Multiple Performance Characteristics Research Articles

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 of some correlated process parameters in EDM of Inconel 800 using a hybrid approach

Electrical discharge machining (EDM) is an extensively used non-traditional machining process used for conductive materials to get intricate or complex shapes. For any manufacturing industry, optimum parameters of control variables are of sheer importance to improve multiple performance characteristics like surface integrity and productivity. This paper presents multi-objective optimization on the basis of ratio analysis (MOORA) method coupled with principal component analysis (PCA) in order to achieve the optimal combination of EDM parameters. In this research work, response surface methodology was used for designing the experiments considering three input parameters, namely pulse-on time, pulse-off time and pulsed current. All the experiments were conducted at different parametric combinations and the performance, namely material removal rate (MRR) and surface roughness (Ra). Proposed MOORA-PCA hybrid results and conventional MOORA results were compared, and it is found that proposed methods are accurate for predicting the responses. Finally, the control variables, namely pulse-on time (TON), pulse-off time (TOFF) and pulsed current (Ip), were set to 300 µs, 85 µs and 18 A, respectively, to get maximum MRR and minimum surface roughness.

Powder mixed electric discharge machining of high-speed steel T1 grade

PurposeThe purpose of this paper is to represent the innovative process of powder-mixed electrical discharge machining of high-speed steel T1 grade and to conduct experimental investigation to optimize the machining parameters associated with multiple performance characteristics using grey relational analysis. The machining of high-speed steel T1 grade via conventional machining is a difficult process. However, it can be easily machined by powder-mixed electric discharge machining.Design/methodology/approachCarefully selected machining parameters give the optimum output results. For experimentation, the following input parameters have been used: pulse on-time, discharge current, tool material and powder concentration. The effects of input parameters, namely, material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR), have been investigated in this research.FindingsGrey relational analysis and analysis of variance have been performed to optimize the input parameters for better output response. Optimized results show increment of TWR, MRR and SR, which is 63.24, 52.18 and 42.49 per cent, respectively.Originality/valueThis research paper will be beneficial for the industrial application. The GRA result gives the better output response.

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.

Optimasi Multirespon Gaya Tekan Dan Momen Torsi Pada Penggurdian Material Komposit Glass Fiber Reinforce Polymer (Gfrp) yang Ditumpuk Dengan Material Stainless Steel (SS) Menggunakan Metode Algoritma Genetika

Glass fiber reinforced polymer (GFRP)-stainless steel stacks used in the aircraft structural components. The assembly process of this components requires mechanical joining using bolt and nut. The drilling process is commonly used for producing hole to position the bolt correctly. Thrust force and torque are responses that used to evaluate the performance of drilling process. The quality characteristic of these responses are “smaller-is-better.” The aim of this experiment is to identify the combination of process parameters for achieving required multiple performance characteristics in drilling process of GFRP-stainless steel stacks materials. The three important process parameters, i.e., point angle, spindle speed, and feed rate were used as input parameters. Point angle was set at two different levels, whilethe other two were set at three different levels. Hence, a 2 x 3 x 3 full factorial was used as designexperiments. The experiments were replicated two times. The optimization was conducted by using genetic algorithm method. The minimum thrust force and torque could be obtained by using point angle, spindle speed and feed rate of 118o, 2383 rpm, 62 mm/min respectively.

Performance optimization of absorption refrigeration systems using Taguchi, ANOVA and Grey Relational Analysis methods

There are various factors having an impact on the energetic and exergetic performance (i.e., COP and eCOP) of an absorption refrigeration systems (ARS) such as the temperatures of the generator, condenser, evaporator and absorber, effectiveness of solution, refrigerant and solution-refrigerant heat exchangers and isentropic efficiency of the solution pump. Many studies have focused on these process parameters, but the importance order and contribution ratios of the parameters due to thermodynamic performance have not been determined by using statistical methods. Firstly, in this study, cycles’ thermodynamic model is established and the variation of the COP and eCOP are calculated for different working conditions with different parameters ranges. The effects of these parameters on the COP and eCOP are examined separately on a statistical basis. The importance order of the parameters are determined by using Taguchi and ANOVA methods and the results are compared. Optimum operating conditions are determined by means of statistical analysis for the COP and eCOP. Under these operating conditions, the COP and eCOP of the system are calculated as 0.697 and 0.2829, respectively. Furthermore, for the simultaneous maximization of these two performance indicators, Taguchi-Grey Relational Analysis (GRA) is used. By using this analysis, importance order of the examined parameters on multiple performance characteristics are determined. The absorber and evaporator temperatures are the most efficient parameters on multiple performance characteristics with a contribution ratio of 29.66% and 26.34% of the total effect while the least efficient parameters are the pump efficiency and effectiveness of solution-refrigerant heat exchanger with a contribution ratio of 0.48% and 2.41%, respectively. For the best condition considering the multiple performance characteristics, COP and eCOP of the system are found as 0.6255 and 0.2829, respectively.

Optimization of RF sputtering process parameters on electrical resistivity, deposition rate and sensitivity of Al-doped ZnO thin films grown on Si substrate using grey-Taguchi technique

Increasing environmental pollution globally demands gas sensors for monitoring urban air quality, fire and exhaust from automobiles. The need for high performance gas sensors requires a good control over sensing material structure. This paper studies the suitability of Al-doped ZnO thin films for development of CO gas sensors. Deposition of Al-doped ZnO thin films on Si substrates by the radio frequency sputtering technique was carried out to study the influence of process parameters. The process parameters selected for the analysis were power, deposition time, substrate temperature and working pressure. An orthogonal array L16 ( $$4^{4}$$ ), signal-to-noise ratio and analysis of variance (ANOVA) were performed to optimize the electrical resistivity, deposition rate and sensitivity of the thin films using the Taguchi method. Grey relational grade (GRG) was performed to obtain multiple-performance characteristics of the thin films by optimizing the process parameters. GRG analyses identified the process parameters: power 150 W, deposition time 35 min, substrate temperature $$25^{\circ }\mathrm{C}$$ and working pressure 1.5 Pa showed optimal multiple-performance characteristics. ANOVA analyses indicate that power and substrate temperature show significant effect compared with other parameters. Thin films at the annealing temperature ( $$450^{\circ }\mathrm{C}$$ ) showed a decrease in electrical resistivity and an increase in sensitivity. At the sensor operating temperature of $$150^{\circ }\mathrm{C}$$ , Al-doped thin films exhibited the lowest resistivity $$3.76 \times 10^{-3}\,\Omega $$ -cm and the highest sensitivity of 59%. The optimal multiple-performance characteristic of thin film sample identified is found suitable for CO gas-sensing applications.

Durable TBCs with self-enhanced thermal insulation based on co-design on macro- and microstructure

During thermal exposure of thermal barrier coatings (TBCs), sintering causes degradation of the thermal barrier performance, as well as spallation. However, it is difficult to simultaneously enhance the thermal insulation and extend the lifetime, because the dominant factors (e.g., micropores and the macroscopic thickness) may have opposite effects on these two performances. In this study, a co-design of the macro- and microstructure is proposed to enhance multiple performance characteristics of TBCs. The microstructural design is achieved by using a co-spraying method, to form a hybrid layered coating. During thermal exposure, new pores are formed because the contractions of dense splats and loose porous nanoheaps are reversed. Consequently, the thermal barrier performance is self-enhanced by 40% with respect to that of mono-layered coatings. The macrostructural design is realized by tailoring the thickness according to the equivalent thermal insulation. As a result, the lifetime is extended by 35%. The mechanisms responsible for the simultaneously enhanced performance characteristics are discussed. This study is expected to contribute to structural tailoring of advanced TBCs with both high thermal insulation and long lifetimes.

Optimization of microridge punch design for deep drawing process by using the fuzzy Taguchi method

In recent years, metal components produced through die forming have decreased in size leading to challenges in drawing forming. In this study, an optimal microridge punch was designed using the robust multi-objective design in the fuzzy Taguchi method to enhance the formability of deep drawing process. Using a round, stainless steel cup measuring 2.5 mm in inner diameter and 0.1 mm in thickness, four punch microridge dimension parameters that influenced the thinning rate (TR) and stripping force (SF) during drawing forming were explored. The design performances of two objective functions, namely, the TR and SF, were assessed using the Taguchi method. The effect of the design parameters on the objective functions were verified using a factor effect analysis and an analysis of variance (ANOVA), whereas the measuring index of optimal multiple performance characteristics (MPC) was obtained using fuzzy logic inference. The analysis results showed that the dimension parameters that influenced TR and SF contribution, listed in descending order, were ridge gap (45.25%), ridge nose radius (40.86%), ridge-to-nose distance (11.18%), and ridge height (2.71%). The optimal values were subsequently used to design and produce drawing dies and an experiment was performed to verify their effectiveness. The experiment result showed that the optimal ridge punch drawing distance reached 1.542 mm, which was an increase of 55.60% compared with the drawing distance of non-ridge punch (0.993 mm).

Multi-Objective Optimal Cloud Model Design of Vehicle-to-Grid Connected Systems Based on the Multiple Performance Characteristic Index Method

In this paper, a statistical cloud model was proposed for optimal design of the proportional integral derivative (PID) controllers used in current control of vehicle-to-grid connected inverter systems with PID parameters. By collecting the effective control factors and noise factors from a cloud data base, the cloud model can minimize both the reactive power and the total harmonic distortion for the single-phase full-bridge vehicle-to-grid connected system. The multi-objective optimal solution is obtained by using statistical fuzzy-based response surface methodology with multiple performance characteristics index. The testing results showed the validity of the proposed cloud model. It is verified that the statistical cloud model can increase the performance of the single-phase full-bridge vehicle-to-grid connected system in practical vehicle-to-grid applications in the Internet of Things.

Multi-objective Optimization in Drilling Kevlar Fiber Reinforced Polymer Using Grey Fuzzy Analysis and Backpropagation Neural Network–Genetic Algorithm (BPNN–GA) Approaches

An integrated approach has been applied to predict and optimize multi-performance characteristics, such as optimum thrust force (Fz), torque (Mz), hole surface roughness (Ra), delamination (D) and hole roundness (R), in drilling process of Kevlar fiber reinforced polymer. The experiments were performed by varying drill point geometry and drilling process parameters, i.e., drill point angle, feed rate, and spindle speed. The quality characteristics Fz, Mz, Ra, D, and R were the smaller the better. Taguchi orthogonal array (OA) L18 was used as the design of experiments. Grey fuzzy analysis was first applied to obtain a rough estimation of the optimum drill point geometry and drilling process parameters. Backpropagation neural network (BPNN) model was developed and utilized to predict the optimum Fz, Mz, Ra, D, and R. Genetic algorithm (GA) was performed to search for global optimum of drilling process parameters combinations. The analysis of the effect of drill point angle, as well as drilling process parameters, on the individual performance characteristics was conducted by examining both the percentage contribution of drill point geometry and drilling process parameters on the total variance of three responses individually, and the response graphs. The results of the confirmation experiment showed that the BPNN based GA optimization method could accurately predict and also significantly improve the multiple performance characteristics.

Tribological characterization of recycled basalt-aramid fiber reinforced hybrid friction composites using grey-based Taguchi approach

Fibers play an imperative role in determining the tribological behavior of reinforced friction composites. The objective of the present study is to develop recycled basalt and recycled aramid fiber reinforced hybrid friction composites with the addition of various fillers and optimize the tribological behaviors of the composites. The pin-on-disc test rig was used to conduct dry sliding wear following Taguchi’s L27 orthogonal array with process parameters of the applied load, sliding speed and the weight percentage of fiber content. Grey relational analysis was adopted for tribological parameters optimization, satisfying multiple performance characteristics. Analysis of variance was used to find the contribution of individual factors that affect tribological behavior. Optimal parameter combination for multi-response characteristics of the friction composite under investigation was applied a load of 15 N, sliding speed of 1 m s−1 and composite with 25 weight percent of fiber content. The results showed that addition of fiber content significantly increased wear resistance of the friction composites. Further, from the scanning electron microscopy images of friction and wear tested friction composites, plateau formation, fiber-matrix debonding, fiber pull out, cracks and damage on the matrix and various wear mechanism were identified.

Experimental study of inverted drilling Al-7075 alloy

Al-7075 alloys are drilled using an inverted drilling method to investigate the effect of hole performances (enlargement, roundness, and surface roughness). Additionally, this study uses a gray-Taguchi methodology to optimize the multiple performance characteristics for the machining parameters in the dry drilling of Al-7075 alloy. The experimental results indicated that holes are enlarged using TiAlN- and TiN-coated drills by only 68.9% and 75.0% more than if an uncoated drill is used. The analysis of variance (ANOVA) results for the multiple performance characteristics in dry drilling Al-7075 alloys show that the spindle speed has a significant effect among all drilling parameters. Uncoated drill experiences the maximum wear, followed by the TiN-coated drill and then the TiAlN-coated drill. In an inverted drilling machine arrangement, chips fall out of the workpiece that is being drilled to prevent chips clogging and tool wear caused by secondary cutting. The values for the enlargement, roundness, and surface roughness for the inverted drilling are 95.5%, 88.8%, and 83.0% that for standard drilling, respectively. Using inverted drilling creates better hole quality and less tool wear than standard drilling.

Selection of optimal glazing material for solar thermal applications using grey relational analysis

ABSTRACT This article deals the selection of optimal glazing materials for solar thermal applications with multiple performance characteristics among the twelve alternatives using the Taguchi's grey relational analysis approach. The processing parameters thermal properties, mechanical properties, optical properties, environmental and economic conditions are optimised with multi-response characteristics, including density, thermal conductivity, specific heat capacity, the coefficient of thermal expansion, notch impact strength, tensile strength, refractive index, transmittance, cost, and environment. In this investigation, twelve alternative materials and ten criteria used for material selection for the optimal design. The outcomes revealed that crystal glass has the highest grey grade of 0.588, which is the optimal glazing material among the twelve alternatives.

Microstructure and mechanical performance of (AlCrNbSiTiV)N films coated by reactive magnetron sputtering

(AlCrNbSiTiV)N films are prepared on cermet cutter tool substrates using direct current reactive magnetron sputtering with a high-entropy alloy target. The use of a grey based Taguchi method to determine the deposition parameters of (AlCrNbSiTiV)N films has been studied by considering multiple performance characteristics. Taguchi quality design concept with an \(\hbox {L}_{9}\) (\(3^{4}\)) orthogonal array, signal-to-noise ratio and analysis of variance are used to determine the performance characteristics of the deposition process. The effect of various deposition parameters of (AlCrNbSiTiV)N films on the chemical composition, the microstructure, the morphology, the mechanical performance and cutting performance of the coated cutter in dry machining is determined. The experimental results show a cutter that is coated with (AlCrNbSiTiV)N exhibits less surface roughness (for workpiece) and flank wear (for cutter), so coated cutter tools have a much longer tool life than the uncoated tools. Transmission electron microscopy patterns confirm that the (AlCrNbSiTiV)N films have a polycrystalline face-centred cubic structure. In the confirmation runs, using grey relational analysis, there are improvements of 3.7% in surface roughness, 69.6% in flank wear and 3.2% in elastic recovery. The Rockwell adhesion test categorizes the coatings as class HF1.

Multi response optimization of process parameters using grey relational analysis for milling of hardened Custom 465 steel

Abstract This study focused on optimizing the process parameters during end milling operation of hardened Custom 465 steel with multi-response criteria based on orthogonal Taguchi matrix with grey relational analysis. Nine experimental tests based on L9 orthogonal network of the Taguchi method have been carried out using Titanium aluminium nitride (TiAlN) coated carbide inserts under a dry environment. Cutting speed, feed rate and depth of cut are optimized by taking various multiple performance characteristics such as cutting force, temperature, surface roughness and material removal rate. Grey relational analysis is a method for analyzing the relationship between sequences using less data with multiple factors and is considered helpful to statistical regression analysis. Based on the grey system theory, the grey relational analysis can be used to solve complicated interdependence of parameters among multiple performance characteristics effectively. A grey relational grade (GRG) is determined from the grey analysis. Optimum levels of parameters have been identified based on the values of grey relational grade to solve the end milling process with multiple performance characteristics. In addition, analysis of variance (ANOVA) is also applied to identify the most significant factor influencing the machinability. Finally, comparisons were made between the experimental results and the predicted model developed. Experimental results have shown that the machining performance in the milling process can be improved effectively with this approach.

A fuzzy optimisation approach for software reliability estimation

Many organisations today utilise information technology to gain competitive advantage. As a result, information technology industry has witnessed extraordinary growth. Many times the software companies compromise on software testing to avoid cost or schedule overrun. This might make the software unreliable. Hence, it is imperative for the software firms to estimate the reliability of the software and ensure that their products are reliable. A wide range of models is available for reliability prediction. But there is not a single model suitable for all software products. Hence, it is necessary to identify the best-suited software reliability model for every software product. Unfortunately, the best fit model identification depends on the performance characteristics used for selecting the model. In this paper, the authors suggest a fuzzy optimisation methodology to choose the best software reliability model based on multiple performance characteristics. Two case studies demonstrating the proposed methodology are also presented in the paper.

Multi-response optimization of 67Ni18Cr5Si4B coating by HVOF spray using Taguchi-OEC technique

High Velocity Oxygen Fuel (HVOF) spray is being widely used in many industries to deposit erosion-resistant coatings or restore the dimension for components. The porosity, adhesion strength, and hardness are the essential properties commonly used to access the quality of coatings and thus the effectiveness of a HVOF spray process. To achieve desired coating properties and reduce production cost, optimization of process parameters in HVOF spray has developed immensely during last decades. Among the spray parameters, particle velocity, powder feed rate and standoff distance contribute a significant influence on the final properties of the coatings. In the present study, experiments of HVOF thermal spray were prepared for the 67Ni18Cr5Si4B alloy powder. The process parameters including spray velocity, powder feed rate and spray distance were optimized for the objectives of adhesion strength, porosity and microhardness of 67Ni18Cr5Si4B coating. Based on the Taguchi method, signal-to-noise (S/N) ratio and overall evaluation criteria (OEC) are determined to solve the optimization problem considering multiple performance characteristics. Moreover, analysis of variance (ANOVA) was conducted to analyse the contribution percentage of each process parameter. The optimal process parameters in HVOF spray of 67Ni18Cr5Si4B were found for the multi-response optimization as velocity of 1000 m/s, powder feed rate of 30 g/min, and spray distance of 0.2 m. The results of ANOVA indicated that the spray distance was the most significant parameter affecting the output spray quality among the investigated process parameters, subsequently to the powder flow rate and velocity of spray. Finally, a confirmatory experiment was carried out to support the accuracy of optimal process parameters.

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.

Removal of metribuzin by sulfate radical‐based photooxidation: multi‐objective optimization by central composite design

AbstractThe objective of this study was to optimize the experimental conditions for maximum removal efficiency and minimum operating cost for removal of metribuzin pesticide from aqueous solution. Central Composite Design model was applied as Response Surface Design. In the model, independent parameters were ultraviolet irradiation time, persulfate anion dosage, TiO2 dosage, pH and response parameters were metribuzin removal (%) and operation cost. Analysis of variance (ANOVA) was used to describe the significance level of factors on the multiple performance characteristics considered. It has been found that the most suitable model is the quadratic model to explain the both responses and the correlation coefficient (R2) of the relationship between the actual metribuzin removal and the predicted removal was calculated as 0.9518. As a result of multi‐optimization, removal efficiency of 84.59 (%) and operation cost of 4.44 (€/g) were obtained at ultraviolet irradiation time at 12.03 min, persulfate anion concentration at 4.909 g/L, TiO2 concentration at 0.197 g/L and pH at 4.