Narrow Results

Several studies using Friction Stir Welding (FSW) on dissimilar Aluminium and Copper weld configurations and the mechanical and microstructure characteristics, as well as the identification of intermetallic phases that appear in the weld region, have been conducted. Indeed, earlier several studies had been conducted in both lap and butt FSW joint designs. Even so, Al–Cu lap welding has received further attention than butt–welding, which has attracted only a few studies thus far. The studies ended with varying findings and were unable to reach high strengths despite the fact that very few studies addressed tool placement factors. The present investigation starts with butt–welding of AA1050 and pure Cu sheet by employing the FSW procedure. Experimental trails were designed using the L16 array (Taguchi’s technique) with distinct combinations of tool spinning speed, tool offset, welding speed, and plunge depth. Weld parameters such as compressive strength (CS), ultimate tensile strength (UTS), and average hardness at nugget zone (AHNZ) were evaluated using three different optimization methodologies, i.e. the WASPAS method, Alibaba and the Forty Thieves algorithm, and the Taguchi–WASPAS method, to find out the best weld constraint arrangement for maximizing the welded joint mechanical characteristics. ANOVA test was also conducted, which revealed that welding speed is the most significant and influential FSW factor for Al–Cu butt–welding using FSW. Finally, confirmatory tests were piloted to check the consistency of the predicted FSW parameters.

Aluminum metal matrix composites (AMMCs) are currently widely engaged in industrial sectors. No other monolithic material can match the characteristics of AMMCs. AMMCs are stronger than traditional materials and have a wide range of industrial uses. This research work aims to study the tribological properties of AA8079/Zirconium boride (ZrB₂) composite manufactured via the stir casting (SC) process. The different combinations of composites are AA8079/0wt.% ZrB₂, AA8079/5wt.% ZrB₂, AA8079/10wt.% ZrB₂, and AA8079/15wt.% ZrB₂. The parameters are reinforcement wt.% [A], load [B], sliding velocity [C], and sliding distance [D]. When designing experiments using the Taguchi method, an L${}_{16}$ orthogonal array was used. In order to determine which process parameter had the biggest influence on the output variables, wear rate (WR), coefficient of friction (COF), and analysis of variance (ANOVA) were applied. Successful fabrication of different weight fractions of ZrB₂ was synthesized with AA8079 matrix through the SC process. The microstructural investigations revealed the dispersion of ZrB₂ particles over the surface of the base AA8079. The response table clearly depicts that the combinations of A and C are the more dominant factor for WR, while the combinations of B and C are the more interacting parameters for COF. From the ANOVA results, it is clear that A with a 55.26% contribution, is the most predominant parameter in attaining the least WR, and for COF, B with a 31.39% contribution, is the major influencing parameter.

Aluminum alloys are widely employed in design and material selection in the industry due to their superior ergonomic properties and low cost. Therefore, aluminum has been strengthened with various elements and powder metal reinforcements in recent studies. In this study, the 6061 aluminum alloy, which is used widely in production, was reinforced with B₄C powder metal and copper elements in a hybrid form. The mechanical, metallurgical, and machinability properties of all reinforced 6061 aluminum materials were examined and characterized. In particular, this study examined the machinability of the materials produced differently from the literature by using equations of energy power conversion and the Taguchi Method, which is one of the experimental design methods, and compared the results of the machining process for different materials. Furthermore, the effects of feed rate, cutting speed, and amount of passes on machinability properties were investigated by conducting the ANOVA analysis on the experimental design parameters and levels. Consequently, while Cu and B₄C reinforcement improved the hardness and mechanical properties, positive results were also obtained on machinability.

A non-traditional machining method based on thermal energy, wire electrical discharge machining (WEDM) can precisely machine conductive materials. In addition to the cost of slot cuts, process parameters are important since they vary depending on the material properties. Hence, it is necessary to employ an appropriate technique to regulate the process parameters and ensure the desired quality of the slot cut. This study involved using WEDM to machine Inconel-825. The experiment used a Taguchi $L_9$ orthogonal array (OA) for maximum cutting rate and minimum surface roughness (SR). Peak current (I), spark on time ($P_{\mathrm{\text{on}}}$), and spark off time ($P_{\mathrm{\text{off}}}$), which are WEDM process parameters, were considered. Analysis of variance (ANOVA) and ANOM identified the significant contribution of cutting rate and SR in terms of WEDM parameters. A MOORA-PCA analysis was conducted to determine the optimum parameter settings. The OA results showed that the best values were at I (5A), $P_{\mathrm{\text{on}}}$ (50ms), and $P_{\mathrm{\text{off}}}$ (8ms), with an assessment value of 0.9935 and 1.0558 for linear and circular cut, respectively. The best outcomes from the MOORA-PCA optimization procedures were compared to the experimental outcome, and an improvement of 22.5643% and 17.7085% in linear and circular cuts were found, respectively. Optimizing WEDM parametrically utilizing a commercially available Inconel 825 machining technique based on MOORA-PCA MCDM is feasible.

The pursuit of precision machining in engineering materials has ignited an imperative to advance electrical discharge machining (EDM) techniques. This study is a deep dive into the domain of micro-electrical discharge machining ($\mu$EDM) with a specific focus on the utilization of carbon-coated tool electrodes. The primary objective is to elucidate the influence of carbon-coated electrodes on vital machining parameters, including machining depth (Z coordinate), tool wear rate (TWR) and overcut (OC) in $\mu$EDM operations. Moreover, the investigation extends to scrutinizing surface characteristics generated by $\mu$EDM with carbon-coated tool electrodes. To enhance performance, carbon-coated tungsten carbide tool electrodes are employed for machining titanium alloy (Ti–6Al–4V) workpieces. The optimization process seeks to identify the optimal combination of process parameters using a Taguchi-DEAR-based multi-response approach. Experimental findings reveal that integrating carbon-coated electrodes significantly improves the $\mu$EDM process, leading to enhanced surface performance metrics. Notably, this study identifies specific parameter settings that effectively optimize machining quality indicators. By achieving a voltage of 160V, a capacitance of 10,000pF and a tool rotation of 600RPM, the research contributes valuable insights into the intricate realm of $\mu$EDM, highlighting the potential for enhanced surface performance through strategic parameter manipulation.

Depth of machining and dimensional accuracy are important parameters in assessing the quality of microelectrical discharge machining (EDM); therefore, enhancement of these two entities is highly essential. The optimal process parameters in microEDM are significantly influenced by the electrode material, which directly affects the machining quality. In this study, depth of machining ($Z$ co-ordinate) and overcut (OVC) in microEDM with carbon-coated electrode were selected; and capacitance ($C$), voltage ($V$), and rotary tool (RT) were the process parameters. The experimental work was performed on a titanium alloy (Ti-6 Al-4 V) with a thin film-coated microtool electrode. The surface of the tungsten carbide microtool electrode was coated with carbon and the thickness of the coating was approximately five microns. The results show that the Taguchi–multi-objective optimization based on ratioanalysis (MOORA) — analytic hierarchy process (AHP) is the best combination for performing a simple and concise calculation in the machining process; and $U=140$ V results in higher machining efficiency with $C=10000$ pF and $\mathrm{\text{RT}}=200$ rpm. Our results show that the quality of the machined surface and machining accuracy in microEDM using coated electrode at optimal parameters are good.

In this paper a process targeting model for three class screening problem is developed. The model developed, extends the work in the literature by incorporating product uniformity. The product uniformity is introduced via a Taguchi type quadratic loss function. Two cases for the process Targeting are considered. In addition, an illustrative example is presented. Sensitivity analysis is also conducted to study the effect of model parameters on expected profit and optimal process mean.

In this study, the surface of AISI 430 stainless steel was alloyed with B₄C using the plasma transferred arc welding hardface coating method and the effect of Ti addition was examined. The microstructure of the resultant hardface coating layer was examined through X-ray diffraction (XRD) analyses by using the scanning electron microscopy (SEM). Abrasive wear resistance was analyzed by measuring mass loss according to the L${}_{16}$ orthogonal array using the Taguchi design method. The “smaller the better” principle of the Taguchi method was used in graphic evaluations. Additionally, the effect of wear test factors on mass loss was calculated in % by performing the analysis of variance. As a result, the austenite, martensite, M${}_{23}$(B,C)₆, M₇(B, C)₃, Ti (B,C), Fe₃(C,B), Fe₃C, Fe₂C and Ti (B,C) phases were detected in the coating layers. The effect of these phases on wear behavior was evaluated. In addition, the optimization of the parameters was obtained with response surface methodology (RSM) based on Taguchi orthogonal experimental design. The results given by the effect parameters required for the developed wear estimation are successful. As a result of the ANOVA analysis, the most effective parameters for wear resistance mass loss were determined as wear distance, applied load and abrasive, respectively. It was observed not to have any effect on wear mass loss of the samples. The most suitable parameter values for the lowest wear values were determined.

In this work, the surface of AISI 430 stainless steel was coated with a hyper-eutectic FeCrC and pure B₄C powders using the plasma transferred arc (PTA) welding method. Further, the microstructure, microhardness, and abrasive wear resistance of the coated layer were evaluated in this work. Hard coating layers were formed between 750HV and 1000HV from the PTA melting coating process with high energy input (due to PTA). Due to the addition of FeCrC and B₄C, the martensite, austenite, M₇(C-B)₃, M${}_{23}$(C-B)₆, Cr(C-B), FeCr, Fe₂B, Fe₃C, Fe₂C, SiC, and Fe₃(C-B) phases were formed in the coating layer which makes the layer very hard. The abrasive wear resistance of the coated layer was evaluated using the Taguchi method with L${}_{16}$ mixed-level orthogonal index. Samples, wear distance (m), applied load (N), and abrasive grain size (grid) were chosen as input parameters. The mass loss due to wearing was evaluated based on “the smaller the better”. All input parameters significantly influence the wear, but the highest effect was found with the wear (slip) distance. Also, a linear regression equation was obtained to estimate the mass loss between the parameters and the levels used.

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 $S$/$N$ and multi-response optimization was performed with TOPSIS. From TOPSIS, the optimal parameter of the pressure of 200 MPa, standoff distance (SOD) of 1.5mm, and traverse speed (TS) of 25mm/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.

Straw combines are intended to process the remaining harvested straw. When cut at high temperatures and in abrasive conditions, the cutting blade of straw combines undergoes substantial surface deterioration. This deterioration shortens the blade’s lifespan and increases the cutting cost of the machine. In recent decades, cryogenic treatments have played a significant role in enhancing material properties. In this paper, cryogenic treatment is utilized to boost the wear resistance of straw combine blades in the current investigation. The performance of cryogenic treatment was tested in the laboratory using the pin-on-disc wear tester with sample type, load, sliding velocity, and time serving as process factors and wear loss as the response parameter. The smoothness of the cryogenically-treated sample’s surface is certified through morphological examination. Specific wear rate and field emission scanning electron microscope (FE-SEM) indicated that cryogenic treatment enhances the grain structure and intermolecular interaction of the specimen, resulting in an increase in wear resistance. As opposed to the untreated specimen, the wear on the treated surface is uniform over the entire surface, as demonstrated by FE-SEM analysis. The grain structure and intermolecular bonding of the specimen were improved as a result of the cryogenic treatment. The cryogenic treatment increased the cost of the cutter bar and chopping cylinder blades by 9.38% and 13.61%, respectively, compared to untreated blades, but the increased cost was fully offset by the longer blade life.

Aluminum-based composite materials are frequently preferred in many new-generation engineering applications due to their high strength, wear and corrosion resistance, improvement of mechanical properties, machinability, and low density. Mechanical alloying has an important place in the production of composites with high properties in powder metallurgy, which is one of the composite material production methods. In this paper, the deformation of Al 2024 alloy powder, which is frequently used in the industry, is investigated by the three-dimensional ball mill. Three different rotation speeds (150, 200 and 250rev/min), three different ball-to-powder ratio (5:1, 10:1 and 20:1) and three different milling times (30, 60 and 90min) were used in the milling processes. Deformations in the powders were evaluated by particle size analysis and powder structure examination. The obtained results were analyzed with analysis of variance and regression method, three-dimensional graphics, and scanning electron microscope images. When the results are examined, the maximum percent areas covered by the deformed particles and maximum particle size among the selected experimental parameters were obtained at 250rev/min, 20:1 ball-to-powder ratio and 60min as 6.849% and 54$\mu$m.

In this study, metal matrix composite (MMC) materials were made with an aluminum matrix (AA7075 alloy) and reinforcement silicon carbide (SiC) elements using molten metal stir and indirect squeeze casting. SiC was used as a reinforcing element in the making of MMC material in different amounts (10%, 14%, and 18%) by mass. Electro Discharge Machining (EDM), cut depth (0.5 mm), three different pulse-on times, three different discharge current values, and a fixed pulse-off time (20 s) were used to machine MMC materials. The effects of machining parameters on machining time, average surface roughness, hole diameter, and material wear difference after machining were studied. As a result of the study, the composite material with 75 $\mu$s pulse-on time, 6A current value, and 10% reinforcement element had the lowest machining time, the largest hole diameter, and the smoothest average surface. These machining parameters and materials also had the shortest machining time (5 min). Based on the signal-to-noise ratios, the best parameters for average surface roughness, hole diameter, Processing time, and material wear amount (MMC, discharge current value, and impact time) were found to be $L_2{L}_1{L}_1$, $L_3{L}_1{L}_1$, $L_1{L}_3{L}_3$, and $L_1{L}_1{L}_2$, respectively. Based on the ANOVA results, the $R$² values for the average surface roughness, hole diameter, machining time, and material wear loss value were 99.3%, 98.7%, 77.8%, and 97.3%, respectively.

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.

Aluminum–Bronze alloys (Al-10%–Cu-balance) are difficult-to-machine cut alloys. Copper (Cu) metal itself is not suitable for machining. The machining performance of the Aluminum–Bronze alloy sample was investigated by the Wire Electric Discharge Machining (WEDM) method, which is among the non-traditional manufacturing methods used in conductive materials that are difficult to process. The aluminum–bronze sample with a diameter of 20mm was cut with 0.18mm molybdenum wire electrode. Table feedrate, pulse-on-time and pulse-space were used as experimental parameters. Surface roughness and energy consumption values were measured in WEDM process. According to Taguchi analysis and ANOVA results, it was determined that the pulse-on-time parameter was effective on the surface roughness and the table feedrate parameter was effective on the energy consumption.

Objective: To optimize the minimum detectable difference (MDD) of a cardiac X-ray imaging system using the Taguchi L₈(2⁷) analysis and a precise line pair (LP) gauge. Methods: The optimal combination of the four critical factors of the cardiac X-ray imaging system, namely X-ray focus, kilovoltage (kVp), milliamper-seconds (mAs) and source image distance (SID), providing the MDD was calculated via the Taguchi analysis and experimentally verified. Two (low and high) levels were assigned for each factor, and eight combinations of four factors were used to acquire instant X-ray images using an NDT commercial LP gauge (with a gauge length of 64mm and a width of $3.5$mm). The latter had five lines and was split gradually from top to bottom for the inspection of X-ray images, whose quality was ranked by three well-trained radiologists according to the double-blind criterion. The ranking grade was given by sharp contrast, low noise and precision to distinguish the LP. Accordingly, the MDD was derived to represent the spatial resolution of instant X-ray images by the revised Student’s $t$-test analysis. The optimal combination of factors was experimentally identified and clinically verified in the follow-up inspections. Results: For the conventional setting, Group No. 7 (which obtained the highest grade among eight groups) and the optimal setting, the obtained MDD values were $0.183$, $0.167$ and $0.157$mm, respectively, while the LP (line pair/mm) interpolated from the gauge scale amounted to $2.7$, $3.1$ and $3.2$LP/mm, respectively. Conclusion: The Taguchi L₈ analysis was proved to be instrumental in optimizing the cardiac X-ray imaging system MDD and is recommended to be used jointly with the revised Student’s $t$-test analysis for improving the spatial resolution of instant X-ray images.

Objective: The minimum detectable difference (MDD) of computed tomography (CT) scanned images was quantified and optimized according to an indigenous hepatic phantom, line group gauge and Taguchi ${\text{L}}_{18}$ optimization analysis in this work. Methods: Optimal combinations of CT scan factors in every group with the level organization were judged using the Taguchi analysis, in which every factor was organized into only 18 groups, creating evaluated outcomes with the same confidence as if every factor was analyzed independently. The five practical factors of the CT scan were (1) kVp, (2) mAs, (3) pitch increment, (4) field of view (FOV) and (5) rotation time for one loop of CT scan. Insofar as each factor had two or three levels, the total number of 162 (i.e., $2\times 3\times 3\times 3\times 3$) combinations was considered. Results: The optimal setting was 120kVp, 300mAs, 0.641 pitch, 320mm FOV and 1.0s of rotation time of CT scan. The minimal MDD was 2.65mm under 0.39mm of the slit depth from the revised Student’s $t$-test with a 95% confidence level. In contrast, the MDD of conventional and the best one (no. 7) among all original 18 groups were 3.27mm and 2.93mm for 0.43mm and 0.41mm slit depths, respectively. Conclusion: The Taguchi analysis was found very lucrative for the design of imaging analysis in practical diagnosis. The indigenous line group gauge and hepatic phantom also proved to be suitable in simulating the human body in real hepatic carcinoma examination.

This study optimized spatial resolution of mammography imaging quality using a CIRS-016A commercial line gauge and the Taguchi methodology. The line gauge with a precise line pair from 5lp/mm to 20lp/mm was placed on top of triangular PMMA plates to simulate the female breast undergoing mammography. Five factors: target/filter, kVp, mAs, PMMA plate thickness, and compression force, were organized into 18 groups according to the Taguchi L${}_{18}$ orthogonal array. Tactically, the 18 various combinations of factors could provide similar confidence levels, as those following the full factorial combination in reality. Seven experienced radiology experts judged the 18 imaging qualities based on contrast, sharpness, and spatial resolution. Then the signal-to-noise ratio was calculated according to the “the larger, the better” ranking order. The optimal preset of mammography was verified from the unique fish bone plot and the follow-up analysis of variance (ANOVA) test. The optimal combination of factors was as follows: Rh/Ag as target/filter, 32kVp, 36mAs, a 45mm thick PMMA plate, and a 13daN compression force in routine diagnosis. The concurrent resolution of 6lp/mm or about a 0.09mm minimum detectable difference (MDD) was superior to 5lp/mm of the conventional preset or combinations of factors of either highest Avg or lowest std. Compared to other studies with various facilities, this was the finest resolution among the routine X-ray, cardiac X-ray or computed tomography (CT), and computed tomography angiography (CTA).

The research investigation reported on the effect of machining parameters on surface roughness (Ra) in electric discharge drilling of Inconel 718. Machining was done by using a copper tool electrode. Machining was conducted by considering different process parameters viz. tool diameter, discharge current, pulse on time, pulse off time, tool rotation and depth of hole. Optical surface profiler was used to measure surface roughness of drilled hole in work-piece. Design of experiment was created by Taguchi method based L18 orthogonal array. For minimum surface roughness, optimum parameters were found using Analysis of variance (ANOVA). Based on analysis, it is found that pulse off time, pulse on time and tool rotation are the most significant parameters that affect the surface roughness. Tool diameter is the less significant parameter that affects the surface roughness. Regression analysis was used to predict a value for minimum surface roughness. The scanning electron microscope (SEM) images were used to identify the microstructure of the drilled hole in Inconel 718 work-piece. Interaction plots and residual plot have been plotted for surface roughness to identify the interaction between parameters and residual errors, respectively.

Duplex turning becomes an important metal cutting process due to unique features like higher productivity with better surface finish at lower specific energy and vibration. Such process requires two-cutting tools which are mounted parallelly and fed inward to cut the material from rotating surfaces. Such complex process needs modeling and optimization to analyze the effect of factors and identify the optimal cutting condition. This paper focuses to develop two models related to statistical and intelligent techniques especially responses surface methodology (RSM) and artificial neural network (ANN) for prediction analysis of duplex turning. Based on prediction potential, the ANN model is utilized to analyze the effect of various parameters (cutting speed, feed rate, primary depth-of-cut (DOC) and secondary-DOC on the responses as surface roughness and cutting forces (primary and secondary). Further, the parameters are optimized using Taguchi Methodology (TM) and experimentally validated. The results show that ANN model predicts the data with more precision than RSM model. Further, the optimal data are experimentally validated and significantly agreed with predicted data of ANN model with percentage error as 2.24%, 1.40% and 0.75% for surface roughness, cutting forces (primary and secondary), respectively.