Optimization Of Multiple Performance Characteristics Research Articles

EFFECT OF PROCESS VARIABLES ON THE CORROSION INHIBITION PERFORMANCE OF AGERATUM CONYZOIDES ON STRUCTURAL STEEL IN HCl – FUZZY LOGIC APPROACH

The aim of this study is to optimize the process variables to achieve a more efficient Ageratum Conyzoides extract (ACE) inhibitive performance on structural steel in 1 M HCl acid, using the integrated Taguchi-fuzzy logic technique. Optimization of multiple performance characteristics of weight and corrosion rate; was achieved via a multiple response performance index (MRPI) from corrosion inhibition parameters of inhibitor concentration, temperature and exposure time. The predictive model determined that the setting of inhibitor concentration of 2 g/cm3, temperature of 30oC and exposure time of 2 days were the optimal setting for maximum inhibitive corrosion performance. The optimal setting was verified and validated by a confirmatory experiment, indicating a significant improvement of 0.634 in the actual MRPI value when the integrated Taguchi-Fuzzy logic optimization model was employed.

Optimization of Multiple Performance Characteristics for CNC Turning of Inconel 718 Using Taguchi–Grey Relational Approach and Analysis of Variance

The optimization of machining processes is a deciding factor when increasing productivity and ensuring product quality. The response characteristics, such as surface roughness, material removal rate, tool wear, and cutting time, of the finish turning process have been simultaneously optimized. We used the Taguchi-based design of experiments L9(34) in this study to test and find the best values for process parameters like cutting speed, feed rate, depth of cut, and nose radius. The Taguchi-based multi-objective grey relational approach (GRA) method was used to address the turning problem of Inconel 718 alloy to increase productivity, i.e., by simultaneously minimizing surface roughness, tool wear, and machining time. GRA and the S/N ratio derived from the Taguchi approach were utilized to combine many response characteristics into a single response. The grey relational grade (GRG) produces results such as estimations of the optimal level of input parameters and their proportional significance to specific quality characteristics. By employing ANOVA, the significance of parameters with respect to individual responsibility and the overall quality characteristics of the cutting process were ascertained. The single-objective optimization yielded the following results: minimal surface roughness of 0.167 µm, tool wear of 44.65 µm, minimum cutting time of 19.72 s, and maximum material speed of 4550 mm3/min. While simultaneously optimizing the Inconel 718 superalloy at a cutting speed of 100 m/min, depth of cut of 0.4 mm, feed rate of 0.051 mm/rev, and tool nose radius of 0.4 mm, the results of the multi-objective optimization showed that all investigated response characteristics reached their optimal values (minimum/maximum). To validate the results, confirmatory experiments with the most favorable outcomes were conducted and yielded a high degree of concurrence.

Study of multi-objective optimization of overall ventilation performance for an impinging jet ventilation system using Taguchi-based grey relational analysis

Impinging jet ventilation (IJV) system is believed to combines the advantages of good indoor air quality (IAQ) and high energy efficiency. However, there is few study about the optimization of multiple performance characteristics and the significance of design variables on the ventilation performance is not clear for IJV. This study addresses an approach based on Taguchi method with grey relational analysis for optimizing an IJV system with multiple performance characteristics (including draught discomfort (PD), temperature difference between the head and ankle level (ΔT), air change efficiency (ACE) and energy utilization coefficient (EUC)). The supply inlet height, supply velocity, supply temperature and return outlet height are selected as factors that affecting the ventilation performance of IJV and L9 (43) orthogonal array is employed to design the experiments. First, the optimal combinations for PD, ΔT, ACE and EUC are determined separately by Taguchi method coupled with numerical simulations and the significance of the studied factors in promoting each evaluation index is revealed and ranked through main effect analysis and ANOVA. Second, the optimal combination of design variables corresponding to the best overall performance for IJV is determined by converting multi-objective into a single-objective according to Taguchi-based grey relational analysis. Then the important factors influencing the overall performance is ranked and the heights of supply inlet and return device are found to be significant factors in improving the overall performance. Finally, a confirmatory test is conducted to verify the improvement of performance characteristics and satisfactory results are obtained.

Experimental study on drilling micro-hole through micro-EDM and optimization of multiple performance characteristics

Drilling of micro-hole with fine edges and smooth internal surfaces finds numerous applications in manufacturing industries; holes drilled in injection nozzles, drug delivery orifices and spinneret holes are some of examples. This work carries out an experimental investigation and parametric optimization on micro-hole drilling using micro-electrical discharge machining (µ-EDM) process. The machining experiments are conducted employing Taguchi L16 orthogonal array for evaluating machining time (t) average overcut (AOV), taper angle (TA), entry overcut (EnOV), exit overcut (ExOV) and material removal rate (MRR) as process performances, while voltage (V), capacitance (pF) and feed rate (µm/s) are considered as process parameters. The influence of process parameters on the responses is studied at different cutting conditions. The drilled micro-holes profile is studied in terms of burrs, craters, spots on the edges and internal surfaces using micrographs obtained with optical and scanning electron microscopes. The ANOVA result reveals that capacitance is the most significant parameter for t, AOV, TA, MRR, EnOV and ExOV. For simultaneous optimization of three performance measures (i.e., t, AOV and TA), an overall evaluation criterion has been evaluated and optimum cutting conditions such as voltage: 80 V, capacitance: 100 pF and feed rate: 15 µm/s are obtained. The expected outcome obtained at the optimum condition is improved by 10.57% (36.934 to 40.839) over the grand average of performance. The confirmation experimental result at optimum cutting condition (i.e., 39.378) is comparable with the expected result (i.e., 40.839).

Multiple performance characteristics optimization in end milling of thin-walled parts using desirability function

With the development of high-performance CNC machine tools, milling has been established as one of the main means of machining thin-walled parts. Thus, the selection of process parameters for milling operations is an important issue in end milling of thin-walled parts to assure product quality and increase productivity. The current study explores three machining parameters, namely wall thickness, feed, and machining strategies, that influence dimensional and form errors, surface roughness, and machining time milling of 7075-T6 aluminum alloy thin-walled parts. The effects of machining parameters on each of the response variables were analyzed using graphs of the main effects and three-dimensional surface plots. Analysis of the results show that the most influential factor for wall thickness deviation, dimensions deviation, perpendicularity deviation, flatness deviation, surface roughness of inner walls, surface roughness of outer walls, and surface roughness of reference plane was machining strategy, while feed is the most influential parameter affecting the machined time, followed by the machining strategy. The desirability concept has been used for simultaneous optimization in terms of machining parameters of the thin-walled parts machining process. Finally, a confirmation test with the optimal parameter settings was carried out to validate the results.

Multiple performance characteristics Optimization of WEDM by Grey Grade for EN-8

Multiple performance characteristics Optimization of WEDM by Grey Grade for EN-8

Dry turning of X2CrNi18-09 using coated carbide tools: modelling and optimization of multiple performance characteristics

The present paper investigates the cutting parameters pertaining to the turning of X2CrNi18-09 austenitic stainless steel that are studied and optimized using both RSM and desirability approaches. The cutting tool inserts used are the CVD coated carbide. The cutting speed, the feed rate and the depth of cut represent the main machining parameters considered. Their influence on the surface roughness and the cutting force are further investigated using the ANOVA method. The results obtained lead to conclude that the feed rate is the surface roughness highest influencing parameter with a contribution of 89.69%.The depth of cut and the feed rate are further identified as the most important parameters affecting the cutting force with contributions of 46.46% and 39.04% respectively. The quadratic mathematical models presenting the progression of the surface roughness and the cutting force and based on the machining parameters considered (cutting speed, feed rate and depth of cut) were obtained through the application of the RSM method. They are presented and compared to the experimental results. Good agreement is found between the two sections of the investigation. Furthermore, the flank wear of the CVD-coated carbide tool (GC2015) is found to increase with both cutting speed and cutting time. A higher tool life represented by t=44min is observed at cutting speed, feed rate and depth of cut of 280m/min,0.08mm/rev and 0.2mm respectively. Moreover and at low cutting speeds, the formation of micro weld is noticed and leads to an alteration of the surface roughness of the work piece. Finally, optimizing the machining parameters with the objective of achieving an improved surface roughness was accomplished through the application of the Desirability Function approach. This enabled to finding out the optimal parameters for maximal material removal rate and best surface quality for a cutting speed of 350m/min, a feed rate of 0.088 mm/rev and a depth of cut of 0.9mm.

Multi-objective optimization in wire-EDM process using grey relational analysis method (GRA) and backpropagation neural network–genetic algorithm (BPNN–GA) methods

PurposeThe purpose of this paper is to investigate prediction and optimization of multiple performance characteristics in the wire electrical discharge machining (wire-EDM) process of SKD 61 (AISI H13) tool steel.Design/methodology/approachThe experimental studies were conducted under varying wire-EDM process parameters, which were arc on time, on time, open voltage, off time and servo voltage. The optimized responses were recast layer thickness (RLT), surface roughness (SR) and surface crack density (SCD). Arc on time was set at two different levels, whereas the other four parameters were set at three different levels. Based on Taguchi method, an L18 mixed-orthogonal array was selected for the experiments. Further, three methods, namely grey relational analysis (GRA), backpropagation neural network (BPNN) and genetic algorithm (GA), were applied separately. GRA was performed to obtain a rough estimation of optimum drilling parameters. The influences of drilling parameters on multiple performance characteristics were determined by using percentage contributions. BPNN architecture was determined to predict the multiple performance characteristics. GA method was then applied to determine the optimum wire-EDM parameters.FindingsThe minimum RLT, SR and SCD could be obtained by setting arc on time, on time, open voltage, off time and servo voltage at 2 ms, 3 ms, 90 volt, 10 ms and 38 volt, respectively. The experimental confirmation results showed that BPNN-based GA optimization method could accurately predict and significantly improve all of the responses.Originality/valueThere were no publications regarding multi-response optimization using a combination of GRA and BPNN-based GA methods during wire-EDM process available.

Multi-objective optimization in face milling process with cryogenic cooling using grey fuzzy analysis and BPNN-GA methods

Purpose The purpose of this study is to investigate the prediction and optimization of multiple performance characteristics in the face milling process of tool steel ASSAB XW-42. Design/methodology/approach The face milling parameters (cutting speed, feed rate and axial depth of cut) and flow rate (FR) of cryogenic cooling were optimized with consideration of multiple performance characteristics, i.e. surface roughness (SR), cutting force (Fc) and metal removal rate (MRR). FR of cryogenic cooling has two levels, whereas the three face milling parameters each have three levels. Using Taguchi method, an L18 mixed-orthogonal array was selected as the design of experiments. The rough estimation of the optimum face milling parameters was determined by using grey fuzzy analysis. The global optimum face milling parameters were searched by applying the backpropagation neural network-based genetic algorithm (BPNN-GA) method. Findings The optimum SR, cutting force (Fc) and MRR could be obtained by setting FR, cutting speed, feed rate and axial depth of cut at 0.5 l/min, 280 m/min, 90 mm/min and 0.2 mm, respectively. The experimental confirmation results showed that BPNN-based GA optimization method could accurately predict and significantly improve all of the multiple performance characteristics. Originality/value To the best of the authors’ knowledge, there were no publications available regarding multi-response optimization using the combination of grey fuzzy analysis and BPNN-based GA methods during cryogenically face milling process.

Optimization of Multiple Performance Characteristics of Friction Stir Welded Joint with Grey Relational Analysis

In the present work, multi-objective optimization of the input welding parameters influencing the quality of weld joint of AA 5052 H32 aluminium alloy by FSW process is investigated using response surface methodology and grey relational analysis. The quality of the welded joint is measured based on the higher value of tensile strength and hardness of the joint obtained by experimentation. A central composite design technique consists of 31 sets of experiments using four factors and five levels are used. Tool pin profile, rotational speed, welding speed, and tool tilt angle are selected as the significant input parameters. Analysis of variance (ANOVA) is used to analyze the effect of each input welding parameters on the output responses. Statistical results show that the developed regression model is adequate. Optimum levels of welding parameters are finally identified using grey relation grade. Grain structure of the welded joint is examined by observing the micro and macro structures of the weld.

Taguchi Based Desirability Function Analysis for the Optimization of Multiple Performance Characteristics

Taguchi Based Desirability Function Analysis for the Optimization of Multiple Performance Characteristics

Multi-Objective Optimization in End Milling Process of ASSAB XW-42 Tool Steel with Cryogenic Coolant Using Grey Fuzzy Logic and Backpropagation Neural Network-Genetic Algorithm (BPNN-GA) Approaches

This study investigates the prediction and optimization of multiple performance characteristics in the end milling process of ASSAB XW-42 tool steel, i.e., surface roughness (SR), tool flank wear (VB) and material removal rate (MRR). The quality characteristics of SR and VB was the smaller the better, while MRR was the larger the better. Three methods, namely grey fuzzy logic, back propagation neural network (BPNN) and genetic algorithm (GA), were separately applied. The experimental studies were conducted by varying the end milling process parameters (cutting speed, feed rate and axial depth of cut) and liquid nitrogen cooling flow rate. Grey fuzzy logic was used to obtain a rough estimation of the optimum end milling parameters. The influences of end milling parameters on multiple performance characteristics were determined by using percent contributions. BPNN architecture was determined to predict the multiple performance characteristics. GA method was then applied to determine the optimum end milling parameters. The results of confirmation experiments showed a good agreement with the predicted responses.

Optimization of Multiple Performance Characteristics of Cold rolling Processes Using Taguchi’s Quality Loss Function

The single stand cold rolling mills are generally called as reversing mills, whereby the metal strip is successively wound and unwound in the form of coil as it is repeatedly passed back and forth through the one mill stand. Rolling Pass schedule consists of setting of control parameters at each pass depending on various factor like input and output thickness, width of the coil, material to be rolled, mill capability etc. Majority Quality related problem faced by cold rolled producers using single stand cold rolling are strip thickness variation and strip flatness, also they are concerned about power consumption and production rate. This happens because there is no standard pass schedule available which can predict these Performance parameters. The operator uses thumb rule to make pass schedule. There is no Theoretical model available for such complex cold rolling process. Experimental Model using Taguchi based Single Optimization Method can improve the quality and process performance of the Mill. The main objective of this paper is to show the capability of the Taguchi Quality loss function based multi response optimization methods in increasing productivity of cold rolling process while controlling the Quality and cost of Production. The proposed methodology is used to optimize Multi process performance namely thickness variation, strip flatness, production rate and Power consumption by obtaining optimal solution for control factors exit tension, entry tension, Mill speed and Roll bending pressure for cold rolling of low carbon steel in single stand reversible cold rolling mill. A L_27 orthogonal array was selected and total 27 experiments were conducted after selecting control factors and its levels. Interaction plot shows no interaction among the control parameters. The ANOVA carried out which shows the mill speed is most significant control factor. The optimal values obtained using the multi characteristics optimization Model using Taguchi loss function has been validated by confirmation experiment.

Experimental investigation and optimization of multiple performance characteristics of Al-Mica-B4C hybrid reinforced composite in drilling operation

Machining of composite is critical task and many parameters are needed to study in order to evaluate the performance of composite. In present study, performance characteristics of Al- mica-B4C composite are evaluated and optimized with Taguchi-grey relational analysis in drilling of B4C and mica reinforced hybrid aluminium matrix composites (AMCs) manufactured by powder metallurgy process. HSS, TiAlN coated carbide drill bits and uncoated cementide carbide drills bits were used under dry cutting conditions. The drilling parameters such as feed rate, spindle speed, drill tool material and material parameters like wt. % of B4C as well as Mica particles were optimized based on multiple performance characteristics including thrust force, drilling torque and surface roughness. Experiments were conducted on a computerised numerically controlled vertical machining centre. Drilling tool dynamometer is used to measure the thrust force and drilling torque. Also, to measure the surface roughness of drilled hole portable surface roughness tester is used which shows the readings in microns. Microstructure of composites and surface morphology of drilled hole is studied by using optical microscopy. Finally, it is concluded that, among the drilling tools used, TiAlN coated carbide drill bit shows the better result than other drill bits used. Composites manufactured in present study shows better strength for all levels of reinforcement. Also, it is found that the drilling feed rate affects on the drilling process significantly.

A hybrid approach for the prediction and optimization of cutting forces using grey-based fuzzy logic

This study focused on the Grey-Based Fuzzy Logic Algorithm for the prediction and optimization of multiple performance characteristics of oblique turning process. Experiments have been constructed according to Taguchi’s L16 orthogonal array design matrix. Cutting speed, rate of feed and depth of cut were selected as input parameters, whereas material removal rate, cutting force and surface roughness were selected as output responses. Using grey relation analysis (GRA), grey relational coefficient (GRC) and grey relation grade (GRG) were obtained. Then, Grey based fuzzy algorithm was applied to obtain grey fuzzy reasoning grade (GFRG). Analysis of variance (ANOVA) carried out to find the significance and contribution of parameters on multiple performance characteristics. Finally, confirmation test was applied at the optimum level of GFRG to validate the results. The results also show the application feasibility of the grey based fuzzy algorithm for continuous improvement in product quality in complex manufacturing processes.

MULTIPLE PERFORMANCE CHARACTERISTICS OPTIMIZATION OF HARD TURNING OPERATIONS USING UTILITY BASED-TAGUCHI APPROACH

In order to sustain in the global competitive scenario, the manufacturing industries have beenpracticing the various tools to achieve the high quality products at lower cost. Selection of appropriate cutting conditions is necessary to improve machinabilty of a work piece material. The present work objective is to find out the optimum cutting parameters in turning of hardened AISI M2 steel using cryogenically treated cutting inserts. The Utility concept coupled with Taguchi approach was employed to optimize both surface roughness and power consumption simultaneously. According to Analysis of variance (ANOVA) results, the feed was the major dominant factor followed by the cutting speed on multiple performance characteristics. The necessary optimum conditions for multiple performance characteristics optimization were obtained as cutting speed of 100 m/min, feed 0.04 mm/rev and depth of cut of 0.2 mm.

Multiple Performance Characteristics Optimization for AA8011 on Friction Stir Welding by RSM Based DEA

In this investigation, the optimization of the multiple responses of Vickers hardness, Impact strength with notched and Un-notched conditions of the process parameters of rotational speed, tool tilt angle and feed rate with the straight cam profiled tool is considered. The three factors, five level rotatable central composite design are selected to optimize the responses of friction stir welded AA 8011 aluminium alloys. The highest relative efficiency is found using the data envelopment analysis to predict the optimum parameters. It reveals that at the rotational speed of 680 RPM, the tool tilt angle of 85 degrees and the feed rate of 24 mm/min the good weld quality can be achieved.

Multiple performance characteristics optimization for Al 7075 on electric discharge drilling by Taguchi grey relational theory

Electric discharge drill machine (EDDM) is a spark erosion process to produce micro-holes in conductive materials. This process is widely used in aerospace, medical, dental and automobile industries. As for the performance evaluation of the electric discharge drilling machine, it is very necessary to study the process parameters of machine tool. In this research paper, a brass rod 2 mm diameter was selected as a tool electrode. The experiments generate output responses such as tool wear rate (TWR). The best parameters such as pulse on-time, pulse off-time and water pressure were studied for best machining characteristics. This investigation presents the use of Taguchi approach for better TWR in drilling of Al-7075. A plan of experiments, based on L27 Taguchi design method, was selected for drilling of material. Analysis of variance (ANOVA) shows the percentage contribution of the control factor in the machining of Al-7075 in EDDM. The optimal combination levels and the significant drilling parameters on TWR were obtained. The optimization results showed that the combination of maximum pulse on-time and minimum pulse off-time gives maximum MRR.

Multiple Performance Characteristics Optimization in the WEDM Process of SKD61 Tool Steel Using Taguchi Method Combined with Weighted Principal Component Analysis (WPCA)

This paper presents the optimization of a wire electrical discharge machining (WEDM) process of SKD61 tool steel (AISI H13). The use of the Taguchi method coupled with weighted principal component analysis (WPCA) has been applied. The WEDM machining parameters (arc on time, on time, open voltage, off time and servo voltage) were optimized with considerations of multiple performance characteristics, i.e., recast layer thickness (RL) and surface roughness (SR). The quality characteristics of both RL and SR were smaller-is-better. WPCA was applied to eliminate response correlation and to convert correlated responses into equal or less number of uncorrelated quality indices called principal components. Experimental results have shown that machining performance of the WEDM process can be improved effectively through the combination of Taguchi method and WPCA.

Multi-Responses Optimization Of Edm Sinking Process Of Aisi D2 Tool Steel Using Taguchi Grey–Fuzzy Method

Rough machining with Electro Discharge Machining (EDM) process gives a large Material Removal Rate (MRR) and high Surface Roughness (SR), while finish machining gives low SR and very slow MRR. In this study, Taguchi method coupled with Grey Relational Analysis (GRA) and fuzzy logic has been applied for optimization of multiple performance characteristics. The EDM machining parameters (gap voltage, pulse current, on time and duty factor) are optimized with considerations of multiple performance characteristics, i.e., MRR and SR. The quality characteristic of MRR is larger-is-better, while the quality characteristic of SR is smaller-is-better. Based on Taguchi method, an L18 mixed-orthogonal array is selected for the experiments. By using the combination of GRA and fuzzy logic, the optimization of complicated multiple performance characteristics was transformed into the optimization of a single response performance index. The most significant machining parameters which affect the multiple performance characteristics were gapvoltage and pulse current. Experimental results have also shown that machining performance characteristics of EDM process can be improved effectively through the combination of Taguchi method, GRA and fuzzy logic.