Optimization of cryogenic cooled EDM process parameters using grey relational analysis

This paper presents an experimental investigation on cryogenic cooling of liquid nitrogen (LN₂) copper electrode in the electrical discharge machining (EDM) process. The optimization of the EDM process parameters, such as the electrode environment (conventional electrode and cryogenically cooled electrode in EDM), discharge current, pulse on time, gap voltage on material removal rate, electrode wear, and surface roughness on machining of AlSiCp metal matrix composite using multiple performance characteristics on grey relational analysis was investigated. The L₁₈ orthogonal array was utilized to examine the process parameters, and the optimal levels of the process parameters were identified through grey relational analysis. Experimental data were analyzed through analysis of variance. Scanning electron microscopy analysis was conducted to study the characteristics of the machined surface.     

Multi-objective optimization of surface roughness and cutting forces in high-speed turning of Inconel 718 using Taguchi grey relational analysis (TGRA)

In this paper, a new effective approach, Taguchi grey relational analysis has been applied to experimental results in order to optimize the high-speed turning of Inconel 718 with consideration to multiple performance measures. The approach combines the orthogonal array design of experiments with grey relational analysis. Grey relational theory is adopted to determine the best process parameters that give lower magnitude of cutting forces as well as surface roughness. The response table and the grey relational grade graph for each level of the machining parameters have been established. The parameters: cutting speed, 475?m/min; feed rate, 0.10?mm/rev; depth of cut, 0.50?mm; and CW2 edge geometry have highest grey relational grade and therefore are the optimum parameter values producing better turning performance in terms of cutting forces and surface roughness. Depth of cut shows statistical significance on overall turning performance at 95% confidence interval.     

Optimization of squeeze casting process parameters using Taguchi analysis

Squeeze casting is a hybrid metal forming process combining features of both casting and forging in one operation. This paper reports a research in which an attempt was made to prepare solid cylindrical components of LM24 aluminum alloy through squeeze casting. The primary objective was to investigate the effect of process parameters on the mechanical properties exhibited by the castings produced though squeeze casting process. A set of trials were conducted based on parameters settings suggested in Taguchi’s offline quality control concept. The experimental results indicate that the squeeze pressure and the die-preheating temperature were the parameters making significant contribution toward improvement in mechanical properties of squeeze cast LM24 aluminum alloy.     

Investigation of micro-milling process parameters for surface roughness and milling depth

The main objective of this study is to investigate the micro-milling performance of the AISI H13 with different process parameters namely laser power, scan speed, frequency, and fill spacing using 30W fiber laser marking machine and to find the optimal operation conditions for minimum surface roughness and maximum milling depth. The 108 different combinations occurred with the interaction of each level of the parameters used in this study. Therefore, the main contribution of this paper to the related literature is that it produces new evidence regarding the effects of the multi-scan times on both surface roughness and milling depth. The experimental results are showed that 0.03?mm of fill spacing, the highest scan speed (800?mm/s), lowest frequency (20?kHz), and laser power (60%) produced better surface roughness, which is 1.75???m. The deeper cavity on the geometry is obtained under the experimental combination as 200?mm/s of scan speed, 0.02?mm of fill spacing, 60% of laser power, and 40?kHz of frequency, which is 195???m. The regression analysis was used to develop a mathematical model and determine the effect of process parameters on the surface roughness and milling depth. The results of subsequent tests verifies regression models.     

Optimization of EDM process for multiple performance characteristics using Taguchi method and Grey relational analysis

Electrical discharge machining (EDM) is one of the most extensively used non-conventional material removal processes. The Taguchi method has been utilized to determine the optimal EDM conditions in several industrial fields. The method, however, was designed to optimize only a single performance characteristic. To remove that limitation, the Grey relational analysis theory has been used to resolve the complicated interrelationships among the multiple performance characteristics. In the present study, we attempted to find the optimal machining conditions under which the micro-hole can be formed to a minimum diameter and a maximum aspect ratio. The Taguchi method was used to determine the relations between machining parameters and process characteristics. It was found that electrode wear and the entrance and exit clearances had a significant effect on the diameter of the micro-hole when the diameter of the electrode was identical. Grey relational analysis was used to determine the optimal machining parameters, among which the input voltage and the capacitance were found to be the most significant. The obtained optimal machining conditions were an input voltage of 60V, a capacitance of 680pF, a resistance of 500Ω, the feed rate of 1.5μm/s and a spindle speed of 1500rpm. Under these conditions, a micro-hole of 40μm average diameter and 10 aspect ratio could be machined.     

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.     

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.     

Multiple process parameter optimization of wire electrical discharge machining on Inconel 825 using Taguchi grey relational analysis

In this paper, an effective approach, Taguchi grey relational analysis, has been applied to experimental results of wire cut electrical discharge machining (WEDM) on Inconel 825 with consideration of multiple response measures. The approach combines the orthogonal array design of experiment with grey relational analysis. The main objective of this study is to obtain improved material removal rate, surface roughness, and spark gap. Grey relational theory is adopted to determine the best process parameters that optimize the response measures. The experiment has been done by using Taguchi’s orthogonal array L36 (2¹?×?3⁷). Each experiment was conducted under different conditions of input parameters. The response table and the grey relational grade for each level of the machining parameters have been established. From 36 experiments, the best combination of parameters was found. The experimental results confirm that the proposed method in this study effectively improves the machining performance of WEDM process.     

Optimizing multi-requirements of surface finishing in magnetic abrasive cutting process using grey relational analysis

Journal of Mechanical Science and Technology - Mechanical machining inevitably generates undesirable parts on the surface of workpieces. It brings adverse effects in terms of manufacturing cost,...     

Optimization of surface roughness,cutting force and tool wear of nitrogen alloyed duplex stainless steel in a dry turning process using Taguchi method

In this work, the dry turning parameters of two different grades of nitrogen alloyed duplex stainless steel are optimized by using Taguchi method. The turning operations were carried out with TiC and TiCN coated carbide cutting tool inserts. The experiments were conducted at three different cutting speeds (80, 100 and 120 m/min) with three different feed rates (0.04, 0.08 and 0.12 mm/rev) and a constant depth of cut (0.5 mm). The cutting parameters are optimized using signal to noise ratio and the analysis of variance. The effects of cutting speed and feed rate on surface roughness, cutting force and tool wear were analyzed. The results revealed that the feed rate is the more significant parameter influencing the surface roughness and cutting force. The cutting speed was identified as the more significant parameter influencing the tool wear. Tool wear was analyzed using scanning electron microscope image. The confirmation tests are carried out at optimum cutting conditions. The results at optimum cutting condition are predicted using estimated signal to noise ratio equation. The predicted results are found to be closer to experimental results within 8% deviations.     

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.     

Optimization of surface roughness in end milling Castamide

Castamide is vulnerable to humidity up to 7%; therefore, it is important to know the effect of processing parameters on Castamide with and without humidity during machining. In this study, obtained quality of surface roughness of Castamide block samples prepared in wet and dry conditions, which is processed by using the same cutting parameters, were compared. Moreover, an artificial neural network (ANN) modeling technique was developed with the results obtained from the experiments. For the training of ANN model, material type, cutting speed, cutting rate, and depth of cutting parameters were used. In this way, average surface roughness values could be estimated without performing actual application for those values. Various experimental results for different material types with cutting parameters were evaluated by different ANN training algorithms. So, it aims to define the average surface roughness with minimum error by using the best reliable ANN training algorithm. Parameters as cutting speed (V _c), feed rate (f), diameter of cutting equipment, and depth of cut (a _p) have been used as the input layers; average surface roughness has been also used as output layer. For testing data, root mean squared error, the fraction of variance (R ²), and mean absolute percentage error were found to be 0.0681%, 0.9999%, and 0.1563%, respectively. With these results, we believe that the ANN can be used for prediction of average surface roughness.     

Application of Taguchi,ANFIS and grey relational analysis for studying,modeling and optimization of wire EDM process while using gaseous media

In the dry wire electrical discharge machining (WEDM) process, the liquid dielectric is replaced with gaseous medium to enhance the machining environment safety. Also, this modification improves the surface quality of machined specimen but decreases the material removal rate of the process. In the present work, experimental study of dry WEDM process has been performed while machining of Al/SiC metal matrix composite. At first, a series of exploratory experiments has been conducted to identify appropriate gas and wire material based on their cutting velocity. After selection of the best gas and best wire, they were used for later stage of experiments. At the next stage, experiment were designed and conducted based on L₂₇ Taguchi's orthogonal array to study the effect of pulse on time, pulse off time, gap voltage, discharge current, wire tension and wire feed on cutting velocity (CV) and surface roughness (SR). Analysis of variances (ANOVA) has been performed to identify significant factors. In order to correlate relationship between process inputs and responses, adaptive neuro-fuzzy inference system has been utilized. At the end, a grey relational analysis has been used to maximize CV and minimize SR simultaneously. Results indicated that oxygen gas and brass wire guarantee superior cutting velocity. Also according to ANOVA, pulse on time and current were found to have significant effect on CV and SR.     

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).     

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

The theory of grey systems 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 optimising the drilling process parameters for the work piece surface roughness and the burr height is introduced. Various drilling parameters, such as feed rate, cutting speed, drill and point angles of drill were considered. An orthogonal array 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 burr height). Experimental results have shown that the surface roughness and the burr height in the drilling process can be improved effectively through the new approach .     

Optimization of process parameters for recycling of mill scale using Taguchi experimenal design

The present work submits an investigation about the optimum process parameters and quality improvement of mill scale recycling. With increasing concerns on environmental issues, the recycling of materials of all types has become an important issue. In this paper, an optimization method is developed to improve quality in mill scale recycling. The optimum configuration of process parameters to achieve high metallization efficiency was determined by experiments. The Taguchi method, the signal-to-noise (S/N) ratio, the analysis of variance (ANOVA) and rsponse surface optimization are employed to find the main effects and to determine their optimum process parameters. The significant process parameters were identified and their effects on mill scale recycling were studied. Finally, a confirmation experiment with the optimal levels of the process parameters was carried out to demonstrate the effectiveness of the Taguchi method.     

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.     

Taguchi-based grey relational analysis for modeling and optimizing machining parameters through dry turning of Incoloy 800H

The present research focused on the optimization of machining parameters and their effects by dry-turning an incoloy 800H on the basis of Taguchi-based grey relational analysis. Surface roughness (Ra, Rq and Rz), cutting force (Fz), and cutting power (P) were minimized, whereas Material removal rate (MRR) was maximized. An L ₂₇ orthogonal array was used in the experiments, which were conducted in a computerized and numerical-controlled turning machine. Cutting speed, feed rate, and cut depth were set as controllable machining variables, and analysis of variance was performed to determine the contribution of each variable. We then developed regression models, which ultimately conformed to investigational and predicted values. The combinational parameters for the multiperformance optimization were V = 35 m/min, f = 0.06 mm/rev and a = 1 mm, which altogether correspond to approximately 48.98 % of the improvement. The chip morphology of the incoloy 800H was also studied and reported.     

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.