Optimal Combination Of Input Parameters Research Articles

Determination of wear rate and coefficient of friction of Al6262 reinforced with different weight percentage of WC/MoS2 under dry sliding condition

A tribological investigation was undertaken on hybrid metal matrix composites based on Aluminum alloy 6262, incorporating varying weight percentages of Tungsten Carbide (WC) and Molybdenum disulfide (MoS2) under dry sliding conditions. Specifically, Tungsten Carbide was incorporated at 3%, 6%, and 9%, while Molybdenum disulphide was introduced at 2%, 4%, and 6%. The fabrication of these hybrid composites was accomplished using the stir casting technique. The experimental design followed an L27 orthogonal array, and Taguchi optimization was employed to identify the optimal combination of input parameters. The orthogonal array, signal-to-noise ratio and analysis of variance were employed to study the optimal testing parameters on developed composites. The optimal formulation, resulting in the minimum wear rate and coefficient of friction, was determined to be 9% WC, 6% MoS2, a load of 10N, a sliding velocity of 1 m/s, and a sliding distance of 400 m. Characterization was carried out for Al6262/WC/MoS2 hybrid composite by using scanning electron microscopy (SEM).

WASPAS Based Multi Response Optimization in Hard Turning of AISI 52100 Steel under ZnO Nanofluid Assisted Dual Nozzle Pulse-MQL Environment

Hard turning is an emerging machining technology that evolved as a substitute for grinding in the production of precision parts from hardened steel. It offers advantages such as reduced cycle times, lower costs, and environmental benefits over grinding. Hard turning is stated to be difficult because of the high hardness of the workpiece material, which causes higher tool wear, cutting temperature, surface roughness, and cutting force. In this work, a dual-nozzle minimum quantity lubrication (MQL) system’s performance assessment of ZnO nano-cutting fluid in the hard turning of AISI 52100 bearing steel is examined. The objective is to evaluate the ZnO nano-cutting fluid’s impacts on flank wear, surface roughness, cutting temperature, cutting power consumption, and cutting noise. The tool flank wear was traced to be very low (0.027 mm to 0.095 mm) as per the hard turning concern. Additionally, the data acquired are statistically analyzed using main effects plots, interaction plots, and analysis of variance (ANOVA). Moreover, a novel Weighted Aggregated Sum Product Assessment (WASPAS) optimization tool was implemented to select the optimal combination of input parameters. The following optimal input variables were found: depth of cut = 0.3 mm, feed = 0.05 mm/rev, cutting speed = 210 m/min, and flow rate = 50 mL/hr.

Optimal combinations of parameters for seismic response prediction of high-speed railway bridges using machine learnings

This study aims to determine the optimal number and combination of input parameters from machine learning (ML) techniques, encompassing both earthquake and bridge parameters, for effectively and efficiently predicting the seismic response of high-speed railway bridges (HSRBs). 14 earthquake parameters and 37 bridge parameters were carefully selected. To mitigate multicollinearity among earthquake parameters and identify the most influential bridge parameters, correlation matrix analysis and the “Weight” method were employed, respectively. ML models were trained using data obtained from finite element analyses of 1000 bridge-ground motion pairs. The forward floating selection algorithm sequentially (FFSAS) and the backward floating selection algorithm sequentially (BFSAS) were utilized to determine the optimal combination of ML inputs for HSRBs. The analysis results demonstrate that the ML model, when utilizing the optimal input combination, yields accurate predictions of seismic response for HSRBs under various ground motion types. The predictive performance of machine learning models improves with more input parameters, but this particular model achieves stability with only six parameters. The optimization of this parameter combination is significantly influenced by factors such as the model type, evaluation criteria, and characteristics of the bridge components. FFSAS and BFSAS methods are both applicable for identifying the optimal combination of input parameters in machine learning models.

Optimization of multi‐response biodiesel fueled diesel engine parameters using Taguchi grey relational analysis and analytical hierarchy process

AbstractCompression ignition engines are currently optimized to run on petrodiesel fuel, but due to its increasing demand and depletion of fossil fuel reserves, alternative fuels must be utilized. In this article, an attempt was made to optimize the operating parameters of an unmodified compression ignition engine fueled with linseed methyl ester biodiesel. The input parameters were compression ratio, load, and fuel blend, while output responses included brake‐specific fuel consumption (BSFC), brake thermal efficiency (BTE), and exhaust gas temperature (EGT), peak cylinder pressure (PCP), the rate of pressure rise (RPR), and net heat release rate (NHR), hydrocarbon (HC), carbon monoxide (CO), NOx, and Smoke. The aim was to determine the optimal combination of input parameters for minimal emissions and better performance and combustion parameters, with a minimum number of experiments. The study used Taguchi's L9 orthogonal array with a grey relational analysis approach (GRA) to obtain the optimum combination of input parameters. The study found that fuel blend B10, CR 18, and load 100% were the optimal input parameter combinations using the TGRA method. The S/N ratio analysis of grey relational grade (GRG) showed the optimal input factor levels to be fuel B10, CR 16, and load 100%, which was confirmed by the AHP method. The analysis of variance (ANOVA) showed that the load was the most influential factor, followed by CR and fuel. The confirmatory results showed an improvement of 56.1%, indicating that biodiesel can be efficiently used in an unmodified compression ignition engine by varying these influencing factors.

Predicting and optimising the surface roughness of additive manufactured parts using an artificial neural network model and genetic algorithm

The selection of parameters affects the surface roughness in the additive manufacturing process. This study aims to determine the optimal combination of input parameters for predicting and minimising the surface roughness of samples produced by Fused Deposition Modelling on a 3D printer using a cascade-forward neural network (CFNN) and genetic algorithm. Box–Behnken Design with four independent printing parameters at three levels is used, and 25 parts are fabricated with a 3D printer. Roughness tests are performed on the fabricated parts. Models generated by the hybrid algorithm achieve the best results for predicting and optimising surface roughness in 3D-printed parts. The surface roughness prediction accuracy of the trained CFNN with optimised parameters is more accurate compared to previous random test results.

Experimental Investigation of EN-31 Steel Using VMC

This research paper presents an experimental investigation using the Vertical Machining Centre (VMC) for machining the EN31 material. The study looks at how different input factors, including feed, speed, and cut depth, affect the output factors, like the material removal rate (MRR) and surface roughness (SR). The Taguchi method is used as the optimization technique to identify the optimal combination of input parameters that lead to maximum MRR and minimum SR. The objective of the study is to establish the relationship between input and output parameters and to offer a workable strategy for machining process optimization. The study's findings will advance our understanding of EN31 material machining with VMCs and have real-world implications for the machining sector.

Early monitoring of gas kick in deepwater drilling based on ensemble learning method: A case study at South China Sea

Gas kick monitoring is of great significance for prevention of blow-out accidents, especially in deep drilling and deep-water drilling. In this study, a machine learning (ML) model for early-monitoring of gas kick is developed using the ensemble learning algorithms based on 7363 lines of drilling logging data at South China Sea. The selected input parameters based on mechanism analysis of gas kick are six fast engineering parameters, including hook load (WHO), weight on bit (WOB), torque (TOR), flow rate (FLW), rate of penetration (ROP) and stand-pipe pressure (SPP), and two slow mud property parameters, i.e. electrical conductivity (CON) and mud outlet density (DEN). The model is constructed using RUSboosted, Subspace-KNN and Bagged Trees algorithms, and is compared with the neural network algorithm. We propose a comprehensive error to quantitatively evaluate the performance of the gas kick monitoring models. The models for early-monitoring of gas kick are applied for a single well and multiple wells, respectively. The results indicate that: (i) The optimal combination of input parameters is made up of six fast engineering parameters and two slow mud parameters. When there is a higher requirement on timeliness, only use of the six fast engineering parameters is also acceptable. (ii) The ensemble learning models work well when the input data expand from single well to multiple wells in the same block. For most cases, the prediction error of the optimal model is below 10%. The RUSboosted algorithm performed best in most data sets. (iii) Gas kick identification from lots of drilling logging records is mathematically a small-sample problem. The output labelling of a potential gas kick should be based on the field practical requirement. The recommended positive length of continuous-point labelling method is 5 m for the studied area, which can effectively reduce the average error from 8.02% to 5.48%.

Study of burr width and height using ANOVA in laser hybrid micro milling of titanium alloy (Ti6Al4V)

Ti6Al4V is regarded as the “workhorse” in aerospace industry for its wide applications at high temperatures. Unique attributes are high strength to density ratio and outstanding material properties at high thermal conditions. This present-day work involves the pre-slotting on Ti6Al4V surface with the help of fibre Laser, followed by the operation of micro-milling on respective Laser engraved slots. Localized melting due to Laser ablation leads to the decrease in the surface contact between milling tool and material surface. Micro-milling in return helps to remove the Heat affected zones (HAZ) created during Laser engraving process. ANOVA helps to point out the contributing factors for burr width and height on both milling sides. Main effect plots generated during process optimization, help to point out optimal combination of input parameters for minimized burr width and height. Higher frequency of Laser helps to generate consistent slots. Lower rotational speeds are responsible for high values of burr width and height. Whereas high rotational speed leads to efficient chip removal, resulting in decreased burr width and height.

Exploring the influence of critical parameters on green supply chain management performance of small and medium-sized enterprise: An integrated multivariate analysis-robust design approach

The implementation of green supply chain management (GSCM) practices is still in its rudimentary stage in small and medium-sized enterprises (SMEs) of emerging economies. So far, too little research has shown clear empirical evidence on the actual impact of various parameters on GSCM performance of SMEs. In order to fill this knowledge gap, this research attempts to identify the critical GSCM parameter and quantitively measure its influence on the GSCM performance of an India-based SME. To this end, twenty important GSCM parameters are identified from an extant literature and verified by the experts. Data is collected through questionnaire development, expert committee formation, and conducting interviews. This research employs an integrated multivariate analysis-robust design approach, in which item analysis is used to check the consistency of the data and principal component analysis (PCA) is used to determine the weights of the parameters. The Taguchi’s L9 orthogonal array analysis is used in which, qualitative data, quantitative data, and attitude of decision maker are taken as input parameters and a GSCM performance index is taken as a response parameter. The result reveals that, ‘Use of emission control system’ is the most critical GSCM parameter and the optimal combination of input parameters are: use of the emission control system (0.08); total carbon emission (0.37); attitude of decision maker (0.6), and the corresponding GSCM performance index is 0.196. The proposed framework may help decision makers identify and control a set of influential parameters for the best GSCM performance.

Experimental investigations and parametric optimization during micro-EDM drilling of Ti -5.6Al-3.6V using ABC algorithm

This work presents experimental investigations and parametric optimization during micro-Electric discharge Machining (EDM) drilling of Titanium alloy Ti-5.6Al-3.6V. The experiments have been designed by response surface methodology (RSM) based central composite design (CCD) taking current, pulse-on-time and pulse-off-time as input parameters; and drill rate and tool wear ratio as performance measures. After carrying out experiments, the effect of each input parameter on performance measures has been found. In order to study the microstructure of machined surface, scanning electronic microscope (SEM) has been performed. Single objective and multi-objective optimization have been done using artificial bee colony (ABC) algorithm to find the optimal combination of input parameters for the best yield of the process. Experimental verification of the obtained results has also been performed and a difference of less than 5% has been observed between experimental values and that obtained using ABC algorithm.

Prediction of bead geometry parameters in MIG welded aluminium alloy 8011 plates

In today's fabrication industry, the MIG (metal inert gas) welding procedure is widely employed because of its versatility, adaptability to automation and good quality welds in a majority of engineering materials. Weld bead geometry of a weld determines its strength, mechanical properties and its performance during service life. It is therefore imperative to optimise these parameters for a reliable weld. An attempt is therefore being made in the present work to find a mathematical connection between the important bead Input parameters including wire feed rate, voltage, welding speed, nozzle to plate distance, and torch angle, and depth of penetration (DOP), weld width (W), height of reinforcement (HOR). The design matrix for the tests was created using the statistical approach of central composite rotatable design (CCRD). The analysis of variance (ANOVA) approach was utilized to evaluate the adequacy of the obtained equation, and response surface methodology (RSM) was employed for graphical interpretation of the findings. Weld bead geometry deeply influences the mechanical strength and quality of the welded joint; therefore it must be of the required dimensions. It has been found that bead geometry depends upon the value of input parameters. To get the desired values of the bead parameters, an optimal combination of input parameters was found.

Mathematical analysis of effect of process parameters on weld bead geometry of MIG welded low carbon steel plates

The weld bead geometry of a fusion-welded workpiece decides its mechanical strength and its performance in service. Important bead parameters are bead width, reinforcement height, and penetration depth. A certain combination of these parameters is essential for the weld to sustain the external loads safely when put to use; therefore, it’s an endeavour of a weld engineer to select these parameters wisely. These bead parameters are predominantly found to depend on the values of input parameters. An optimal combination of input parameters is thus required to have desired bead parameters. The goal of this study is to create a mathematical model that relates bead properties to input parameters in MIG welding of mild steel plates. Different effects of these parameters are also analysed graphically. The model developed by the Taguchi approach is expected to help predict the angular distortions.

Mathematical Modelling to Predict Angular Distortion in Butt-welded Aluminium 1200 Plates

MIG welding is an inert gas shielded arc welding process using a consumable electrode. The process finds huge applications in various engineering industries due to its versatility and its ability to get automated. In the present work, angular distortion (AD) occurring in the butt welds has been analysed using mathematical modelling. The important input parameters selected are wire feed rate (WFR), welding speed, and voltage. These variables are taken at three levels to analyse their effect on AD. The investigation was carried out using a statistical design of experiments technique. The ANOVA technique is employed to evaluate the mathematical model's adequacy. The graphical results were evaluated using the response surface methodology (RSM). The optimal combination of input parameters was determined to have minimum possible AD.The present study is expected to provide useful results which may prove beneficial for the aluminium fabrication practices.

Novel machine learning approach for solar photovoltaic energy output forecast using extra-terrestrial solar irradiance

The inherently intermittent nature of solar irradiance and other meteorological variables means that accurate forecasting of the photovoltaic power output is essential for planning and balancing photovoltaic power systems. This study proposes a novel approach to predicting one-week-ahead half-hourly photovoltaic power output in the United Kingdom using sloped extra-terrestrial irradiance and weather data (e.g., cloud-cover and temperature) as input parameters. A Non-linear Autoregressive Exogenous Neural Network is trained on a three-year historical dataset from two photovoltaic plants in the United Kingdom with capacities of 53 and 103 MWp. The forecasting model captures huge intra-daily variations of photovoltaic output, which is particularly useful to balance the supply and demand of the electricity system. The result of the study validates the concept of using sloped extra-terrestrial irradiance as an input parameter and suggests that meteorological conditions will dictate the accuracy of predictions. Findings also indicate that the use of sloped extra-terrestrial irradiance in conjunction with cloud-cover presented the optimal combination of input parameters as these provided the simplest and most cost-effective model without reducing accuracy. The approach can have universal value as it only requires coordinates and weather data. There is now a strong imperative to use the model in other locations where the weather is more stable.

Optimization of Drilling Parameters During Machining of Al-Mg-Si Alloys By Taguchi Method Coupled with Grey Relational Analysis and Validated By Fea Based Deform - 3D

Abstract In the present work to validate the experimental results as per Taguchi based grey relational analysis by considering L27 orthogonal array corresponding to five factors with three levels and obtained optimal combination of input parameters to minimize the output responses during drilling process which is most important finishing operation required in the structural assembly works where we found application of Al-Mg-Si alloys, Deform-3D software is implemented and found good feasibility with that of experimental results.

Impact of dry sliding wear parameters on the wear rate of A7075 based composites reinforced with ZrB2 particulates

The dry sliding wear behavior of A7075 based composites strengthened with particulates of ZrB2 was examined using the pin-on-disc device. The composites were synthesized by reinforcing different weight % (5, 10, and 15) of the ZrB 2 using a stir casting technique followed by hot extrusion. The dry sliding wear resistance is optimized under varying conditions of sliding velocity, sliding time, and load. The sample's worn-out surfaces were examined using scanning electron micrographs to obtain the composites' wear mechanism. Delamination, abrasion adhesive modes of deformation were observed. The wear parameters were optimized with Grey relational analysis (G.R.A.) and ANOVA techniques and obtained the optimal combination of input parameters.

ANFIS-ARIMA modelling for scheming re-aeration of hydrologically altered rivers

The discharge of a significant amount of wastewater from the urban agglomerations in the rivers is the primary cause for the degradation of the river’s assimilative capacity. The re-aeration process represents an interaction among the air–water interface to absorb oxygen from the atmosphere and indicates the capacity of the water to hold the oxygen that can be used in the degradation of pollutants without affecting the river’s health. Therefore, accurate estimation of re-aeration capacity is the prime requirement to maintain the riverine ecosystem. In this paper, an adaptive neuro-fuzzy inference system (ANFIS) is used to simulate the non-linear behaviour of various water quality parameters by estimating the re-aeration coefficient of Yamuna River, Delhi. The hydraulic and water quality parameters are used to identify the most optimal combination of input parameters for simulation through the Takagi-Sugeno (TS) ANFIS model. The results of ANFIS are ensembled with the time series autoregressive integrated moving average (ARIMA) model to minimize bagging, non-stationarity and biasing. The performance of the ANFIS models was measured using the coefficient of determination (R2), root mean square error (RMSE), and the coefficient of correlation (R). The ARIMA models are evaluated based on standard error, p-value, and t-test. Out of the five different ANFIS models, ANFIS-5 provides the optimal values of R during training and testing of the model as 0.966 and 0.989, respectively, and integrated with the ARIMA model to obtain the re-aeration coefficient for each sampling location. The results indicate that the ensembled ANFIS-ARIMA model reduces the variance significantly with the minimum error of 0.0075 and produces an exceptional improvement in predicting the re-aeration coefficient.

Multi-objective dynamic optimization of seeded suspension polymerization process

The temperature profile of the seeded suspension polymerization process was optimized to maximize molecular weight, shell thickness and monomer conversion ratio of core–shell polymer particles. Extreme learning machine radial basis function neural networks with R2 values greater than 0.93 were developed, to predict polymer properties at any point in time, using data generated by a computational fluid dynamics model. The optimal combination of input parameters for each neural network was selected from a pool of 44 variables, by using a weight-based method that uses a support vector regression model, and a global exhaustive search algorithm, consecutively. The neural networks developed were incorporated into a genetic algorithm that maximizes the molecular weight, monomer conversion ratio and shell thickness. The optimum temperature profile generated by the algorithm satisfactorily maximized all target polymer properties. This study also demonstrates that a support vector machine classifier could be reliably used for imposing nonlinear inequality constraints for solving dynamic optimization problems.

Optimization of dry sliding wear parameters of Al4Mg system reinforced with high strength alloy particulate (HSAp)

The dry sliding wear behaviour of Al4Mg binary alloy and Al4Mg reinforced with Al20Cu20Mg composites was examined using pin on disc apparatus. The composites were synthesized by the reinforcement of different weight % (5, 10 and 15) of the high strength alloy particulate (HSAp) in the base binary alloy Al4Mg using stir casting technique followed by hot extrusion. In this way, composites were prepared with metal particulate reinforcement which are termed as metal-metal composites which is a novel idea instead of reinforcement of ceramic particulate. These extrudates of non-reinforced base alloy and composites were characterized to optimise dry sliding wear parameters, wear resistance and coefficient of friction under the load conditions of 0.5, 1 and 1.5 kgf at the sliding velocities is of 100, 120 and 140 metre per second and sliding times of 15, 30 and 45 seconds respectively. The wear parameters were optimised using Grey relational analysis (GRA) and ANOVA techniques and obtained the optimal combination of input parameters.

Optimization of diesel engine performance and emission parameters of Karanja biodiesel-ethanol-diesel blends at optimized operating conditions

This study reviews Karanja biodiesel, ethanol and diesel blending in context of Indian subcontinent and also focuses on its effects on engine’s performance, emission and combustion responses. Experimentations carried out on four stroke single cylinder diesel engine at constant speed 1500 rpm and injection pressure 210 bar. The four input parameters as Injection angle, Compression ratio, Blend % and loads varied to obtain engine responses as Brake thermal efficiency, Brake specific fuel consumption, Exhaust gas temperature, Carbon dioxide, Carbon monoxide, Nitrogen Oxide and hydrocarbon. Taguchi method was applied to optimize the trials using L9 orthogonal array and to get main effects S/N ratio curves, for optimal combination of input parameters to response. The ANNOVA was used to establish the contribution of input parameters to responses. Further single combination of input parameters for all seven responses as Injection Angle 19°CA bTDC, Compression ratio 18, Krishna 30 (fuel blend) and 50% load was obtained by Gray Relational Analysis. Experimentation revealed marginal reduction of brake thermal efficiency about 2%, Brake specific fuel consumption higher by 3%, Exhaust gas temperature increases by 3%, Carbon dioxide increases by 0.86%, hydrocarbon decreases by 12 PPM, carbon monoxide and Nitrogen oxide reduced by 0.029% and 8% respectively when compared with diesel at full load. The combustion parameters as Cylinder pressure, Ignition delay, Combustion duration, Start of combustion, End of combustion, Rate of pressure rise, Mean effective pressure and Net heat release rate were observed at full load, also support optimal combination of input parameters.