Multi-response Performance Index Research Articles

Experimental Design and Analysis of MWCNT, Nanoclay and Zinc Oxide Nanofillers on Bi-directional-Chopped Stranded Glass Fibre Reinforced Epoxy matrix on Mechanical Properties using Multi Response Performance Index Approach

Experimental Design and Analysis of MWCNT, Nanoclay and Zinc Oxide Nanofillers on Bi-directional-Chopped Stranded Glass Fibre Reinforced Epoxy matrix on Mechanical Properties using Multi Response Performance Index Approach

Prediction of Optimal Parameter Settings and Significant Parameter for Reduced Geometric Deviations Through Multi Criteria Decision Making and Machine Learning Algorithms

Part dimensional inaccuracies serve as a barrier from adopting Additive Manufacturing (AM) processes in mass production. Fused Deposition Modeling (FDM) is a thermoplastic based low cost AM process which can create conceptual models, prototypes and end user industrial parts. The current study involves predicting the optimal parameter settings and significant parameter for reduced geometric deviations in printed part using Nylon filament reinforced with 20% carbon fiber. Five input factors such as build orientation, layer thickness, infill density, raster angle and infill pattern have been considered for preparing the experimental layout through taguchi�s mixed fractional factorial design. The changes in length, width and thickness of the printed part from CAD value have been evaluated individually through ANOVA and Signal to Noise Ratio method (Smaller the better). Layer thickness is significant only for variations in length, but build orientation affects both width and thickness dimensions. The geometric deviations are further analyzed using combined multi criteria decision making (MCDM) approaches such as Entropy-CoCoSo and PCA-TOPSIS. The optimal parameter settings obtained for reduced geometric deviations is found to be Flat orientation, 0.1mm layer thickness, 50% infill density, 0� raster angle and cubic infill pattern. Layer thickness is found to be highly significant parameter influencing the geometric deviations subsequently followed by build orientation from both the MCDM methods. The multi response performance index values obtained from Entropy-CoCoSo has been trained using classification algorithms such as decision tree, random forest and Naive Bayes. Naive Bayes algorithm outperformed other methods with highest classification accuracy of 99.4% in a training-testing split ratio of 75:25.

Optimization of TIG welding process parameters on 304 austenitic stainless steel sheet metal using fuzzy logic based Taguchi method

TIG welding can be used to produce excellent weld quality and precise welding operation for sheet metals. The aim of this study was to get the best welding parameters for enhancing ultimate tensile strength, bending strength and Rockwell hardness of the butt-weld joint. The experimental work was used, and the experiment was carried out on 2 mm thickness of 304 austenitic-stainless steel sheet metal using the L9 orthogonal array of the Taguchi design. Automated TIG welding fixture was developed to control the welding speed accurately. The selected welding parameters were welding speed, current, voltage and gas flow rate with their three levels. Based on the fuzzy logic based Taguchi method, the best optimal levels of parameters were found at the values of 110 A of current, 13.86 cm/min of speed, 17.5 V of voltage and 7.5 L/min of gas flow rate. The analysis results of ANOVA showed that gas flow rate and current were found as the significant factors, and the contributions of the gas flow rate, current, speed and error were 47.63%, 34.34%, 16.49% and 1.54%, respectively. According to the confirmation tests, the multi response performance index mean value of the confirmatory test of 0.6068 was found between the 95% confidence interval of 0.5028 and 0.7314, and the maximum ultimate tensile strength, bending strength and Rockwell hardness were obtained 614.8 MPa, 765.32 MPa and 95.3 HRB, respectively.

Optimisation of process parameters for dimensional stability in FDM

The fused deposition modelling (FDM) technique has been adopted in the present work to fabricate Acrylonitrile Butadiene Styrene (ABS) plastic components using input variables like layer thickness, orientation, infill rate and the number of shells. The dimensional geometrical changes of FDM printed built parts, that is, length, width and thickness variation, are optimised individually. Still, a confounding effect is found in the optimisation of individual responses. To overcome this issue, this study uses a fuzzy inference system as an artificially intelligent system. A fuzzy inference system transformed all the responses into one response, known as a multi-response performance index (MPI), and optimised the MPI coupled with Taguchi philosophy. The optimal parameter combination of FDM-printed ABS components for the slightest variation in length, width, and thickness was found to be layer thickness: 200 µm, orientation: 0°, infill rate: 20%,and the number of shells: 3. The significant parametric effect on the responses of FDM components was studied using analysis of variance, and the results reveal that orientation is the most influential parameter trailed by infill rate and the number of shells.

Fuzzy logic optimization with regression analysis on EDM machining parameters of Si3N4-TiN ceramic composites

Electro discharge machining (EDM) is a cycle for molding tough materials and framing profound contour formed openings by warm disintegration in all sort of electrically conductive materials. The goal of the venture to be concentrating because of working parameters of EDM for machining of silicon nitride-titanium nitride in the machining qualities with copper electrode, for example input Spark on time (Son), current (Ip), Spark off time (Soff), spark gap and dielectric pressure on the metal removal rate (MRR) and Electrode Wear Rate (EWR) were analyzed. Subsequently, using Taguchi analysis of various plots like Mean effect plots, Interaction plots, and contour plots, performance characteristics are looked at in relation to multiple process factors. Fuzzy logic and Regression analysis is utilized to combine various reactions into a solitary trademark record known as the Multi Response Performance Index (MRPI).The trial and anticipated qualities were in a decent programming instrument for discovering the MRPI esteem. For numerous performance aspects, such as material removal rate, electrode wear rate and so on, the optimal process parameter combination was established using fuzzy logic analysis. The key process factors, which included spark off time and current, were found using an ANOVA based on a fuzzy algorithm. Topography on machined surface and cross-sectional view of conductive Si3N4-TiN composite and surface characteristics of machined electrode is examined by SEM analysis and identified the best hole surface and worst hole surface. Sensitivity analysis is being utilized to determine how much the input values, such as Ip, Son and Soff, will need to alter in order to get the desired, optimal result. In the complexity analysis, each constraint of the machine, composite and process is addressed. Future researches might look into various electrodes to assess geometrical tolerances including angularity, parallelism, total run out, flatness, straightness, concentricity, and line profile employing other optimization methodologies to achieve the best outcome. The findings of the confirmatory experiment have been established, indicating that it may be feasible to successfully strengthen the spark eroding technique.

A hybrid approach consisting of multi-objective and multivariate analyses for WAAM specimens

In this study, a combination of multivariate and multi-objective analyses, namely principal component analysis with fuzzy logic, has been proposed to identify the input parameters for ER4043, aluminum alloy in wire arc additive manufacturing. Taguchi L9 orthogonal array was designed with three-factor, three-level input parameters. Mechanical properties such as tensile strength and hardness were used as the output parameters. As the Taguchi optimization is not sufficient for multi-response optimization, observed data for each response were normalized and then principal component analysis was applied. Further, multi-response performance index (MRPI) was identified using fuzzy logic approach so that this hybrid approach could be successfully applied and MRPI could give a combination of input parameters at an optimal level.

Multi-performance characteristics optimization in near-dry rotary EDM of AlSiC by weighted principal component analysis

In the current study, electrical discharge machining of particle reinforced aluminium matrix composite (AlSiC) using near-dry conditions is investigated. The investigation has been carried out by using de-ionized water as liquid phase and air as gaseous phase of a two-phase dielectric fluid. The gap between the work material and the tool is supplied with a little amount of de-ionized water droplets at a slow, steady pace using a minimum quantity lubrication (MQL) device. The process parameters included in the study are peak current, duty cycle, pulse-on time, tool rotation and air pressure. MRR (Material removal rate), TWR (Tool wear rate) and HT (Hole Taper) were selected as evaluation criteria. L27 (313) orthogonal array was used to conduct the experiments. The experimental results obtained were used in principal component analysis (PCA). The MPI (multi-response performance index), which combines these principal components and weighted principal component analysis (WPCA), is calculated as overall performance index. The approach is capable of forecasting optimal levels of machining factors. Significant factors and their contribution is determined by ANOVA performed for MPI. To validate the optimum parametric settings confirmatory experiment was conducted. Experimental results show that the responses in near dry rotary EDM process can be successfully improved using the suggested method.

Process parameter optimisation of fused deposition modelling for augmenting the strength of nylon-aramid PMC’s

In mechanical parts and components, the strength of the material plays a vital role for its effective functioning. For certain individual materials, the enhancement on its quality of strength must be performed at the initial development stage and can rarely be performed using post processing techniques. 3-D printed Polymer Matrix Composites (PMC’s) are one such material. The current research focuses on augmenting the strength of nylon-aramid PMC’s, using Fused Deposition Modelling (FDM) 3-D printing technique. The 3-D printed material was tested according to ASTM standards for mechanical properties such as compression, tensile, flexural and impact strength. Since the association amongst the mechanical properties and the processing parameters of FDM is challenging to verify, an effort was attempted to obtain an empirical formula using the Taguchi Technique of Optimisation. The influencing processing parameters in the FDM technique, such as infill part, layer thickness, print temperature, raster angle, infill pattern style and density were optimised using Taguchi analysis combined with Multi-Response Performance Index (MPI), to enhance the Tensile, Flexural, Impact and Compression Strength, thereby augmenting the total strength of the FDM component. Thedevelopment of the FDM component used these optimised levels again. The mechanical properties were validated according to the ASTM standards of testing. The optimisation technique using T-DoE indicated that the similarity between the computed values from the regression equation and the measured with minimalistic deviations. The research concluded that for a print temperature of 300 °C, layer thickness of 0.4 mm, raster angle of 90°, infill part density of 90 %, and having a rectilinear style of infill pattern, provided an optimal levels of process parameters for augmenting the strength of 3-D printed Nylon-Aramid PMC’s.

Process monitoring in submerged abrasive waterjet cutting of Ti6Al4V by vibration signal

ABSTRACTThe submerged AWJ reduces the supersonic cutting noise, reduces the divergence of the jet, and improves the performance characteristics, but affects the visibility of the workpiece. Hence, this work deals with the monitoring of the combined effect of responses, termed as the multi-response performance index (MRPI), by vibration signals. Firstly, the unsubmerged and submerged AWJs are compared, and the submerged condition gave an average improvement of 1.9 mm in depth of cut, 2.1 mm in smooth cutting zone, 1.03 mm in kerf width, and 1.4 µm in roughness. Furthermore, the TOPSIS method is applied to combine the aforementioned responses into MRPI. The wavelet transform (WT) was used to extract information from the vibration signals, and the features selected by principal component analysis were fed as inputs to ANN and ANFIS techniques. In ANN, 20 architectures were compared for the prediction of MRPI, and the architecture 4-8-4-1 gave a minimum mean absolute error (MAE) of 0.065, and ANFIS gave a minimum MAE of 0.03. Overall, the integrated WT-ANFIS efficiently predicted the MRPI. Finally, the most influential parameters affecting the MRPI were found to be the pressure followed by the transverse rate with contributions of 44% and 21%.

Applying a Taguchi-based fuzzy logic approach to optimize hydrothermal pretreatment of canola seeds using multi-response performance index

The Taguchi method with fuzzy logic was applied for optimizing the hydrothermal pretreatment of canola with multiple performance responses (oil extraction yield, free acidity and peroxide index) using published data. The canola seeds had been subjected to hydrothermal pretreatments using steam in an autoclave whose base was perforated, under different conditions of temperature (100, 120 and 130 °C), time (5, 15 and 30 min) and granulometry (entire, broken and ground seeds), and the responses were measured as performance characteristics of the process. The output value that represents the responses was called multi-response performance index (MRPI), and the significance of the experimental factors was analyzed by ANOVA. A confirmation test of the optimum parameters was carried out to verify the optimum parameters, obtaining a predicted MRPI of 0.588, while the experimental value was of 0.849, and the MRPI calculated using the predicted values from the literature was of 0.577.

Artificial Intelligence System Approach for Optimization of Drilling Parameters of Glass-Carbon Fiber/Polymer Composites

In recent times, the study on machining characteristics of combined (hybrid) fiber polymer composites has drawn a remarkable research attention because of its emerging industrial applications. The present study focuses on the drilling of hybrid glass-carbon fiber reinforced (GCFR) epoxy composites fabricated using hand layup technique. The machining characteristics were considered in the drilling of GCFR composites which include thrust force, torque, delamination factor and surface roughness. The influence of the drilling process parameters such as spindle speed, drill diameter and feed rate on the characteristics are studied. To avoid the confounding effect of the individual optimized characteristics, an artificial intelligence system i.e. fuzzy inference system approach is adopted. The fuzzy inference system transformed all the performance characteristics of drilled hybrid composites into a multi response performance index (MPI) and optimized the MPI at the common factor level setting. The optimal combination of process parameters for minimum thrust force, torque, delamination factor and surface roughness found to be: speed 3000 RPM, drill diameter 5 mm and the feed rate 50 mm/min. The analysis of variance results show that drill diameter is the most significant parameter followed by feed rate and speed. Further, a theoretical model was proposed for the estimation of MPI and found that an average absolute error of 14.8% with respect to the experimental MPI data is obtained.

Multi-objective optimisation of user experience in mobile application design via a grey-fuzzy-based Taguchi approach

User experience is a key quality of mobile application design. It involves all aspects of human–computer interactions, and thus the optimisation of user experience is a multi-objective optimisation problem. User experience is subjective and uncertain; however, very little attention has been directed toward this issue when optimising user experience. In this article, a grey-fuzzy-based Taguchi approach is proposed to optimise user experience in mobile application design. Design analysis is first conducted to determine mobile design patterns and user experience characteristics. Next, a Taguchi experiment is executed, and then the signal-to-noise ratios are computed. After that, the signal-to-noise ratios are transformed into a multi-response performance index via a grey-fuzzy-based analysis. Finally, based on the multi-response performance index, the optimal design is achieved by using statistical analysis. A mobile health application design was employed to illustrate the proposed approach. The results indicate that this approach can effectively manage the subjectivity and uncertainty concerning user experience characteristics, and can be used as a general robust design approach for optimising user experience of mobile application design.

Multidimensional optimization of electrical discharge machining for high speed steel (AISI M2) using Taguchi-fuzzy approach

Electrical discharge machining (EDM) is one of the non-traditional machining processes characterized by its ability to machine parts that electrically conductive but difficult to be machined in the traditional machining processes due to its high hardness, complex geometry, and low tolerances. To minimize EDM process cost, ensure the highest process efficiency and achieve the highest product quality, the EDM process needs to be optimized. The aim of this research is optimizing EDM process parameters for machining HSS (AISI M2) material by use of copper electrode and brass electrode considering conflicting performance measures in one and multidimensional levels. The performance measures used in the current research are material removal rate (MRR) and electrode wear rate (EWR), while machining parameters that will subject to optimization process are current (A), pulse on (TON), and pulse off (TOFF). During optimization stages, the Taguchi method, signal to noise ratio (S/N ratio), and analysis of variances (ANOVA) will be used in the first stage to find the optimal machining parameters for each performance measure and for each electrode material. In the second stage, multidimensional optimization approach encompasses using the calculated S/N ratio as input from the first stage, fuzzy logic and ANOVA to calculate multi response performance index which will be used to select optimal machining parameters for each electrode and then select the best electrode and optimal machining parameters for machining AISI M2 steel. In one-dimensional optimization, for brass electrode, to maximize MRR value, the optimal machining parameters combination is A3TON1TOFF3 and to minimize EWR value, the optimal machining parameters combination is A1TON1TOFF2. For copper electrode, to maximize MRR value, the optimal machining parameters combination is A1TON1TOFF3 and to minimize EWR value, the optimal machining parameters combination is A1TON3TOFF1. In multidimensional optimization, for the copper electrode, the optimal machining parameters combination was A1TON1TOFF3 and for the brass electrode, the optimal machining parameters combination is A3TON1TOFF3. It could be concluded that EDM machining of HSS (AISI M2) by using the copper electrode will give the best results and the optimal machining parameters combination is A1TON1TOFF3. Yet, further research needs to be conducted to study these results and analyze machining process effectiveness in terms of cost and process sustainability, i.e. environmental impact. This research has both theoretical and practical implications.

Multi-objective genetic algorithm in reliability-based design optimization with sequential statistical modeling: an application to design of engine mounting

The present study explores reliability-based design optimization (RBDO) using multi-objective genetic algorithm (MOGA) in the context of practical engineering design. The reliability analysis is carried out using a most probable point-based method with first-order sensitivities. As an effective optimization approach, the two methods MOGA and RBDO are integrated to propose a multi-objective reliability-based design optimization (MORBDO). For considering the uncertainty propagation of reliable constraints, sequential statistical modeling is employed to estimate the appropriate distribution of probabilistic design variables. The present study also applies a multiresponse performance index based on the dimension reduction technique with a quality loss function. After verifying the MORBDO results using simulations on numerical problem, these techniques are applied to an automotive engine mounting system in the hierarchically decomposed problem of minimizing both the difference between the torque roll axis and the elastic roll axis and also the vibration transmissibility under mode purity and frequency requirements. The probabilistic design results indicate that the MORBDO results successfully identify more reliable and conservative optimized solutions than the deterministic results for a given reliability, and the failure probability is compared using a Monte Carlo simulation.

The bioactive efficiency of ultrasonic extracts from acorn leaves and green walnut husks against Bacillus cereus: a hybrid approach to PCA with the Taguchi method

The objective of this study was to investigate the usefulness of the hybrid approach using the Taguchi method (TM) and principal component analysis (PCA) in determining the optimum conditions for ultrasonic extracts of acorn leaves and green walnut husks, which potentially demonstrate the best bioactive efficiency against Bacillus cereus. First, an L36 (21 × 34) mixed level orthogonal array design was implemented, consisting of five factors: extract type, temperature, time, solvent and concentration, respectively. Also, the total phenolic content, antiradical activity, and antibiogram analyses were investigated by design, and signal-to-noise (S/N) ratios were calculated for each trial. An analysis of variance (ANOVA) was performed using S/N ratios to estimate the effects of the measured parameters and their interactions for a single-objective TM. Following that, PCA was used to normalize the S/N ratios for each response to calculate the multi-response performance index for measuring the effects of factors on all responses. In this study, the expected optimal factor levels for each experiment were found to be different for a single-objective TM and inadequate for interpreting all responses simultaneously. The results of the study showed that the optimal conditions for all responses with PCA-based TM was found to be a 5% concentration of acorn leave extract and 50% acetone at 60 °C for 60 min, with 136.96 mg g–1 gallic acid equivalent, 90.75% inhibited 2,2-diphenyl-1-picrylhydrazyl and 14.33 mm inhibition zone against B. cereus.

Surface modification of AZ61 magnesium alloy with nano-Al<SUB align="right">2O<SUB align="right">3 using laser cladding technique: optimisation of wear properties through hybrid GRA-PCA

The aim of this research is to investigate the wear behaviour of surface modified AZ61 magnesium alloy with nanoceramic reinforcement (Al2O3), done by laser cladding technique. Nano-Al2O3 is added in three different weight proportions, viz., 5%, 10% and 15% on to the surface of AZ61 using Nd:YAG laser. Dry sliding wear behaviour of the surface modified AZ61 material is studied considering various reinforcement of nanoceramic particles, and by varying the speed and axial load in the pin-on-disc apparatus. Experiments were designed using Taguchi's design of experiments and the measured outputs, viz., wear, coefficient of friction and frictional force were analysed using grey relational analysis (GRA), coupled with principal component analysis (PCA) for multivariate optimisation by calculating multi-response performance index (MRPI). Using SEM image, distribution of particles in the magnesium alloy matrix is studied. It is observed that the % reinforcement of nanoceramic particles on the surface of AZ61 alloy is the most critical factor that contributes by 79.30% towards the MRPI, followed by 9.91% contribution by load and speed by 7.94%, as determined from analysis of variance (ANOVA). From confirmation experiment, the optimised condition shows an improved wear resistance obtained from the surface modified magnesium alloy.

Surface modification of AZ61 magnesium alloy with nano-Al<SUB align="right">2O<SUB align="right">3 using laser cladding technique: optimisation of wear properties through hybrid GRA-PCA

The aim of this research is to investigate the wear behaviour of surface modified AZ61 magnesium alloy with nanoceramic reinforcement (Al2O3), done by laser cladding technique. Nano-Al2O3 is added in three different weight proportions, viz., 5%, 10% and 15% on to the surface of AZ61 using Nd:YAG laser. Dry sliding wear behaviour of the surface modified AZ61 material is studied considering various reinforcement of nanoceramic particles, and by varying the speed and axial load in the pin-on-disc apparatus. Experiments were designed using Taguchi's design of experiments and the measured outputs, viz., wear, coefficient of friction and frictional force were analysed using grey relational analysis (GRA), coupled with principal component analysis (PCA) for multivariate optimisation by calculating multi-response performance index (MRPI). Using SEM image, distribution of particles in the magnesium alloy matrix is studied. It is observed that the % reinforcement of nanoceramic particles on the surface of AZ61 alloy is the most critical factor that contributes by 79.30% towards the MRPI, followed by 9.91% contribution by load and speed by 7.94%, as determined from analysis of variance (ANOVA). From confirmation experiment, the optimised condition shows an improved wear resistance obtained from the surface modified magnesium alloy.

PCA based hybrid Taguchi philosophy for optimization of multiple responses in EDM

The present study is aimed at a multi-response optimization problem by applying Principal Component Analysis (PCA) combined with Taguchi method. The investigation has been carried out through a case study in Electric Discharge Machining (EDM) of D2 steel by using copper, brass and Direct Metal Laser Sintered (DMLS) electrode produced by direct metal laser sintering using Directmetal20. The research work has been carried out to evaluate the best parametric combination which could fulfill multiple responses like lower Tool Wear Rate (TWR), higher Material Removal Rate (MRR) and lower Surface Roughness (Ra). Unlike the use of single-objective optimization problem in traditional Taguchi method, a hybrid Taguchi method has been developed in combination with PCA to solve multi-objective problem. Taguchi method assumes that the quality characteristics should be uncorrelated or independent which is not always fulfilled in actual condition. PCA is applied to remove response correlation and to calculate independent (uncorrelated) quality indices known as principal components. These principal components combined with weighted principal component analysis (WPCA) are used to calculate overall quality index denoted as Multi-response Performance Index (MPI). This investigation combines WPCA and Taguchi method for forecasting optimal setting. The predicted result by this method was validated through confirmatory test proving the efficacy of the process. Out of five input process parameters considered, tool electrode has been found to be the most significant factors through the Analysis of Variance (ANOVA).

Friction Stir Welding of Aluminum and Magnesium Alloys—Experimental Investigation and Optimization Using Hybrid Grey Relational Analysis and Principal Component Analysis

Friction stir welding of dissimilar materials is investigated experimentally in this work and optimization is performed by applying a hybrid Taguchi-Grey relational analysis-Principal component analysis to maximize the tensile strength and hardness of the weld bead. Two dissimilar metals AA6061 and AZ61 is friction stir welded and considered for the experimentation. Experimental matrix is designed using Taguchi's Design of Experiment (DOE). Optimum inputs rotational speed, axial load and transverse speed is obtained by applying the hybrid optimization technique. Statistical analysis of Multi Response Performance Index (MRPI) through Analysis of Variance (ANOVA) shows that axial load is the significant parameter that contributes by 75.67% towards MRPI, followed by transverse speed and rotational speed. Confirmation experiment with optimum condition produces a better friction stir welding joint with higher tensile strength and hardness.

Role of multi-response principal component analysis in reliability-based robust design optimization: an application to commercial vehicle design

The Taguchi method is a widely used conventional approach for robust design that combines experimental design with quality loss functions. However, this method can be only used in a single-response problem. In this study, we propose the use of principal component analysis (PCA) to consider multi-response problems in the Taguchi method and to investigate the influence factor of a cab suspension system. We compute the normalized quality loss for each response and perform PCA to calculate the multi-response performance index. In this study, control factors with three level combinations and noise factors with random sampling from each normal distribution are considered. Additionally, we applied multi-objective reliability based robust design optimization (RBRDO) to accommodate design uncertainties and its data scattering based on rational probabilistic approaches. This is used to develop the reliability assessment and reliability based design optimization and corresponds to an integrated method that accounts for the design robustness in the objective function and reliability in the constraints.