Determination of optimum parameters for multi-performance characteristics in turning by using grey relational analysis

Optimization of multi-criteria problems is a great need of producers to produce precision parts with low costs. Optimization of multi-performance characteristics is more complex compared to optimization of single-performance characteristics. The theory of grey system is a new technique for performing prediction, relational analysis, and decision making in many areas. In this paper, the use of grey relational analysis for optimizing the turning process parameters for the workpiece surface roughness and the chip thickness is introduced. Various turning parameters, such as cutting speed, feed rate, tool nose radius, and concentration of solid–liquid lubricants (minimum-quantity lubricant) were considered. A factorial design with eight added center points was used for the experimental design. Optimal machining parameters were determined by the grey relational grade obtained from the grey relational analysis for multi-performance characteristics (the surface roughness and the chip thickness). The results of confirmation experiments reveal that grey relational analysis coupled with factorial design can effectively be used to obtain the optimal combination of turning parameters. Experimental results have shown that the surface roughness and the chip thickness in the turning process can be improved effectively through the new approach. The minimum surface roughness and smallest chip thickness are 9.83 and 0.32?mm, respectively, obtained at optimal conditions of cutting speed, 1,200?rpm; feed rate, 0.06?mm/rev; nose radius, 0.8?mm; and concentration of solid–liquid lubricant (10% boric acid + SAE-40 base oil).     

Application of grey fuzzy logic for the optimization of drilling parameters for CFRP composites with multiple performance characteristics

Carbon Fibre Reinforced Plastic (CFRP) composite materials have potential applications in various domains. In machining, drilling is essentially required to join different structures. But CFRP drilling poses many problems that decrease the quality of holes. In this paper, Taguchi’s L₂₇ orthogonal array is used to perform drilling of CFRP composite plates. To improve the quality of the holes drilled, the optimal combination of drilling parameters is chosen using grey relational analysis. Grey fuzzy optimization of drilling parameters is based on five different output performance characteristics, namely, thrust force, torque, entry delamination, exit delamination and eccentricity of the holes. Analysis of variance (ANOVA) is used to find the percentage contribution of the drilling parameters and found that feed rate is the most influential factor in drilling of CFRP composites.     

Multi response optimization of machining parameters of drilling Al/SiC metal matrix composite using grey relational analysis in the Taguchi method

This paper presents a new approach for the optimization of drilling parameters on drilling Al/SiC metal matrix composite with multiple responses based on orthogonal array with grey relational analysis. Experiments are conducted on LM25-based aluminium alloy reinforced with green bonded silicon carbide of size 25 μm (10% volume fraction). Drilling tests are carried out using TiN coated HSS twist drills of 10 mm diameter under dry condition. In this study, drilling parameters namely cutting speed, feed and point angle are optimized with the considerations of multi responses such as surface roughness, cutting force and torque. A grey relational grade is obtained from the grey analysis. Based on the grey relational grade, optimum levels of parameters have been identified and significant contribution of parameters is determined by ANOVA. Confirmation test is conducted to validate the test result. Experimental results have shown that the responses in drilling process can be improved effectively through the new approach.     

Multi-objective optimization using Taguchi based grey relational analysis for micro-milling of Al 7075 material with ball nose end mill

This study was carried out to understand micro-milling of aluminum material with ball nose end mill and consisted of four stages: experimental work, modelling, mono and multi objective optimization. In the first stage (experimental work), micro-milling experiments were carried out using Taguchi method. The effects of spindle speed, feed per tooth and depth of cut on tool wear, force and surface roughness were investigated. Cutting tools and workpiece surfaces were also inspected via scanning electron microscope. Adhesion and abrasion wear mechanisms during micro-milling of aluminum were observed. Workpiece surfaces had the accumulations of plastically deformed workpiece material due to the high ductility of aluminum. In the second stage (modelling), all data gathered in the experimental works were utilized to formulate first-order models with interaction. These first-order models with interaction could be used to predict responses in micro-milling of aluminum with a minor error. In the third stage (mono-objective optimization), responses were used alone in optimization study as an objective function. To minimize all responses, Taguchi’s signal to noise ratio was used. The effect of control factors on responses was determined by analysis of variance. In the fourth stage (multi objective optimization), responses were optimized simultaneously using grey relational analysis.     

Multi-response optimization of machining process parameters for powder mixed electro-discharge machining of H-11 die steel using grey relational analysis and topsis

Electro-discharge machining (EDM) is an enormously used nonconventional process for removing material in die making, aerospace, and automobile industries. It consists of limitations like poor volumetric material removal rate (MRR) and reduced surface quality. Powder mixed EDM (PMEDM) is a new development in EDM to enhance its machining capabilities. The present work investigates the effect of powder concentration (C_p), peak current (I_p), pulse on time (T_on), duty cycle (DC) and gap voltage (V_g) on MRR, tool wear rate (TWR), electrode wear ratio (EWR), and surface roughness (SR) simultaneously for H-11 die steel using SiC powder. Taguchi's L₂₇ orthogonal array has been used to conduct the experiments. Multiobjective optimization using grey relational analysis (GRA) and technique for order of preference by similarity to ideal solution (TOPSIS) has been used to maximize the MRR and minimize the TWR, EWR, and SR and determine the optimal set of process parameters. Analysis of variance (ANOVA) has been performed to understand the significance of each process parameter. Results were verified by conducting confirmatory tests. GRA and TOPSIS exhibit an improvement of 0.1843 and 0.14308 in the preference values, respectively. Microstructure analysis has been done using scanning electron microscope (SEM) for the optimum set of parameters.     

Application of the central composite design in optimization of machining parameters in drilling hybrid metal matrix composites

In this study, the application of response surface methodology (RSM) and central composite design (CCD) for modeling, optimization, and an analysis of the influences of dominant machining parameters on thrust force, surface roughness and burr height in the drilling of hybrid metal matrix composites produced through stir casting route. Experiments are carried out using Al 356-aluminum alloy reinforced with silicon carbide of size 25 μm and Mica of size 45 μm. Drilling test is carried out using carbide drill of 6 mm diameter. The design of experiment concept has been used to optimize the experimental conditions. The experimental data are collected based on a three-factor-three-level full central composite design. The multiple regression analysis using RSM is used to establish the input–output relationships of the process. The mathematical models are developed and tested for adequacy using analysis of variance and other adequacy measures using the developed models. The main and interaction effect of the input variables on the predicted responses are investigated. The predicted values and measured values are fairly close, which indicate that the developed models can be effectively used to predict the responses in the drilling of hybrid metal matrix composites. The optimized drilling process parameters have been obtained by numerical optimization using RSM by ensuring the minimum thrust force of 84 N, surface roughness of 1.67 μm, and the burr height of 0.16 mm. After the drilling experiments, a scanning electron microscope (SEM) is used to investigate the machined surface and tool wear.     

Experimental investigation and performance analysis of cemented carbide inserts of different geometries using Taguchi based grey relational analysis

In this work, effect of machining parameters cutting speed, feed rate and depth of cut, geometrical parameters cutting insert shape, relief angle and nose radius were investigated and optimized using Taguchi based grey relational analysis. 18 ISO designated uncoated cemented carbide inserts of different geometries were used to turn practically used automotive axles to study the influence of variation in carbide inserts geometry. Performance measures viz., flank wear, surface roughness and material removal rate (MRR) were optimized using grey relational grade, based on the experiments designed using Taguchi’s Design of Experiments (DoE). A weighted grey relational grade is calculated to minimize flank wear and surface roughness and to maximize MRR. Analysis of variance shows that cutting insert shape is the prominent parameter followed by feed rate and depth of cut that contributes towards output responses. An experiment conducted with identified optimum condition shows a lower flank wear and surface roughness with higher MRR. The confirmation results obtained are confirmed by calculating confidence interval, which lies within the width of the interval.     

Experimental investigation and optimisation in drilling of GFRP composites

Glass fibre-reinforced polymer (GFRP) composite materials are one of the important materials and are economic alternative to engineering materials because of their superior properties. This paper presents an effective approach for the optimisation of drilling parameters with multiple performance characteristics based on the Tagugch’s method with grey relational analysis. Taguchi’s L₁₆, 4-level orthogonal array has been used for the experimentation. The drilling parameters such as spindle speed and feed rate are optimised with consideration of multiple performance characteristics, such as thrust force, workpiece surface roughness and delamination factor. Response table and response graph are used for the analysis. The analysis of grey relational grade indicates that feed rate is the more influential parameter than spindle speed. The results indicate that the performance of drilling process can be improved effectively through this approach.     

The worst ill-conditioned silicon wafer slicing machine detected by using grey relational analysis

In the silicon slicing process, machine vibrations and the unstable wire knife motion cause the slicing precision to drift, or other ill-conditions. This process involves several synchronously occurring multiple quality characteristics, such as thickness (THK), bow, warp, total indicator reading (TIR), and total thickness variation (TTV), which must be closely monitored and controlled. In this research, grey relational analysis (GRA) is applied to prevent an ill-conditioned wire saw machine from producing an unconfirmed product that is screened from the synchronously occurring multiple quality characteristics. Five weights of those characteristics are determined by an entropy method. Finally, a case study and the exponential weighted moving average (EWMA) control chart are presented to demonstrate and verify the feasibility and effectiveness of the proposed method.     

Design optimization of cutting parameters for side milling operations with multiple performance characteristics

In this paper, a grey relational analysis is applied to a set of two-stage experiments designed to determine the cutting parameters for optimizing the side milling process with multiple performance characteristics. The cutting parameters to be considered are cutting speed, feed per tooth, axial depth of cut, radial depth of cut, overhang length and flank wear of peripheral cutting edge. L₃₆ and L₉ orthogonal arrays are used in the experiments and lower-the-better is used as a qualitative characteristic to evaluate the results. It is found that using the grey relational analysis coupled with a deliberate design of the two-stage experiments is simple and efficient in determining an optimal combination of the cutting parameters. The results of the confirmation test also show that this new approach can greatly improve the cutting performance of side milling process.     

Optimization of bone drilling parameters using grey-based fuzzy algorithm

In this article a modified algorithm (grey based fuzzy algorithm) is used to optimize multiple performance characteristics in drilling of bone. Experiments have been performed with different cutting conditions using full factorial design. The quality parameters considered are temperature, force and surface roughness. Grey relational analysis (GRA) coupled with fuzzy logic is employed to obtain a grey fuzzy reasoning grade (GFRG) combining all the quality characteristics. The highest GFRG is obtained for the feed rate of 40 mm/min and the speed of 500 rpm and is the optimal level. Analysis of variance (ANOVA) carried out to find the significance of parameters on multiple performance characteristics revealed that the feed rate has the highest contribution on GFRG followed by the spindle speed. The optimum level of the process parameters obtained is validated by the confirmation experiment.     

Measurement and optimization of surface roughness and tool wear via grey relational analysis,TOPSIS and RSA techniques

Magnesium alloy (Mg alloy) is one among the lightest materials and which has wide applications in the production of aircraft engines, airframes, helicopter components, light trucks, automotive parts and computers parts for its attractive properties. In this paper, a study to analyze the turning properties of magnesium alloy AZ91D in dry condition with polycrystalline diamond (PCD) cutting inserts is presented. Firstly, to investigate turning of magnesium alloy using grey relational analysis and TOPSIS of optimum cutting parameter values. Secondly, to determine using response surface analysis of mathematical model depending on cutting parameters of surface roughness and tool flank wear in turning. The adequacy of the developed mathematical model is proved by ANOVA. The findings from the investigation showed that feed rate and cutting speed are the dominant factors for surface roughness and tool flank wear respectively.     

Novel method for evaluating surface roughness by grey dynamic filtering

The evaluation of surface roughness is of great importance for manufacturing industries as the roughness of a surface has a considerable influence on its quality and the function of products. For surface roughness evaluation, to find an appropriate reference line is of the utmost importance. A smooth grey reference line obtained by grey dynamic filtering is proposed to evaluate surface roughness. The primary sampling data of the measured surface need not obey the typical distributions and the surface profile with less data can also be evaluated without losing primary data. Through sample analysis, the grey reference line is well consistent with ISO Gaussian reference line and their evaluation results for surface roughness are in agreement. The grey reference line can be used as one of complements for Gaussian reference line.     

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.     

Performance studies of multilayer hard surface coatings (TiN/TiCN/Al2O3/TiN) of indexable carbide inserts in hard machining: Part-II (RSM,grey relational and techno economical approach)

This paper presents the mathematical modelling and parametric optimization on flank wear and surface roughness based on response surface methodology and grey-based Taguchi method in finish hard turning of AISI 4340 steel (HRC 47 ± 1) using multilayer coated carbide (TiN/TiCN/Al₂O₃/TiN) insert under dry environment. The economical feasibility of utilizing multilayer TiN coated carbide insert has been described. Model adequacy has been checked using correlation coefficients. From main effect, it is evident that, cutting speed is the most significant factor for flank wear followed by depth of cut and feed. Again, feed is the most significant factor for surface roughness followed by cutting speed and depth of cut. The coefficient of determination (R²) is more than 75% for RSM models developed, which shows the high correlation exist between the experimental and predicted values. The experimental vs. predicted values of flank wear and surface roughness (Ra and Rz) are also found to be very close to each other implying significance of models developed. The improvement of grey relational grade from initial parameter combination (d2–f3–v4) to the optimal parameter combination (d1–f1–v3) is found to be 0.3093 using grey relational analysis coupled with Taguchi method for simultaneous optimization of responses. Flank wear (VBc) and surface roughness parameters (Ra and Rz) are decreased 1.9, 2.32 and 1.5 times respectively considering optimal parametric combinations for multi-responses. The calculated total machining cost per part is only Rs. 3.17 due to higher tool life (47 min at their optimal level) of multilayer TiN coated carbide insert. It brings to the reduction of downtime and increases the savings.     

Experimental investigation,prediction and optimization of cylindricity and perpendicularity during drilling of WCB material using grey relational analysis

Manufacturing is always the heart of majority of industries. Drilling is an extremely important and an essential machining process which requires a lot of attention as in most of the cases it is required for assembly purposes. Majority of the holes produced during drilling are made with the help of Vertical Machining Centre (VMC) meant for pin- hole assembly. Though the tolerance is within limit, assembly problems arise due to the improper geometry of these holes. Various geometrical tolerances like cylindricity, circularity, perpendicularity and position errors are responsible for such assembly problems. This investigation is focussed on cylindricity and perpendicularity in the drilling of Wrought Cast Steel Grade B (WCB) material using SOMX 050204 DT insert. In this work, effect of machining variables like cutting speed, feed rate and depth of cut (canned cycle) are investigated and optimized using grey relational analysis (GRA). Reliable experiments are conducted based on a 3³ full factorial, replicated twice. Second order regression models are developed for predicting cylindricity and perpendicularity. The models’ adequacy has been checked by calculating correlation coefficient. It shows that the developed models are well fitted for the prediction of responses within the specific range of input variables.     

Dynamic multi-response optimization using principal component analysis and multiple criteria evaluation of the grey relation model

The application of parameter design methodology has been considerable in recent years to make system performance robust over a wide range of input conditions. This notion has been referred to as a robust design with dynamic characteristics. Due to product complexity, multiple correlated characteristics must be simultaneously evaluated for improving product quality. Dynamic multi-response optimization is becoming an important issue to contemporary industry. This study developed a novel procedure of optimizing dynamic multi-responses using principal component analysis (PCA) and multiple criteria evaluation of the grey relation model. PCA can consider the correlations among multiple quality characteristics to obtain uncorrelated components. These components are then substituted into multiple criteria evaluation of the grey relation model to determine the optimal factor level combination. A case study demonstrates the effectiveness of the proposed procedure for optimizing dynamic multi-response processes.     

Surface roughness evaluation by using wavelets analysis

Based upon wavelets theory, a novel reference for evaluating surface roughness is proposed here, wherein the surface roughness can be separated from the actual surface profile f(t). Some examples have shown that more precise evaluation results could have been achieved than those found using classical reference lines.     

Application of Taguchi method for determining optimum surface roughness in turning of high-alloy white cast iron

This paper focused on optimizing the cutting conditions for the average surface roughness (R_a) obtained in machining of high-alloy white cast iron (Ni-Hard) at two different hardness levels (50 HRC and 62 HRC). Machining experiments were performed at the CNC lathe using ceramic and cubic boron nitride (CBN) cutting tools on Ni-Hard materials. Cutting speed, feed rate and depth of cut were chosen as the cutting parameters. Taguchi L₁₈ orthogonal array was used to design of experiment. Optimal cutting conditions was determined using the signal-to-noise (S/N) ratio which was calculated for R_a according to the “the-smaller-the-better” approach. The effects of the cutting parameters and tool materials on surface roughness were evaluated by the analysis of variance. The statistical analysis indicated that the parameters that have the biggest effect on R_a for Ni-Hard materials with 50 HRC and 62 HRC are the cutting speed and feed rate, respectively. Additionally, the optimum cutting conditions for the materials with 50 HRC and 62 HRC was found at different levels.