Narrow Results

This investigation explores the application of electrical discharge machining (EDM) to ceramic composites constructed from Si₃N₄–TiN (Syalon 501) by testing both square and cylindrical electrodes. Various experimental methods have been employed, including design-of-experiments (DoE), Grey relational analysis (GRA), analysis of variance (ANOVA), confirmation testing, and scanning electron microscopy (SEM), in an effort to optimize machining settings and comprehend their effect on performance. While ANOVA revealed crucial parameter levels influencing overall machining conditions as measured by the grey relational grade (GRG), the sophisticated method of GRA considerably improved the machining characteristics. Significant improvements in the EDM process were shown experimentally, resulting in outstanding results for material removal rate (MRR), surface roughness (SR), and electrode wear rate (EWR). In particular, numerous benefits of the square electrode layout over the more common cylindrical arrangement stood out. All of the aforementioned resulted in an impressively high MRR (measured at 0.0281gm/min), a low EWR (at 0.0020gm/min), and a significant improvement in SR (at 0.3150$\mu$m). The GRG value was 0.0163 for the square electrode design, which was excellent, and 0.0025 for the cylindrical electrode, which was significantly better. The optimized parameters were successfully used, especially with the square electrode arrangement, leading to significant improvements in MRR and decreases in EWR. On the other hand, surface quality was negatively affected by larger parameter values, which increased the development of microfractures, particularly when using the square electrode design. In any case, our study provides strong proof that adjusted settings can greatly improve EDM performance, allowing for accurate machining of Si₃N₄–TiN composites with increased MRR, great surface texture, and reduced electrode wear. In conclusion, our study provides useful information to improve EDM procedures for Si₃N₄–TiN ceramic composites, as well as practical insights into increasing the efficiency and quality of EDM operations in a variety of industrial sectors.

This study investigates the optimization of wire electrical discharge machining (WEDM) parameters for microchannel machining of aluminum alloy Al 6061 with improved mechanical properties, which is crucial for various industries. This investigation for microchannel fabrication considers four response parameters: material removal rate (MRR), surface roughness (SR), tool wear rate (TWR), and spark gap (SG), alongside four input parameters: pulse on time (Ton), sparking gap voltage (SGV), wire tension (WT), and wire feed rate (WFR). The selected levels for the experiment are, Ton 105, 115, and 125$\mu$s, SGV 10, 25, and 40 volts, WT 6, 8, and 12kgf, and the WFR 2, 4, and 6m/min. By examining Ton, SGV, WT, and WFR, the research identifies optimal conditions using Taguchi-based grey relational analysis (GRA). The findings highlight the importance of parameters such as Ton of 105$\mu$s, SGV of 40 volts, WT of 8kgf, and WFR of 4m/min for machining Al 6061-based microchannels, offering valuable insights for future manufacturing endeavors. This research also incorporates the morphology study of machined Aluminium alloy microchannel.

Friction stir welding (FSW) has become one of the most used solid-state joining methods because of the increased mechanical properties and weld quality that can be obtained. The present investigation focuses on the effects of Titanium Carbide nanoparticles (TiCnp) reinforcement with the welds of AZ31 magnesium alloy using the grey relational coefficient optimization technique with the aid of artificial neural networks (ANNs) for modeling. The parameters considered are TiCnp content of approximately 1.5wt.%, tool inclination angle of 0^∘, 1^∘, and 2^∘, tool spindle speed of 1000, 1250, and 1500rpm, tool geometry square, cylinder, and triangle, feed rate of 25, 50, and 75mm/min and axial force of 5, 10, and 15kN. Other mechanical properties determined involve microhardness, Tensile Strength (TS), wear rate (WR), and impact strength (IS). The results show the improvement of mechanical properties with an increase in TiCnp concentration within the range which implies that the highest TS of 242MPa is obtainable when the amount of TiCnp is optimally added. Interestingly, while identifying the optimal parameters for mechanical properties, it was ascertained that 1250rpm of rotational speed (RS), 50mm/min of traverse speed (TS), 1^∘ of tilt angle (TA), and square tool profile shape were found to have the best results. Similar findings were backed up by the ANN models whereby the introduction of TiCnp into the AZ31Mg alloy boosts TS to about 130MPa, microhardness to 70MPa and IS to about 89.34MPa, and lowers WR to 0.0046m³/m. This integrated approach highlights the possibility of applying ANN coupled with grey relational analysis for the improvement of FSW process for improving the material characteristics.

The investigation deals with the turning of Alumina (Al₂${\text{O}}_3)$ ceramic using various parameters of the input process. The experimental design has been used based on Taguchi technology to perform the experimentation. Pulse frequency, average laser power, work piece rotational speed and feed rate are the process inputs considered during investigation. The Signal-to-Noise ratio values are used for measuring various outputs. The best amount of spot overlap has been reached with various combined parameter settings. In addition, a better width of the rotational scan has been attained by varying axial feed rate as well as the rotational speed of the work piece. Micro-turned deviation (depth) and machined surface finish at different input parametric combinations were considered as output reactions for machining. During the laser turning operation, analyses learned the effect of overlaps on the various inputs considered for output measurements such as micro-degree deviation and surface roughness. The investigation reveals that the surface finish decreases with an increase in overlap in the circumferential direction and rotation of the work sample. The maximum surface finish is 0.507$\mu$m achieved at a frequency of 5000Hz, 300rpm work piece cutting speed, 8.5W power and 0.4mm/s feed.

$\alpha$–$\beta$ titanium alloy (Ti-6Al-4V) is being used in diverse applications, such as aircraft, chemical, automotive, and biomedical industries due to its excellent attractive properties. But machining of titanium and its alloy is a tedious task through conventional material removal processes. Removing the material at the micro level to create micro-sized features like a hole, slot and intricate projection in the titanium alloy is more complicated. In this paper, micro-sized through-hole machining on the $\alpha$–$\beta$ titanium alloy has been performed using electrochemical micro-machining (EC$\mu$M) with nonaqueous ethylene glycol (CH₂OH)₂–sodium bromide (NaBr) electrolyte and the influence of machining parameters, such as electrolyte concentration (EC), machining voltage (MV), tool feed rate (TFR) and duty cycle (DC) during micro-hole machining on the $\alpha$–$\beta$ titanium alloy is analyzed. The material removal rate (MRR), radial overcut (ROC), conicity ($C$), and surface roughness (SR) of the micro-sized through hole have been ascertained and analyzed. The selected machining parameters are optimized using Taguchi based Grey relation analysis and DEAR methods.

The experimental analysis carried out in this paper aimed at the selection of optimal machining conditions for the 3D profile machining of Inconel 825 alloy using Wire Electrical Discharge Machining (WEDM). It was noticed that the 3D profile machining of Inconel 825 alloy using WEDM process has not been performed and reported so far by the researchers in this domain to the best of our knowledge. As the machining of Inconel 825 alloy is difficult using the conventional machining processes, nontraditional machining is generally used. As the material is conductive, the WEDM process is suitable to machine this material. The effects of input factors such as wire material, pulse on-time, pulse off-time, peak current, wire tension and gap voltage on the material removal rate (MRR), surface roughness (SR) and dimensional shift (DS) have been investigated using $L_{18}$ mixed-level orthogonal array of experiments. The DS value for every machining condition has been predicted and inputted into the CNC system as the wire offset value to enhance the dimensional accuracy of the product. The parametric analysis has been done by Minitab software. Analysis of variance (ANOVA) has been used to find the significant parameters affecting surface quality. The regression equations have also been formulated to study the adequacy of the model. Multi-response optimization based on the Gray Relational Analysis has been employed and the results that optimize the MRR and SR simultaneously have been suggested. SEM images have been taken on the machined surfaces to compare the surface finish on the specimens.

Laser cladding (LC) is mostly employed to enhance the wear resistance of magnesium alloy substrates. Adding nanoparticles will further strengthen the tribo surface properties, making them suitable for applications requiring lightweight components. This work investigated a dry sliding wear analysis for the laser-cladded AZ61 magnesium alloy with TiO₂ nanoparticles at different volume ratios through the LC method. The spatial dispersion of the TiO₂ nanoparticles in the AZ61 magnesium alloy microstructure was analyzed using scanning electron microscopy (SEM). The reinforcement ratio, sliding speed, and normal load were selected to study the tribo performance of the cladded surface. Coefficient of friction (COF) and wear loss analyses were performed using a pin on the disc dry sliding wear test. The effect of dry sliding variables on reinforcement ratio was analyzed with an orthogonal array experimental design. Grey relational analysis (GRA) studied multiple wear test responses to reveal optimal conditions to decrease the wear and friction coefficient of the AZ61 laser cladded surface. The reinforcement percentage of nanoceramic TiO₂ particles in the AZ61 alloy surface was the most significant factor, contributing 97.76%, followed by a contribution of 0.26% by sliding speed and a normal load of 1.82%, confirmed with the grey relational grade. Both SEM and GRA confirmed that the reinforcement ratio of 10% exhibited lower wear loss and friction coefficient. The revealed wear mechanism operating on the worn surface of laser-cladded AZ61 magnesium alloy was micro-grooving exerted by a counter surface at all sliding conditions. This study shows that the LC of magnesium alloys will be preferred in sliding seal and lightweight gear applications.

Laser cutting is a one of the efficient manufacturing processes in industry to cut the hard materials by vaporizing. Stainless steel (SS347) is the most popular material for many applications due its unique characteristics such as efficiency to retain good strength with no inter-granular corrosion even at elevated temperatures. However, the cutting or machining of this material is very difficult. On the other side, the machining cost of laser process is high when compared with other processes. In this work, GRA and TOPSIS techniques are used to study the laser cutting process parameters of SS347. The obtained results were compared with the data mining approach. The input parameters are power, speed, pressure and stand-off distance (SOD) and the output responses of surface roughness, machining time and HAZ are considered. The set of experiments were constructed by using the Taguchi’s L9 method. The predicted closeness value of TOPSIS is greater than the GRA technique and the predominant factor observed is SOD followed by pressure, speed and power. In this work, C4.5-decision tree algorithm is applied to find the most influential parameter. It also represents the low-level knowledge of data set into high level knowledge (If-Then rules form). This investigation reveals that both TOPSIS and data mining suggested the SOD as predominant factor. This result of the optimized process parameters supports the laser assisted manufacturing industries by providing optimized output. Better results were obtained using the optimized set of parameters with the machining time, HAZ and surface roughness being 7.83 s, 0.09 mm and 0.86 $\mu$m, respectively. The results of this work would be very useful for automobiles and aircrafts industries where SS347 is highly employed.

In this paper, the machinability aspects of Ti-5553 have been experimentally investigated during Electro-Discharge Machining using three different geometrical shapes of copper electrodes i.e. cylindrical, triangular, and square. TIMETAL (Ti-5553) is a titanium-based alloy extensively used for special aerospace and marine applications due to its high strength and higher hardenability. By changing each of the aforementioned process parameters at three distinct levels, experiments based on the ${\text{L}}_{27}$ orthogonal array (OA) design of the experiment (DOE) were carried out. The machining performance has been carried out by utilizing different input parameters i.e. current, pulse off time ($T_{\text{off}}$), flushing pressure ($F_p$), tool shape ($T_s$), and voltage (V) to examine machining performance characteristics like material removal rate (MRR), tool wear rate (TWR), surface roughness (Ra), white layer thickness (WLT), and surface crack density (SCD). Surface integrity in contents of surface morphology and surface topography is discussed herein. In this MRR, TWR, and Ra have been optimized by using a methodology (combining Grey relational analysis method and Taguchi’s philosophy) and found the optimal parameters that will have more MRR and less TWR, Ra. Later SCD and WLT were investigated by employing the optimal setting (A3, B2, C2, D2, E1, F3) and arbitrary setting (A3, B2, C1, D3, E3, F2) and concluded that SCD and WLT are less by utilizing optimal settings for machining as compared to arbitrary settings. Among all three different geometrical shapes of copper electrodes, the cylindrical tool has been found to be the best suitable tool for machine Ti-5553.

This investigation focuses on the study of the effect of process parameters like peak current ($I_p)$, base current ($I_b)$, pulse frequency ($F)$, shielding gas flow rate ($Q)$ on mechanical properties like yield strength (YS), ultimate tensile strength (UTS) and flexural strength (FS) of the welded joints during pulsed TIG welding of SAILMA 450 and EN14 B steels. Taguchi’s L${}_{25}$ orthogonal array has been used for conducting the tests. Multi-objective optimization has been performed using Grey relational analysis (GRA) in order to maximize the mechanical strength and to find out the optimal set of parameters. The optimum parametric combination is obtained at a peak current of 220Amps, base current of 120Amps, pulse frequency of 5Hz and shielding gas flow rate of 17l/min. Analysis of variance (ANOVA) is used to predict the significant process parameters. It has been observed from the ANOVA analysis that peak current and pulse frequency have more influence on the output responses than the shielding gas flow rate and base current. The results of the confirmatory test show an improvement of 0.5801 in the GRG, which is satisfactory. A microstructure study has been performed using scanning electron microscopy (SEM) for the optimal set of process parameters.

In bone-drilling operations, undesirable temperature rises are experienced due to high-contact friction. These increases in temperature can damage bone and soft tissues from time to time. When the temperature exceeds 47^∘C, osteonecrosis occurs. This article presents a new method for both the selection of optimum drilling parameters and the mathematical temperature model ($T$^∘C). In this study, the optimum parameter values for bone-drilling operations were found using gray relational analysis, and a mathematical model was created based on the temperature parameters using the response surface method. The accuracy of the developed analytical model has been proven by ANOVA. As a result, it has been revealed that the value of spindle speed is the most effective factor in bone-drilling operations and that the developed analytical model and experimental measurements are in harmony.

This study is the first attempt that aims to develop a comprehensive decision-making model for Software as a Service (SaaS) adoption in the educational environment. Accordingly, a new hybrid Multi-Criteria Decision Making (MCDM) approach of Grey Relational Analysis (GRA), Classification and Regression Trees (CART), and Fuzzy Rule-Based (FRB) techniques is developed to reveal the importance level of significant factors, model adoption status in the form of “IF-THEN” rules, and predict the level of adoption based on the significant adoption factors and their relationships. This study is the first-hand experience that takes complementary advantages of GRA, CART, and FRB techniques for technology adoption decision-making. The findings can be used as a guide by the administrator of universities, ministry of education, and services providers to successfully proceed for SaaS-based applications adoption in the educational environments.

In this paper, we propose a robust ABC classification for inventories using a hybrid technique for order of preference by similarity to ideal solution-alternative factor extraction approach (TOPSIS-AFEA) as the cornerstone method to calculate and rank importance scores for each item in stock. This is done to mitigate multicollinearity that may exist among different inventory criteria, which artificially inflates total data variance. Besides, and differently from previous research, information reliability techniques such as information entropy and gray relational analysis (GRA) are used as an auxiliary tool to differentiate alternative ABC methods proposed in the literature in terms of the principle of maximal entropy. This principle states that the probability distribution that best represents the current state of knowledge given prior data is the one with largest entropy. Results suggest that the proposed robust TOPSIS-AFEA provides an adequate representation of score ranks that may be computed on different datasets by using existing alternative ABC inventory classification models.

Intermetallic/ceramic composites with multiple responses are based on L₁₈ orthogonal array with gray relational analysis (GRA). This paper presents a new approach for the optimization of drilling parameters on drilling MoSi₂–SiC composites. Optimal machining parameters can then be determined by the gray relational grade as the performance index. In this study, the sparking parameters namely current (I), pulse on time (t_on), pulse off time (t_off), spark gap and dielectric flushing pressure (P) are optimized with considerations of multiple performance characteristics including multi responses such as material removal rate (MRR), electrode wear rate (EWR), circularity (CIR), cylindricity (CYL), perpendicularity (PER). A gray relational grade obtained from the GRA is used to solve the electrical discharge machining (EDM) process with the multiple performance characteristics. Based on the gray relational grade, optimum levels of parameters have been identified and significant contribution of parameters is determined by ANOVA. Confirmation test is conducted to validate the test result. Experimental results have shown that the responses in EDM drilling process can be improved effectively through the new approach.

Traveling Wire Electro-Chemical Spark Machining (TW-ECSM) process is a new innovative thermal erosion-based machining process suitable for cutting electrically nonconductive materials using tool electrode in the form of wire. This article attempts experimental modeling of TW-ECSM process using a hybrid methodology comprising Taguchi methodology (TM) and response surface methodology (RSM). The experiments were carried out on borosilicate glass using L${}_{27}$ orthogonal array (OA) considering the input parameters like applied voltage, pulse on-time, pulse off-time, electrolyte concentration and wire feed velocity along with process performances such as material removal rate (MRR), surface roughness (R${}_{\text{a}})$ and kerf width (K${}_{\mathit{\text{w}}})$. The interaction influence of input parameters on process performances was also discussed. Further, multi-objective optimization (MOO) of response performances of TW-ECSM process is executed using a coupled approach of grey relational analysis (GRA) and principal component analysis (PCA). The optimal process parameter setting illustrates the improvement of MRR by 171%, diminution of R_a and K${}_{\mathit{\text{w}}}$ by 27% and 8% against the initial parameter settings. Moreover, irregular cutting of kerf width and surface characteristics were also scrutinized using scanning electron microscope (SEM).

The use of multiwall carbon nanotube (MWCNT) reinforced polymer nanocomposites have significant importance since MWCNT enhances the aspect ratio and interface conditions of polymer materials. However, the machining aspects of carbon nanofillers are still a potential area of research. The mechanical and electrical properties of MWCNT/epoxy nanocomposites makes it a suitable alternative to conventional engineering materials. This work focused the machining characteristics optimization during drilling of MWCNT/epoxy nanocomposites using TOPSIS and GRA theory. The machining characteristics are considered which are surface roughness, torques and thrust. By using both the multi-criteria optimization techniques, all the machining characteristics aggregated into the single objective function. The effects of drilling parameters have been investigated by variance analysis. The outcomes of both the proposed module give different optimal conditions of process parameters. The confirmatory experiment carried out to validate the obtained results, and it has been observed that GRA is more feasible than TOPSIS. During machining, the cutting force rises with the high feed rate, and the lower value of feed rate reduces the surface roughness. The achieved improvements in drilling performances are highly required for an efficient machining environment. Further, a microstructural investigation was done to check the quality of the machined samples.

An experimental investigation was conducted to evaluate the machinability of a titanium alloy (Ti6Al4V) using copper (Cu), tungsten carbide (WC), and graphite (C) tools. Voltage (V), capacitance (pF), pulse-on time (T_on), and pulse-off time (T_off) were considered as the input machining parameters, whereas the material removal rate (MRR) and tool wear rate (TWR) were considered as the output machining parameters. A Taguchi L${}_{16}$ orthogonal array and gray relational analysis (GRA) were utilized to design and optimize the machining parameters for both responses. Artificial neural network (ANN) analysis was performed to predict the experimental outcomes. Scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS) were used to assess the surface morphology and determine the elemental composition of the machined surface. The results indicated that the optimum machining conditions for the copper tool were 150 V, 1000 pF, 15$\mu$s (T_on), and 15$\mu$s (T_off). However, the optimal machining conditions for the WC were 200 V, 100 pF, 25 $\mu$s (T_on), and 10$\mu$s (T_off), and the optimal conditions for the C were 200 V, 1000 pF, 20$\mu$s (T_on), and 25$\mu$s (T_off), respectively. The highest MRR achieved using the WC tool was 9.4510 mg/s, whereas the TWR of the Cu, WC, and C tools were 1.1039 mg/s, 1.0307 mg/s, and 1.2796 mg/s, respectively. The results showed that machining with the graphite tool had a higher TWR than machining with the Cu and WC tools.

The aim of this paper is to introduce the notion of m-polar spherical fuzzy set (mPSFS) as a hybrid model of spherical fuzzy set (SFS) and m-polar fuzzy set (mPFS). The proposed model named as mPSFS is an efficient model to address multi-polarity in a spherical fuzzy environment. That is, an mPSFS assigns $m$ number of ordered triple of three independent grades (membership degree, neutral-membership degree and non-membership degree) against each alternative in the universe of discourse. The existing models, namely, mPFS and SFS, are the special cases of suggested hybrid mPSFS. In order to ensure the novelty of this robust extension, various operations on the m-polar spherical fuzzy sets (mPSFSs) are introduced with some brief illustrations to understand these concepts. A robust multi-criteria decision-making (MCDM) method is established by using new score function and accuracy function for m-polar spherical fuzzy numbers (mPSFNS). Additionally, the extensions of technique of order preference by similarity to ideal solution (TOPSIS) and gray relationship analysis (GRA) towards m-polar spherical fuzzy environment are proposed. Moreover, an application to nephrotic syndrome which may lead to kidney damage is analyzed by extensions TOPSIS and GRA. The proposed techniques and their algorithms provide a fruitful diagnosis procedure in the treatment of nephrotic syndrome. Lastly, we give a comparison analysis of the suggested models with some existing models as well.

The factors that affect the performance of the equipment are numerous and complicated, which makes it difficult to establish a performance calculation model. This paper puts forward a data-driven modeling method with reverse process for this problem. Based on the partial least squares (PLS) algorithm and the gray relational analysis (GRA) method, the analysis method of the performance related factors, the extraction method of characteristic variables, and the performance modeling method are studied. The related factors of the energy consumption of an industrial steam turbine are analyzed, and an energy consumption calculation model is established, and the effectiveness of the above-mentioned modeling methods is verified with sample data, which provides a basis for the energy-saving optimization of the steam turbine.