with different process levels. Taguchi L₂₇ orthogonal table has been assigned for conducting experiments with the consideration of interactions among the input electrical process parameters. Material removal rate, electrode wear rate and surface roughness have been selected as response parameters. From the experimental results, it has been found that the electrical conductivity of the tool electrode has the most influencing nature on the machining characteristics in EDM process. The optimal combination of the input process parameters has been obtained using Taguchi-grey relational analysis.     

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.     

Enhancement and verification of a machined surface quality for glass milling operation using CBN grinding tool—Taguchi approach

Nowadays, the demand for high product quality focuses extensive attention to the quality of machined surface. The (CNC) milling machine facilities provides a wide variety of parameters set-up, making the machining process on the glass excellent in manufacturing complicated special products compared with other machining processes. However, the application of grinding process on the CNC milling machine could be an ideal solution to improve the product quality, but adopting the right machining parameters is required. Taguchi optimization method was used to estimate optimum machining parameters with standard orthogonal array L₁₆ (4⁴) to replace the conventional trial and error method as it is time-consuming. Moreover, analyses on surface roughness and cutting force are applied which are partial determinant of the quality of surface and cutting process. These analyses are conducted using signal to noise (S/N) response analysis and the analysis of variance (Pareto ANOVA) to determine which process parameters are statistically significant. In glass milling operation, several machining parameters are considered to be significant in affecting surface roughness and cutting forces. These parameters include the lubrication pressure, spindle speed, feed rate, and depth of cut as control factors. While, the lubrication direction is considered as a noise factor in the experiments. Finally, verification tests are carried out to investigate the improvement of the optimization. The results showed an improvement of 49.02% and 26.28% in the surface roughness and cutting force performance, respectively.     

Multi-objective optimization of cutting parameters in high-speed milling based on grey relational analysis coupled with principal component analysis

This paper investigates optimization problem of the cutting parameters in high-speed milling on NAK80 mold steel. An experiment based on the technology of Taguchi is performed. The objective is to establish a correlation among spindle speed, feed per tooth and depth of cut to the three directions of cutting force in the milling process. In this study, the optimum cutting parameters are obtained by the grey relational analysis. Moreover, the principal component analysis is applied to evaluate the weights so that their relative significance can be described properly and objectively. The results of experiments show that grey relational analysis coupled with principal component analysis can effectively acquire the optimal combination of cutting parameters and the proposed approach can be a useful tool to reduce the cutting force.     

Dimensional analyses and surface quality of pulsed UV laser micro-machining of STAVAX stainless steel mold inserts

Laser micro-machining is a new, precise, and very flexible process in micro-mold manufacturing, especially for difficult to machine material, i.e., hardened steel. The aim of the work reported in this paper was to utilize response surface methodology to optimize the dimensional accuracy and surface finish for STAVAX stainless steel mold inserts in the pulsed UV laser micro-machining. The influence of laser machining parameters on the ablated depth and surface roughness of the machined mold inserts have been experimentally investigated. The parameters of insert quality are analyzed under varying laser power, pulse frequency, hatched spacing, scan rate, and number of passes. The settings of the laser micro-machining parameters are determined by using design of experiments method. The analysis of variance, and regression analyses are employed to find the optimal levels and to analyze the effect of the parameters on the depth accuracy values and surface finish. Confirmation experiments with the optimal levels of micro-machining parameters are carried out in order to illustrate the effectiveness of the multi-optimization method. The validity of regression approach to process optimization is well established.     

基于混合算法的薄壁件铣削加工工艺参数优化

结合神经网络法和遗传算法的优点,提出了一种以倒传递神经网络法为基础的加工工艺参数优化方法,对薄壁件铣削加工工艺参数进行优化。将田口实验所得数据经倒传递神经网络进行训练与测试,来建立薄壁件铣削加工的信噪比预测器,并通过最大化信噪比,将铣削过程变异降至最低,进而找出最佳加工工艺参数组合。通过数值模拟与加工实验,验证了所提方法在薄壁件铣削加工工艺参数优化中的有效性。     

An integrated parameter optimization system for MISO plastic injection molding

This paper presents the development of a parameter optimization system that integrates mold flow analysis, the Taguchi method, analysis of variance (ANOVA), back-propagation neural networks (BPNNs), genetic algorithms (GAs), and the Davidon–Fletcher–Powell (DFP) method to generate optimal process parameter settings for multiple-input single-output plastic injection molding. In the computer-aided engineering simulations, Moldex3D software was employed to determine the preliminary process parameter settings. For process parameter optimization, an L₂₅ orthogonal array experiment was conducted to arrange the number of experimental runs. The injection time, velocity pressure switch position, packing pressure, and injection velocity were employed as process control parameters, with product weight as the target quality. The significant process parameters influencing the product weight and the signal to noise (S/N) ratio were determined using experimental data based on the ANOVA method. Experimental data from the Taguchi method were used to train and test the BPNNs. Then, the BPNN was combined with the DFP method and the GAs to determine the final optimal parameter settings. Three confirmation experiments were performed to verify the effectiveness of the proposed system. Experimental results show that the proposed system not only avoids shortcomings inherent in the commonly used Taguchi method but also produced significant quality and cost advantages.     

A new CNC turning canned cycle for revolved parts with free-form profile

Laser micromilling technique is a thermal machining process which is used to remove material on the target geometry and has been widely employed in mold and die making industry. In this technique, the control factors of process such as scan speed, scan direction, frequency, and fill spacing play major affect on the surface quality. The selected quality characteristics are the mean surface roughness and milling depth. The main objective of this study is to determine the optimal milling conditions based on machining direction for minimizing the surface roughness and maximizing the milling depth. Therefore, L18 orthogonal array is constituted and subsequently signal/noise ratio and analysis of variance were employed to investigate the optimal levels of process parameters. The analysis results show that the scan speed has the highest effect on the surface roughness of which percentage contribution is 39.68% and also the beam scan direction and fill spacing have significant effects which contribute 19.67% and 16.09%, respectively. The experimental result for optimal condition is 2.23?μm. The results for milling depth show that only scan speed and fill spacing have significant effects which contribute 69.08% and 19.21%, respectively. Moreover, the scan direction has the least effect on the milling depth which can be neglected. The frequency has no effect on both surface roughness and milling depth. The result obtained from experiment at the optimal condition is 121.4?μm.     

Modeling and Optimization of Machining Process in Discontinuously Reinforced Aluminium Matrix Composites

This article presents development of an Artificial Neural Networks (ANN) based model for the prediction of surface roughness during machining of composite material using Back Propagation algorithm. Statistically designed experiments based on Taguchi method were carried out on machining of Al/SiCp composite material. The experimentation helped generate a knowledge base for the ANN system and understand the relative importance of process, tool and work material dependent parameters on the roughness of surface generated during machining. The ANN model trained using the experimental data was found to predict the surface roughness with fair accuracy. An optimization approach was also proposed to obtain optimal cutting conditions that yield the desired surface roughness while maximizing the metal removal rate.     

Optimization of the EDM parameters on machining Ti–6Al–4V with multiple quality characteristics

In this paper, parameter optimization of the electrical discharge machining process to Ti–6Al–4V alloy considering multiple performance characteristics using the Taguchi method and grey relational analysis is reported. Performance characteristics including the electrode wear ratio, material removal rate and surface roughness are chosen to evaluate the machining effects. The process parameters selected in this study are discharge current, open voltage, pulse duration and duty factor. Experiments based on the appropriate orthogonal array are conducted first. The normalised experimental results of the performance characteristics are then introduced to calculate the coefficient and grades according to grey relational analysis. The optimised process parameters simultaneously leading to a lower electrode wear ratio, higher material removal rate and better surface roughness are then verified through a confirmation experiment. The validation experiments show an improved electrode wear ratio of 15%, material removal rate of 12% and surface roughness of 19% when the Taguchi method and grey relational analysis are used.     

A new approach to surface roughness and roundness improvement in wire electrical discharge turning based on statistical analyses

In this paper, the effects and the optimization of machining parameters on surface roughness and roundness in the turning wire electrical discharge machining (TWEDM) process are investigated. In the TWEDM process, a new machining parameter, such as rotational speed, is introduced, which changes the normal machining conditions in conventional wire electrical discharge machining (WEDM). By the Taguchi method, a complete realization of the process parameters and their effects were achieved. The Taguchi method has not been used in TWEDM by other researchers. The surface roughness and roundness were measured to verify the process. In addition, the open-circuit voltage, pulse-off time, open arc voltage, and the inter-electrode gap size, which are replaced by power, time-off, voltage, and servo, respectively, and also wire tension, wire speed, and rotational speed were chosen for evaluation by the Taguchi method. An L₁₈ (2¹?×?3⁷) Taguchi standard orthogonal array was chosen for the design of experiments. The level of importance of the machining parameters on the surface roughness and roundness was determined by using analysis of variance (ANOVA). The optimum machining parameters combination was obtained by using the analysis of signal-to-noise (S/N) ratios. The variation of surface roughness and roundness with machining parameters was mathematically modeled by using the regression analysis method. Finally, experimentation was carried out to identify the effectiveness of the proposed method. The presented model is also verified by a set of verification tests.     

Single and multiobjective optimization of Inconel 718 nickel-based superalloy in the wire electrical discharge machining

Inconel 718 is widely used in high-temperature environments, high-performance aircraft, and hypersonic missile weapon systems; however, it is very difficult to machine using conventional techniques. This study employed an L9 Taguchi orthogonal array for the analysis of wire electrical discharge machining parameters when used for the machining of Inconel 718. Our aim was to determine the optimal combination of parameters to minimize surface roughness while maximizing the material removal rate. The Taguchi method is widely applied in mechanical engineering with the aim of identifying the optimal combination of processing parameters as they pertain to single quality characteristics. Unfortunately, Taguchi analysis often leads to contradictory results when seeking to rectify multiple objectives. To resolve this issue, this study implemented gray relational analysis in conjunction with Taguchi method to obtain the optimal combination of parameters to deal specifically with multiple quality objectives. For the dual objectives of surface roughness and material removal rate, the optimal combination of parameters derived using gray relational analysis resulted in a mean surface roughness of 2.75 μm. In L9 orthogonal array experiments, run 1 produced the best gray relational grade with mean surface roughness of 2.80 μm, representing an improvement of 1.8%. The material removal rate achieved after the application of gray relational analysis was 0.00190 g/s, whereas the L9 experiment achieved a material removal rate of 0.00123 g/s, representing an improvement of 54.5%.     

ELECTRIC DISCHARGE MILLING AND MECHANICAL GRINDING COMPOUND MACHINING OF SILICON CARBIDE CERAMIC

Silicon carbide (SiC) ceramics have been widely used in modern industry. However, the manufacture of SiC ceramics is not an efficient process. This paper proposes a new technology of machining SiC ceramics with electrical discharge milling and mechanical grinding compound method. The compound process employs the pulse generator used in electrical discharge machining, and uses a water-based emulsion as the machining fluid. It is able to effectively machine a large surface area on SiC ceramics with a good surface quality. In this paper, the effects of pulse duration, pulse interval, peak voltage, peak current and feed rate of the workpiece on the process performance parameters, such as material removal rate, relative electrode wear ratio and surface roughness, have been investigated. A L₂₅ orthogonal array based on Taguchi method is adopted, and the experimental data are statistically evaluated by analysis of variance and stepwise regression. The significant machining parameters, the optimal combination levels of machining parameters, and the mathematical models associated with the process performance are obtained. In addition, the workpiece surface microstructure is examined with a scanning electron microscope and an energy dispersive spectrometer.     

Optimization of surface roughness and flank wear using the Taguchi method in milling of Hadfield steel with PVD and CVD coated inserts

In this paper, the Taguchi method and regression analysis have been applied to evaluate the machinability of Hadfield steel with PVD TiAlN- and CVD TiCN/Al₂O₃-coated carbide inserts under dry milling conditions. Several experiments were conducted using the L₁₈ (2 × 3 × 3) full-factorial design with a mixed orthogonal array on a CNC vertical machining center. Analysis of variance (ANOVA) was used to determine the effects of the machining parameters on surface roughness and flank wear. The cutting tool, cutting speed and feed rate were selected as machining parameters. The analysis results revealed that the feed rate was the dominant factor affecting surface roughness and cutting speed was the dominant factor affecting flank wear. Linear and quadratic regression analyses were applied to predict the outcomes of the experiment. The predicted values and measured values were very close to each other. Confirmation test results showed that the Taguchi method was very successful in the optimization of machining parameters for minimum surface roughness and flank wear in the milling the Hadfield steel.     

Analysis and optimization of the ball burnishing process through the Taguchi technique

In the present study, the analysis and optimization of the ball burnishing process has been studied. The Taguchi technique is employed to identify the effect of burnishing parameters, i.e., burnishing speed, burnishing feed, burnishing force and number of passes, on surface roughness, surface micro-hardness, improvement ratio of surface roughness, and improvement ratio of surface micro-hardness. Taguchi tools such as analysis of variance (ANOVA), signal-to-noise (S/N) ratio and additive model have been used to analyse, obtain the significant parameters and evaluate the optimum combination levels of ball burnishing process parameters. The analysis of results shows that the burnishing force with a contribution percent of 39.87% for surface roughness and 42.85% for surface micro-hardness had the dominant effect on both surface roughness and micro-hardness followed by burnishing feed, burnishing speed and then by number of passes.