Multi-response Characteristics Research Articles

Suprahydrostable ultrasmall hybrid cuprous halide with pseudo-net protector for wireless light-triggered rewritable transient display and dynamically recoverable encryption

Zero-dimensional (0D) organic-inorganic hybrid metal halides (OIHMHs) are receiving significant attention due to their exceptional photovoltaic properties. However, their applications are restricted by their inferior water stability and undesired particle size. Herein, we synthesized suprahydrostable ultrasmall 0D OIHMHs with an average size of ∼3.4 nm, named (TOA)CuBr2 for the first time. (TOA)CuBr2 nanocrystals exhibit bright fluorescence attributed to self-trapping excitons and demonstrated by density functional theory calculations. It is worth emphasizing that (TOA)CuBr2 displays excellent reversible photoluminescence (PL) response to UV light, temperature, and acid/alkali simultaneously, especially to acid/alkali. The underlying mechanism of the reversibility properties is explained and the reversible PL switch is developed by utilizing the acid/alkali responsive property. Moreover, it is the first demonstration of a real-time rewritable transient display based on 0D OIHMHs with “writing/self-erasing/rewriting” capability under wireless near-infrared light. In addition, encrypted strips prepared by in-situ growth of (TOA)CuBr2 in polydimethylsiloxane show quite stable fluorescence properties in various scenarios. Dynamically recoverable encryption systems are also designed by taking full advantage of the material’s multi-response characteristics. Our work provides a strategy for synthesizing suprahydrostable ultrasmall OIHMHs, which will lay a solid foundation for developing their multi-scenario applications in the future.

Tribological Investigation of Glass Fiber Reinforced Polymer Composites against 52100 Chrome Alloy Steel Based on ELECTRE Decision-Making Method.

Fibers play an important role when studying the tribological behavior of reinforced friction composites. The purpose of the current research is to develop a glass fiber reinforced polymer (GFRP) recipe with improved tribological properties as well as to find the composites with the optimal tribological behavior. A ball-on-disc tribometer was used to perform dry sliding friction testing, the obtained results were then analyzed with the ELECTRE (ELimination Et Choix Traduisant la REalite-elimination and choice translating the reality) method based on a utility matrix having process parameters the applied load, sliding velocity, and weight percentage of the fiber content. The ELECTRE method was chosen to find the optimal tribological parameters, with respect to more performance criteria, because it is one of the best multiple criteria decision-making methods. The optimal combination of parameters for the multi-response characteristics of the investigated friction composite was at an applied load of 10 N, a sliding velocity of 0.1 ms-1 and a 54% weight fiber content. The results showed that the addition of glass fiber (GF) content did not considerably improve the tribological behavior of the friction composites. In addition, from the nano focus-optical 3D scanning electron microscopy, images of the friction, tested friction and wear composites, plate formation, fiber-matrix delamination, fiber pull-out, and matrix cracking and damage, various wear mechanisms were identified.

Exploring the dual sensing properties of an anthraldehyde based Schiff base for the successive determination of picric acid using AIEEF and copper using colorimetric methods

A novel and lucid Schiff base, designated as AHN, exhibiting multiple analytical responses comprising AIEE (Aggregation Induced Emission Enhancement) and colorimetric activity towards distinct analytes has been designed and synthesized. The probe, AHN, effectively leverages AIEE activity to selectively detect picric acid (PA) on behalf of fluorescence switch-off response amidst other nitro chemicals. Additionally, it offers remarkable colorimetric selectivity to bivalent copper ions (Cu2+) via a distinct colour change from colourless to yellow. The multi-response characteristics of AHN propel its practicality for naked-eye detection of these analytes. The addition of PA to the aggregate of AHN in DMSO with a 90% water fraction induces a quenching in the fluorescence intensity of the AHN aggregate and the limit of detection of PA was found to be 24.5 × 10−7 M with a quenching constant value of 6.21 × 107 M−1. Stern Volmer plots and lifetime measurements, clearly indicate that both static and dynamic processes were involved in the quenching mechanism. It is also evident that ground-state complexation between electron-rich fluorescent aggregates of AHN and electron-deficient PA takes place through π-π interactions and intramolecular hydrogen bonding interactions. Further, AHN in DMSO exhibits a selective colorimetric response to bivalent copper among various metal ions with a detection limit of 3.16 µM which is substantially below the permitted limit recommended by WHO in drinking water. The mechanism of colorimetric response is the complexation of AHN with Cu2+ in the 2:1 stoichiometry, as confirmed by Job’s plot method. Consequently, the versatility of probe AHN in detecting PA and Cu2+ through distinct mechanisms fosters its significance in the field of sensing and opens promising avenues for practical sensing applications.

A framework of active learning and semi-supervised learning for lithology identification based on improved naive Bayes

Lithology identification is the basis of energy exploration and reservoir evaluation, intelligent and accurate identification of underground lithology is a key issue. The establishment of a machine learning lithology identification model using logging data is a hot research direction in recent years. However, the logging data has a high degree of non-linearity and multi-response characteristics, and there are insufficient numbers of labeled samples in the training data set. These will eventually affect the modeling accuracy and may cause over-fitting. Therefore, a framework of active learning and semi-supervised learning for lithology identification based on improved naive Bayes (ALSLINB) is proposed. The contributions are fourfold: (i) The Gaussian mixture model (GMM) based on the EM algorithm is used to estimate the probability density of the log data, which fits the probability distribution of the nonlinear multi-response log data. (ii) A framework combining active learning (AL) and semi-supervised learning is proposed for the expansion of labeled samples in the training data set. (iii) The application of pseudo-labeling detection technology can effectively improve the authenticity of pseudo-label samples. (iv) Different from the general deterministic lithology identification method, the result of the ALSLINB algorithm corresponds to the probability score, which provides an auxiliary basis for the prediction result. Finally, the ALSLINB algorithm is applied to two different data sets for a large number of experiments and compared with the related baseline methods to verify its effectiveness and generalization ability. The result proves that the ALSLINB algorithm can complete the lithology recognition task well and has high accuracy and robustness, which provides a new direction for intelligent lithology identification.

Optimization of process parameters in ultrasonic machining using integrated AHP-TOPSIS method

The process by virtue of which holes and cavities are formed in a metal work piece with the help of shaped tools, high frequency mechanical motion and abrasive slurry is known as Ultrasonic Machining Process. Its working principle is completely different from the other traditional machining processes such as laser beam machining and electrical discharge devices. This is developed machine removal processes in which the work material is less damaged by the thermal energy and it also introduce significant level of residual stress which is important for survival of the work material. This character of the process makes USM more enhanced and better machine job characteristics. This characteristic is also required for determining the optimal control parameter settings of an USM process’s is a non-traditional machining process in which abrasive content in slurry are driven against work by tool oscillating at low amplitude and high frequency. In this research paper stainless steel is used as tool material in the whole process and zirconia bio ceramic plate is taken as a work piece material. The process parameters are considered as feed rate, slurry concentration, power rating and grit size. Through this work, it is found that responses highly correlated with correlation coefficient, which justifies the application of MCDM as an optimization tool. The optimization studies are carried out on an ultrasonic machining process with multi response characteristics depend on the multi criteria decision making methodology (MCDM), using technique for order preference by similarity to ideal solution (AHP-TOPSIS) approach are described in this paper. The final results of this paper indicate the effectiveness of USM using MCDM method for different USM parameters to produce high quality parts made of zirconia Bio-ceramics with excellent productivity.

Multi-response optimization of cellulose fiber isolation from tapioca solid waste and its characteristics

The tapioca-based starch industry produces solid waste in abundance that has not been used optimally, especially the cellulose fraction. This study aimed to optimize the H2O2 concentration and the process temperature of cellulose fiber isolation from tapioca solid waste. Statistical regression modeling and optimization of H2O2 concentration and process temperature using the response surface methodology. A central composite design (CCD) was applied for experimental design and analysis of the effect of H2O2 concentration and process temperature on multi-response characteristics of cellulose, consisting of whiteness index (WI), yield, and α-cellulose content. Cellulose fibers were characterized, including surface morphology, crystallinity degree, and thermal stability. The results showed that the H2O2 concentration and process temperature were significantly affected by WI, yield, and α-cellulose content. The maximum WI, yield, and α-cellulose content were 63.99%, 65.73% (w/w), and 78.31% (w/w), respectively, obtained from H2O2 concentration of 22.62% (v/v) and process temperature of 93.51ºC. This cellulose has a relatively coarse fiber formation, with a high degree of crystallinity and thermal stability. Thus, cellulose from TSW might have a potential to be applied in broader fields.

MULTI-RESPONSE OPTIMIZATION ON ABRASIVE WATERJET MACHINING OF GLASS FIBER REINFORCED PLASTICS USING TAGUCHI METHOD COUPLED WITH TOPSIS

This work aims to perform the multi-response optimization for abrasive waterjet machining (AWJM) of glass fiber reinforced plastics (GFRP). The experiments were conducted with AWJM factors like pressure (P), traverse speed (TS), and standoff distance (SOD) at three levels. Taguchi’s L9 orthogonal array (OA) was used to design the experiments. The influence of control factors was evaluated by measuring the surface roughness and taper angle while cutting GFRP. The optimum parameter for an individual response was obtained through Taguchi’s [Formula: see text]/[Formula: see text] and multi-response optimization was performed with TOPSIS. From TOPSIS, the optimal parameter of the pressure of 200 MPa, standoff distance (SOD) of 1.5[Formula: see text]mm, and traverse speed (TS) of 25[Formula: see text]mm/min were found. After optimization, the taper angle was decreased by 1.41%. The influence of cutting variables on the responses was statistically analyzed through analysis of variance. It was observed that the pressure has a significant effect on multi-response characteristics and a contribution of 85.90%. After, AWJM, the surface was examined using SEM analysis and found the deformation and pull-out of fibers.

OPTIMASI MATERIAL REMOVAL RATE (MRR) BAJA ST 42 PADA PROSES CNC TURNING DENGAN MENGGUNAKAN METODE TAGUCHI

Abstract High MRR is a goal to be achieved in the workpiece machining process using CNC turning. But the slow process will affect the processing time of the product, and will increase production costs. To overcome this, it is necessary to set appropriate process parameters to produce the most influential contribution to MRR and also to produce optimum parameter values. The purpose of this study was to determine the effect of parameters (cutting speed, spindle speed and depth of cut) on MRR and to determine the correct process parameter settings in order to produce optimal MRR on ST 42 steel. The study was conducted using ST 42 steel and using carbide chisels on CNC turning process. The process parameters that will be varied are spindle speed, feeding motion, and infeed depth. The experimental design used was orthogonal matrix L9(33). The repetition is done twice. Furthermore, the optimization of multi-response characteristics is carried out using the S / N Ratio method. From the research results, it was found that in order to reduce the variance of the response simultaneously, the infeed depth had the largest percentage contribution value, which was 46.642%, the spindle speed had a percentage contribution value of 14.362%, and then the feeding motion with a percentage contribution value of -7.548 And to obtain the optimal MRR of the workpiece, the spindle speed is set at 1500 rpm, the feeding motion is set at 0.5 mm / put, and the infeed depth is set at 1 mm. Keywords: Carbide, CNC Turning, Taguchi, S/N Ratio, MRR

Multi-response Characteristics Optimization of a Paper-based Composite using Taguchi Method and Particle Swarm Optimization

Our dependency towards wood has reduced the number of forests especially in Indonesia. Hence, many people tried to use another material as a replacement of wood. One of them is composite. Department of Industrial Engineering Universitas Sebelas Maret has developed paper-based composite using newspaper waste in Praktikum Perancangan Teknik Industri (PPTI) II. Unfortunately, the quality of composite is not so good. This paper tried to understand the effect of factors to responses and discover the best combination of factors to obtain the best responses. The responses of this experiment are tensile strength, bending strength and impact value. The factors of this experiment are paper pulp, PVAc glue and water. Taguchi method is used to find the appropriate orthogonal array and Particle Swarm Optimization method to find the optimum combination of the factors. Orthogonal array used in this experiment is L9 (34). Through analysis, all of the factors are significant to tensile and bending strength, but they are not significant to impact value. The best combination of factors is 175 grams paper pulp, 130 grams PVAc glue and 175 ml water. Optimum results obtained for tensile strength is 0.7595 N/mm2, bending strength is 4.873 N/mm2 and impact value is 0.0138 J/mm2.

Optimization and effect of load, sliding velocity, and time on wear behavior of AA8011- 8 wt.% fly-ash composites

This research work was undertaken to study the effect of dry sliding wear process parameters such as load, time, and sliding velocity on the wear rate and coefficient of friction of AA8011 with 8 wt.% fly-ash composites. The response surface methodology was used to determine the most important criteria to maximize the wear rate and minimize the coefficient of friction. The experimental procedure was designed based on second-order rotatable central composite design. The optimization studies were carried out on dry sliding wear process parameter with multi-response characteristics, including the wear rate, coefficient of friction, based on multi-criteria decision-making using the TOPSIS approach. Sensitivity analysis was used to identify the important criteria and classify them by their order of importance in model validation. A confirmation test was carried out to validate the results, and the obtained optimal parameter levels were found to be very close to an ideal solution.

Investigation on Slicing Behavior of Single Crystal Silicon Wafer in AWJM and Influence of Micro Dimple Textured Surface for Solar Applications

The current investigation focuses on the slicing characteristics of silicon, a hard and brittle solid that is very difficult to cut, with abrasive water jet machining (AWJM) process by varying different operational parameters like abrasive flow rate (QA), traverse rate (TR), water pressure (P) and stand-off distance (SOD). The trials were carried out based on Taguchi L9 orthogonal array with multi-response characteristics optimization using the Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) approach. The main effect plot is drawn for the relative closeness coefficient (Cn*) and TOPSIS. The most favorable combinations of the experiment are obtained from the response diagram, traverse rate at level 3, abrasive flow rate at level 3, water pressure at level 3 and standoff distance at level 1 for minimizing the surface roughness, and maximizing the Material Removal Rate (MRR) and flatness. A Field Emission Scanning Electron Microscope (FESEM) and Atomic force microscopy (AFM) study were carried out to observe the quality of the obtained sliced surfaces. The optical characteristics like reflectance and absorption of the laser textured and sliced surfaces are measured using a UV-VIS spectrophotometer, which reveals the significant enhancement in the optical properties, in the case of the laser textured surface.

Multi-response optimization of AA 8011 and 12 wt.-% fly ash composites

Abstract The present research work is focused on the production of aluminum alloy 8011 with 12 wt.-% fly ash composite by using the stir casting method. A three-level central composite design experiment is developed using response surface methodology (RSM) with various parameters. Load, time and sliding velocity are varied in the range of (5-15 N), (5-15 min) and (1.5-4.5 m × s-1), respectively. Dry sliding wear tests are performed as per the experimental design using a pin-on-disc at room temperature. This paper describes how optimization studies were carried out on a dry sliding wear process with multi-response characteristics based on MCDM using the TOPSIS approach. The process parameters, load, time and sliding velocity are optimized with multi-response characteristics, including the wear rate (WR), and the coefficient of friction (COF). A sensitivity analysis is also carried out and compared with the relative impact of input parameters on wear behavior in order to verify the measurement errors on the values of the uncertainty in estimated parameters. The experimental results indicate that the multi-response characteristics of aluminum alloy 8011 with 12 wt.-% fly ash composite used during the wear behavior process can be enhanced through the TOPSIS method.

Prediction and Analysis of Multi-Response Characteristics on Plasma Arc Cutting of Monel 400™ Alloy Using Mamdani-Fuzzy Logic System and Sensitivity Analysis.

Nickel-based alloys, especially Monel 400™, is gaining its significance in diverse applications owing to its superior mechanical properties and high corrosion resistance. Machining of these materials is extremely difficult through the traditional manufacturing process because of their affinity to rapid work hardening and deprived thermal conductivity. Owing to these difficulties a well-established disruptive metal cutting process namely plasma arc cutting (PAC) can be widely used to cut the sheet metals with intricate profiles. The present work focuses on an intelligent modeling of the PAC process and investigation on the multi-quality characteristics of PAC parameters using the fuzzy logic approach. The Box-Behnken response surface methodology is incorporated to design and conduct the experiments, and to establish the relationship between PAC parameters such as cutting speed, gas pressure, arc current, and stand-off distance and responses which include the material removal rate (MRR), kerf taper (KT), and heat affected zone (HAZ). The quadratic regression models are developed and their performances are assessed using the analysis of variance (ANOVA). Fuzzy set theory-based models are formulated to predict various responses using the Mamdani approach. Fuzzy logic and regression results are compared with the experimental data. A comparative evaluation predicted an average error of 0.04% for MRR, 0.48% for KT, and 0.46% for HAZ, respectively. The effect of variations in PAC process parameters on selected responses are estimated through performing the sensitivity analysis.

Multicharacteristics optimization of electrical discharge micro hole drilling in Mg alloy using hybrid approach of GRA–regression–PSO

PurposeMagnesium alloys are becoming prominent as an alternative to the permanent biomedical implants. In present work, electric discharge drilling (EDD) process has been investigated and optimized for ZM21 Mg alloy that can be used for producing perforated bone implants having geometrically precise micro holes.Design/methodology/approachPlanning of experiments has been carried out in accordance to the Taguchi mixed L18 orthogonal array (OA). The hole overcut (HO), circularity at entrance (Cent) and circularity at exit (Cext) of drilled micro holes were measured as response characteristics during experimentation corresponding to different settings of EDD input parameters. For optimizing multiresponse characteristics, the hybrid approach of grey relational analysis, regression analysis and particle swarm optimization has been implemented.FindingsIt is found from hybrid approach that brass electrode along with Ip; 3 Amp, Ton; 50 µs and Toff; 52 µs outperformed over all other parametric settings against the collective result of response characteristics. The experimental values of response characteristics at suggested optimized setting are HO: 93.48 µm; Cent: 0.988 and Cext: 0.992, respectively.Originality/valueThe optimization of EDD process for developing perforated Mg alloy bone implants, using hybrid approach is still missing.

Experimental Investigation and Optimization of Wire Electrical Discharge Machining for Surface Characteristics and Corrosion Rate of Biodegradable Mg Alloy

Wire electrical discharge machining (Wire EDM) is a spark erosion process that modifies the surface characteristics by creating overlapped craters and oxides on the machined surface. This oxide and metamorphic layer can be advantageous for Mg alloy in terms of improved corrosion resistance and osteoblast activities. In the current work, face-centered central composite design has been used to carry out the experiments on Wire EDM to study the influence of process parameters and to generate the correlation between input parameters and performance characteristics of ZM21 Mg alloy. Performance characteristics chosen for Mg alloy machining are cutting speed, surface roughness and corrosion rate. To identify the important input parameters and a numerical model that fits the response characteristics, analysis of variance has been used. Using a scanning electron microscope (SEM), it has been found that Wire EDM resulted in overlapped craters and formation of µ-cracks on the machined surface, influenced by discharge energy developed across the electrodes. SEM and XRF (x-ray fluorescence) analysis confirm the formation of a metamorphic layer on the machined surface which leads to corrosion resistance improvement as compared to polished ZM21 Mg alloy. Under response surface methodology, desirability function was utilized to obtain the optimal solutions for multi-response characteristics which were validated experimentally and the sample machined at optimal setting shows improved surface morphology with an oxide layer having uniform nanoscale structure. Electrochemical impedance spectroscopy analysis (for 7 days) shows an increasing trend of corrosion resistance for machined samples, which supports the application of Wire EDM for Mg alloy implants.

Linen Fabric-Jute Fabric-Fumed Silica-Epoxy Sandwich Laminate: AWJ Machining and Multi-Response Optimisation

Since fabric-polymer laminates are being used in several engineering applications, it is imperative to compute and analyse the performance of any novel fabric reinforced polymer laminates while machining. Given these perspectives, an endeavor has been made to formulate a polymer sandwich laminate by incorporating industrial by-product & natural fibres and to machine the sandwich laminate using Abrasive Water Jet (AWJ) machining. For enhancing the machining process, a multi-objective optimisation is employed. In this study 2 wt.% fumed silica added epoxy reinforced with linen fabric (3 layers) & jute fabric (2 layers) sandwich laminate is machined using AWJ machining and the optimal combination of machining parameters are predicted using Taguchi-grey relational technique for multi-response characteristics such as Material Removal Rate (MRR) and Kerf Inclination (KI). For multi-response optimisation the traverse speed, the standoff distance, and the abrasive flow rate are considered as input machining parameters. In AWJ machining, the MRR increases with traverse speed and abrasive flow rate. Whereas, passing of higher abrasive particles at higher traverse speed causes reduction in kerf inclination. Using Taguchi-grey relational analysis (GRA), the optimal combination of AWJ machining process parameters for maximum MRR and minimum kerf inclination is attained. For determining the greatest significant parameter in optimisation, ANOVA is carried out. The standoff distance has the highest influence on multi performance characteristics of AWJ machining of laminate followed by traverse speed and abrasive flow rate. The confirmation test revealed that the grey relation grade value has improved by 1.81% from predicted grey relation grade value.

Multi-response optimization of AA 8011 and 12 wt.-% fly ash composites

Abstract The present research work is focused on the production of aluminum alloy 8011 with 12 wt.-% fly ash composite by using the stir casting method. A three-level central composite design experiment is developed using response surface methodology (RSM) with various parameters. Load, time and sliding velocity are varied in the range of (5-15 N), (5-15 min) and (1.5-4.5 m × s-1), respectively. Dry sliding wear tests are performed as per the experimental design using a pin-on-disc at room temperature. This paper describes how optimization studies were carried out on a dry sliding wear process with multi-response characteristics based on MCDM using the TOPSIS approach. The process parameters, load, time and sliding velocity are optimized with multi-response characteristics, including the wear rate (WR), and the coefficient of friction (COF). A sensitivity analysis is also carried out and compared with the relative impact of input parameters on wear behavior in order to verify the measurement errors on the values of the uncertainty in estimated parameters. The experimental results indicate that the multi-response characteristics of aluminum alloy 8011 with 12 wt.-% fly ash composite used during the wear behavior process can be enhanced through the TOPSIS method.

Process parameter optimization for the magnetic abrasive finishing of SS310s steel

In this work, the MAF set-up is fixed on a vertical milling machine, and experiments are performed for optimizing the process parameters using central composite design (CCD) based on response surface methodology (RSM). From these experiments, output responses like percentage improvement in surface roughness (%ΔRa) and the material removal rate (MRR) are measured. An airgap is maintained in the range of 1.5-2 mm at a voltage of 10-20 V. A series of 62 experiments are conducted using selected process parameters at different levels. A grey based fuzzy algorithm is used to optimize the multi-response characteristics. The grey fuzzy reasoning grade (GFRG) is used to identify the optimal process parameters. From the results, the highest GFRG value is obtained at a voltage of 15 V with a corresponding rotational speed, machining gap, mesh number and mixing ratio of 750 rpm, 1.75 mm, 800 and 1: 2, respectively. In addition, analysis of variance (ANOVA) is used to identify the percentage contribution of each parameter in the MAF process, and the same is verified through experimentation. Optical microscopic images confirm that the surface finish of SS310s has been improved using the MAF process.

Influence of CNC turning variables on high strength Beryllium-Copper (C17200) alloy using tungsten carbide insert

The current investigation is, to determine the optimum process variables on CNC turning operation of Be-Cu (C17200) alloy using grey relational approach (GRA). In GRA, optimum level of turning variables can be identified by the grey relational grade as the performance index. In present work, the three turning variables were chosen namely cutting speed (V1 = 800 rpm, V2 = 1000 rpm and V3 = 1200 rpm), feed rate (F1 = 0.10 mm/rev, F2 = 0.12 mm/rev and F3 = 0.14 mm/rev) and depth of cut (D1 = 0.8 mm, D2 = 1.0 mm and D3 = 1.2 mm) and the output responses such as material removal rate (MRR) and surface roughness (SR) were considered. A grey relational grade attained from the GRA was used to identify the optimum turning process variables on multi response characteristics with an objective to maximize the MRR and minimize the SR. Based on the grey relational grade, optimum level of turning variables was identified as V3F3D2 that is cutting speed at level 3 (1200 rpm), feed rate at level 3 (0.14 mm/rev) and depth of cut at level 2 (1.0 mm). The significant effect of each variable on response was determined by analysis of variance (ANOVA). From ANOVA result shows that cutting speed was the most significant factor on multi response characteristics followed by depth of cut. Finally, the verification test was performed to validate the experimental results.

Selection of Heat Transfer Fluids for Solar Thermal Applications Using Multi-Criteria Decision-Making Tools

Abstract This article describes how optimization studies were carried out on a selection of optimal heat transfer fluids (HTFs) for solar applications with multiresponse characteristics based on the multi-criteria decision-making methodology (MCDM) using the Technique for Order Preference by Similarity Ideal Solution approach and grey relational analysis among 16 alternatives. The processing parameters’ thermophysical properties and the environmental, safety, and economic conditions are optimized with multiresponse characteristics, including the viscosity, thermal conductivity, specific heat capacity, density of fluid, thermal diffusivity, ozone depletion potential, global warming potential, flammability, toxicity, and cost of the fluid. In the proposed technique, the grade ratings and weights allocated by decision-makers are averaged and normalized into a comparable scale. By comparing both these techniques, deionized water is selected as the perfect HTF to operate the solar thermal applications. Hence, both of the techniques are suitable to establish the best possible solution for the set of input parameters depending upon the required performance characteristics. This article highlights a novel vision into MCDM methods to evaluate the best HTF for the decision-makers such as solar manufactures and research and development engineers to meet the low-cost, quick, appropriate, and environmentally friendly fluid selection.