Desirability Function Analysis Research Articles

Optimizing the SPIF parameters for enhancing microhardness and surface quality in Inconel 625 superalloy components

Inconel 625, a high-strength superalloy with excellent corrosion resistance, discovers wide applications in critical sectors such as aerospace, nuclear, marine, and petrochemical. Single-Point Incremental Forming (SPIF) has appeared as an adequate fabricating process for shaping superalloy components. This work used a systematic approach, implementing Response Surface Methodology (RSM) to explore the influence of SPIF process variables, including tool nose diameter, step size, tool spindle speed, and wall angle, on microhardness (MH) and surface roughness (SR). The experimental results have been analyzed to determine the influencing process variables and their optimal settings, which simultaneously enhance MH and reduce SR of Inconel 625 superalloy truncated cones using analysis of variance and the desirability function analysis approach. The most significant parameters that influence MH and SR were TND and SS, respectively. The development of quadratic models for MH and SR has been completed successfully. The maximum MH of 486.8 HV and minimum SR of 0.432 µm have been obtained at the optimal parametric setting of TND = 15 mm, SS = 0.4 mm, TSS = 900 rpm, and WA = 57.5° with the desirability values for MH, SR and combined are 0.832, 0.933, 0.881 respectively. The F-value of the MH model = 109.18 and SR Model = 78.24 implies that both models are significant and both models have excellent prediction strength. The percentage error between predicted and actual values of MH and SR in confirmation run results is less than 5 %.

Multi-parametric optimization of convex spherical surface drilling of Ti6Al4V using finite element model and Taguchi coupled desirability function analysis

Multi-parametric optimization of convex spherical surface drilling of Ti6Al4V using finite element model and Taguchi coupled desirability function analysis

Development of a magnetic nanocomposite sorbent (NiCoMn/Fe3O4@C) for efficient extraction of methylene blue and Auramine O

A rapid and efficient method for the simultaneous monitoring and recovery of Auramine O (AO) and Methylene Blue (MB) dyes from water samples is presented. This method, named ultrasound-assisted dispersive-magnetic nanocomposites-solid-phase microextraction (UA-DMN-μSPE), utilizes NiCoMn/Fe3O4@C composite sorbents. Response surface methodology (RSM) combined with artificial neural networks (ANN) and generalized regression artificial neural network (GRNN) under central composite design (CCD) was employed to optimize various parameters for efficient extraction, followed by further refinement using desirability function analysis (DFA) and genetic algorithms (GA). Under optimized conditions, the method achieved exceptional recovery rates (99.5 ± 1.2% for AO and 99.8 ± 1.1% for MB) with acetone as the eluent. Additionally, a high preconcentration factor of 45.50 and 47.30 for AO and MB, respectively, was obtained. Low detection limits of 0.45 ng mL⁻1 (AO) and 1.80 ng mL⁻1 (MB) were achieved with wide linear response ranges (5–1000 and 5–2000 ng mL⁻1 for AO and MB, respectively). The method exhibited good stability with RSDs below 3% for five recycling runs, and minimal interference from various ions was observed. This UA-DMN-μSPE-UV/Vis method offers significant advantages in terms of efficiency, preconcentration, and detection limits, making it a valuable tool for the analysis of AO and MB in water samples.

Multiobjective optimization of process parameters of AZ91D/AgNPs/TiO2 composite fabricated by friction stir processing using response surface methodology and desirability

PurposeThis paper aims to determine the optimum parametric settings for yielding superior mechanical properties, namely, ultimate tensile strength (UTS), yield strength (YS) and percentage elongation (EL) of AZ91D/AgNPs/TiO2 hybrid composite fabricated by friction stir processing.Design/methodology/approachAn empirical model has been developed to govern crucial influencing parameters, namely, rotation speed (RS), tool transverse speed (TS), number of passes (NPS) and reinforcement fraction (RF) or weight percentage. Box Behnken design (BBD) with four input parameters and three levels of each parameter was used to design the experimental work, and analysis of variance (ANOVA) was used to check the acceptability of the developed model. Desirability function analysis (DFA) for a multiresponse optimization approach is integrated with response surface methodology (RSM). The individual desirability index (IDI) was calculated for each response, and a composite desirability index (CDI) was obtained. The optimal parametric settings were determined based on maximum CDI values. A confirmation test is also performed to compare the actual and predicted values of responses.FindingsThe relationship between input parameters and output responses (UTS, YS, and EL) was investigated using the Box-Behnken design (BBD). Silver nanoparticles (AgNPs) and nano-sized titanium dioxide (TiO2) enhanced the ultimate tensile strength and yield strength. It was observed that the inclusion of AgNPs led to an increase in ductility, while the increase in the weight fraction of TiO2 resulted in a decrease in ductility.Practical implicationsAZ91D/AgNPs/TiO2 hybrid composite finds enormous applications in biomedical implants, aerospace, sports and aerospace industries, especially where lightweight materials with high strength are critical.Originality/valueIn terms of optimum value through desirability, the experimental trials yield the following results: maximum value of UTS (318.369 MPa), maximum value of YS (200.120 MPa) and EL (7.610) at 1,021 rpm of RS, 70 mm/min of TS, 4 NPS and level 3 of RF.

An experimental assessment of simultaneous reduction in vehicle tailpipe emissions employing desirability function analysis

In vehicles powered by fuel, the effort to minimize CO and HC emissions through various strategies leads to an increase in CO2, contributing to global warming. This study aimed to experimentally assess the simultaneous reduction of vehicle tailpipe emissions of CO, HC, and CO2 using desirability function analysis (DFA). On predetermined routes in Addis Ababa city, two vehicles were tested for on-road emissions at five various speeds and on five various road slopes using a portable emissions tester. Surface plots were used to display how the tailpipe emissions of CO2, CO, and HC vary with changes in vehicle speed and road gradient. The DFA results revealed that the optimal speed for simultaneous reduction of CO, HC, and CO2 emissions was 40 km/h on a flat route and 30 km/h on a 2-degree uphill, with composite desirability of 0.83 and 0.72, respectively. This study found that a speed of 30 km/h on a flat road increased CO2 by 2.82%, CO by 18.97%, and HC by 5.28% compared to an optimized vehicle speed of 40 km/h. On a 2-degree gradient, a vehicle traveling at 20 km/h exhibited a 4% increase in CO2 emissions, a 23.92% increase in CO emissions, and a 1.26% decrease in HC emissions compared to the optimized speed of 30 km/h. Adjusting speed limits according to road gradients is recommended to minimize vehicle tailpipe emissions simultaneously using DFA. This approach contributes to lowering air pollution by reducing pollutant emissions from vehicles through optimized speeds.

A multi-objective optimal method for polymer cut-off wall of earth dam based on response surface methodology and desirability function analysis

Polymer cut-off wall (PCOW) technology as a new-type reinforcement technology has been widely used in medium and small-sized earth dams. However, the density and thickness of the PCOW of earth dams are usually determined based on engineering experience. In order to establish a design method for the density and thickness of PCOW, a multi-objective optimal method based on response surface methodology (RSM) and desirability function analysis is proposed. In the framework of the proposed method, the density and thickness of PCOW are selected as the influence factors; the maximum horizontal gap length and the maximum vertical displacement difference are selected as the response values for deformation index; the maximum Mises stress of the PCOW and the stability coefficient of the earth dam slope are selected as the response values for strength index; the construction cost of the PCOW is selected as the response values for economic index. Then, nine experiments are designed using the central composite design, and response surface function models are constructed for each response value. The optimal problem of the PCOW with five response values is solved by using desirability function analysis. The weight of each response value is determined based on the possible failure mode during the dam operation period. An earth dam with height 15 m is taken as an example, the optimal density and thicknesses of the PCOW are 2.29 cm and 0.3 g/cm3 under strength failure mode, 2.15 cm and 0.29 g/cm3 under deformation failure mode. The proposed method comprehensively considers the control indexes such as deformation, strength and economy and provides an effective method for the optimal design of PCOW.

Multi-objective optimization and evolution of dissimilar welding process between Cr-Mo steel and austenitic stainless steel for power plant application

In this study, response surface methodology (RSM) was employed to optimize the welding parameters’ effects on mechanical properties of dissimilar welds between Cr-Mo steel grade (P11) and austenitic stainless steel (AISI 316LN). To determine the best welding parameters, variance analysis (ANOVA), desirability function, and perturbation analysis were used to create regression models and identify the significant parameters influencing tensile strength and hardness gaps in the weld joints. The results indicated that welding speed is the most significant parameter affecting both the austenitic hardness gap and tensile strength, while gas flow has the most significant impact on the hardness gap of Cr-Mo steel. Furthermore, welding speed positively influences the mechanical properties of dissimilar weld, whereas welding current has a slight negative effect on tensile strength. The optimum welding parameters were found to be 130 A for welding current, 70 mm min−1 for welding speed, and 13 l min−1 for welding gas flow, resulting in hardness gap values of 18.10 HV (Stainless steel side), 27.38 HV (Cr-Mo steel side), and a tensile strength of 453.90 MPa. The optimum parameter effect is concentrated at the weld interfaces between the fusion zone and the heat-affected zone. This effect led to limitations in grain coarsening, a reduction in the martensite and delta ferrite phase percentages, a slight increase in the bainite ratio, and a decrease in carbide precipitations. As a result, a homogenization of strain distribution in the optimum weld was achieved, leading to ductile fracture in Cr-Mo steel.

Numerical investigation of the strength of Al/GFRP adhesive bonding under tensile loading

Abstract In this study, adhesive bonding of aluminum (Al) to glass fiber reinforced polymer (GFRP) was investigated using finite element analysis to optimize bond strength. Mechanical surface preparation has a great influence on the chemical properties (increasing surface energy and creating a stronger bond) and mechanical properties (creating mechanical interlocking and increasing friction) of adhesive bonding. Hence, the response surface method was employed to examine the influence of groove number (1, 3, 5), groove angle (0, 45, 90°), groove shape (V-shape, square, concave), and joint type (metal–metal, metal–composite, composite–composite) on the tensile strength of the bond. To simulate the bond behavior of Al/GFRP under different parameter conditions, the cohesive zone model was used to consider the crack growth. Optimization results obtained by the desirability function method showed that the maximum bond strength was achieved with a groove number of 1, groove shape of square, groove angle of 0°, and metal–metal joint type. The optimization results predicted by the desirability function and finite element analysis were in good agreement with those obtained by experimental tests.

Istraživanje proizvodnje malog stepenastog rotorskog točka postupkom graviranja

This study aims to explore the potential of the engraving milling process to manufacture 3D structures and layouts like rotor wheels. An experimental study was carried out to manufacture a stepped rotor wheel from EN8 steel blank using a computer numeric control (CNC) engraving milling machine tool. The impact of variable parameters, namely rotational speed (SR), feed (f), and plunge feed (Pf), on maximum roughness depth (Rt) and material removal rate (MRR) was determined through a total of fourteen tests, each with two replications based on Box-Behnken design (BBD) of the response surface methodology (RSM). The best variable combination was found using desirability functional analysis (DFA) with goals of minimizing Rt and maximizing the MRR. The best values achieved at the optimal combination of variable parameters were 2.95 µm as Rt and 43.45 mm³/min as MRR. Micrographs of the rotor wheel blades and cutting tool supported the findings.

Double ionic liquid reinforced g-CN nanocomposite for an enhanced adsorption of methylparaben: Mechanism, modeling, and optimization

The efficient removal of organic pollutants, especially pharmaceuticals, from aquatic environments has attracted great attentions. Application of green, multipurpose, and inexpensive compounds is being extensively favorite as adsorbent instead of the traditional chemicals or materials. In this study, sulfonated graphitic carbon nitride was modified with two ionic liquids of polyethyleneimine and choline chloride to create a novel nanocomposite (Sg-CN@IL2 NC) and to use for removal of methylparaben (MeP) from aqueous media. After confirmation of the successful synthesized using different methods, the effective parameters for MeP removal, such as initial MeP concentration, adsorbent dose, sonication time, and temperature, as well as their interactions, were experimentally examined and modeled using response surface methodology (RSM), generalized regression neural network (GRNN), and radial basis function neural network (RBFNN). The models were then optimized using desirability function analysis (DF) and genetic algorithm (GA). The results showed that MeP adsorption: a) can be explained more accurate and reliable using GRNN (AARD% = 11.67, MAE = 15.31, RAE % = 45.42, RRSE % = 55.18, MSE = 435.86, RMSE = 20.70, and R2 = 0.995) than the others; b) reached equilibrium within 7.0 min with a maximum uptake of 267.2 mg/g at a temperature of 45 °C and a neutral pH; c) followed from Freundlich (R2 = 0.999) isotherm and PSO kinetic (R2 = 0.95) models; d) is endothermic and spontaneous; e) is mainly due to π-π stacking, electrostatic and hydrogen bonding interactions. Moreover, Sg-CN@IL2 NC showed an appropriate reusability for up to five cycles. These findings demonstrate the potential of as-prepared NC as an excellent adsorbent for removal of MeP from aqueous media.

Analytical optimization of open hole effects on the tensile properties of SS400 sheet specimens using an integrated FFD-CRITIC-DFA method

Structural components are generally composed of material discontinuities, including open holes, which are considered stress concentrators in engineering components. In view of this, assessing the influence of open holes on the tensile properties is crucial to determine the sensitivity and tensile strength of a particular material. Nevertheless, investigation of the impact of open holes on the tensile properties of SS400 steel sheets is very limited and yet to be explored. Therefore, this study was performed to optimize the effects of open holes on the tensile properties of SS400 sheet specimens based on a Full Factorial Design (FFD) experiment. Four input parameters that represent various hole configurations, which include the hole diameter, location of the hole, number of holes, and hole shape, were considered in this study to develop the experimental-based prediction models to optimize the output performance, namely yield strength, ultimate tensile strength, and ultimate elongation, commonly denoted as YS, UTS, and UE respectively. A total of 10 additional experimental trials were then utilized to verify the constructed models. In addition, the weight fractions for YS, UTS, and UE were identified using the Criteria Importance Through Inter-Criteria Correlation (CRITIC) method. Subsequently, the Desirability Function Analysis (DFA) is utilized to pinpoint the optimal parameter conditions for maximizing the tensile properties. Based on the results, all four parameters showed significant effects on the response variables, except the number of holes for UTS and hole location for UE. The diameter also recorded the highest contribution toward UTS and UE, followed by the hole shape. Regarding YS, hole diameter takes precedence, with the number of holes as the second most influential factor. Furthermore, the average absolute percent deviation for the prediction responses of 10 experimental cases were 1.06 %, 0.90 %, and 0.85 % for YS, UTS, and UE, respectively, confirming the validity of the constructed models. Meanwhile, the CRITIC method estimated the weight fractions for YS, UTS and UE from the experimental data, which were 0.3825, 0.2559, and 0.3616, respectively. The DFA-derived composite desirability, rated at 0.9820, suggests optimal conditions: a 1 mm hole diameter, centered hole location, three holes, and a hexagonal shape. The minimal deviations between predicted and experimental values affirm the robustness of the models. Overall, this investigation yields important insights for optimizing open holes and elevating the tensile performance of SS400 sheet specimens.

Turning SKD 11 Hardened Steel: An Experimental Study of Surface Roughness and Material Removal Rate Using Taguchi Method

Heat-treated steel is widely used in industrial applications due to its high strength and other desirable mechanical qualities. Grinding, which requires a lot of power and is expensive, is typically used to harden machining. In recent times, hard machining has emerged as a viable alternative to grind in select applications. In this investigation, turning operations with a carbide insert (CNMA 120408-KR3215) were carried out on SKD 11 (53 HRC) hardened steel. A total of nine machining tests were completed using the L9 orthogonal array. The response variables considered in this study were surface roughness (Ra) and material removal rate (MRR). The analysis of the signal to noise ratio reveals that the optimal combination of cutting process parameters for achieving a desired surface roughness consists of a cutting speed of 119 m/min, a feed rate of 0.11 mm/rev, and a depth of cut of 0.2 mm. The contribution of each process parameter to the machining performance of the carbide tool-work piece combination is determined through the use of ANOVA. Depth of cut has the greatest impact (57.33%) to MRR, while feed rate has the highest contribution (82.15%) to Ra. Moreover, desirability function analysis (DFA) was conducted to optimize the multiple responses. DFA suggested that, to attain a satisfactory response to the output parameters, higher range of cutting speed, depth of cut, and lower range of feed rate are appreciable; therefore, the analytical findings suggest that a cutting speed of 189 m/min, feed rate of 0.11 mm/rev, and a depth of cut of 0.5 mm can induce a favorable Ra of 0.971 μm and MRR of 10.248 cm3/min. In hard machining, cutting speed has a bigger influence on surface finish than feed rate.

Experimental study on static and impact properties of concrete incorporating nano-SiO2, nano-Al2O3 and Fe2O3 in single and combined forms

This paper presents a comparative evaluation of the single and combined effect of nanoparticles on the static and impact properties of concrete. Nano-SiO2, nano-Al2O3 and nano-Fe2O3 were used separately and in combined forms as partial replacements of cement by 1% and 2% by weight. The impact of nanoparticles on the unit weight, compressive strength, split tensile strength, flexural strength, toughness, fracture energy, impact performance of concrete was experimentally examined. In addition, the effect of these nanoparticles on the microstructure of concrete was investigated using SEM analysis. Moreover, to evaluate the commercial production of such concretes, the cost effectiveness of use of nanoparticles in concrete was also discussed. Desirability function analysis was also conducted to compare the overall performance of the tested concretes. The results revealed that use of nano materials in concrete had a positive effect on improving their mechanical performance. Nanoparticles increased the compressive strength, split tensile strength, flexural strength, and energy absorption capacity of concrete. Use of nanoparticles didn't show any significant influence on the unit weight of concrete. Moreover, the results also showed that nano materials didn't have a good influence on the impact performance of concrete. SEM analysis showed that use of nanoparticles improved the microstructure of concrete.

Enhancing Cutting Efficiency and Minimizing Forces for Nomex Honeycomb Core Using Grey Relational Analysis and Desirability Function Analysis.

Nomex Honeycomb core is the foundational building block for manufacturing aerospace composite components. Its usage requires machining honeycomb in complex aerodynamic profiles where the quality of the core is governed by accuracy and precision of cut profiles. The assessment of accuracy and precision is directly related to forces induced in the cutting tool and cutting efficiency. These two parameters form the basis of a multi-objective function that this paper aims to optimize for the milling operation. The parameter of depth of cut considered in this paper has not been analyzed in a multi-objective optimization study of the Nomex Honeycomb core previously. A Taguchi-based array of Design of Experiments followed by Analysis of Variance and correlation analysis is utilised. The results indicate that the most significant factor is the feed rate, with a percentage contribution of 72% for the cutting forces and depth of cut, with a percentage contribution of 85% in the case of cutting efficiency. The two parameters are optimized using Desirability Function Analysis and Grey Relational Analysis. The results are validated through experimental runs with an error within 5% of the statistical predictions, with the percentage improvement in cutting forces for optimum runs as compared to the worst experimental run at 47.8%. The percentage improvement in cutting efficiency likewise is 11%.

Desirability function analysis approach for optimization of fused deposition modelling process parameters

Abstract This study uses desirability function analysis to optimize the process parameters namely layer thickness, fill pattern, fill density, and build orientation for fused deposition modelling on polyethylene terephthalate glycol material. The Taguchi-desirability function analysis is one of the optimization techniques for the multi-objective decision-making process. Taguchi L9 orthogonal array is employed in the test trials during the fused deposition modelling process. The output responses measured are dimension error, ductility, and tensile strength. The goal of process parameter optimization is to attain the best of the trials for minimal dimension error and maximum tensile strength and ductility. The best feasible amalgamation of input variables is discovered for the investigated and assessed response features utilizing desirability function analysis. As per the results obtained from experiments, it is concluded that the optimal combination of parameters is layer thickness of 0.1 mm, build orientation of 0°, fill density of 90 % and hexa fill pattern. By following this combination, the error in the dimensions was slightly increased which needed to be reduced, but both tensile strength and ductility were increased which are more desirable characteristics.

Forming limit diagram and optimization of process parameters in SPIF of Inconel 625 aviation-grade superalloy

PurposeThis paper aims to optimize the single-point incremental forming process variables for realizing higher formability in Inconel 625 components and to plot the forming limit diagram for Inconel 625 aviation-grade superalloy.Design/methodology/approachThe formability of Inconel 625 components has been measured in terms of major strain, minor strain and minimum sheet thickness. Response surface methodology with desirability function analysis has been used to achieve maximum formability. The finite element analysis has been conducted at optimal parametric setting.FindingsThe derived forming limit diagram proves that the maximum forming limit for Inconel 625 is 57.5° at the optimal parametric setting, achieved with desirability of 0.995. The outcomes of finite element analysis conducted at optimal parametric setting show excellent agreement with confirmation experiment results.Practical implicationsInconel 625 superalloy is frequently used in aircraft and other high-performance applications for its superior strength.Originality/valueIt has been suggested that to enhance formability, higher tool rotation speed, minimum step-size, larger tooltip diameter and higher wall angle must be used. Wall angle is the governing parameter among all the parameters.

A multi-response optimization approach to mechanical properties improvement of dissimilar resistance spot welding joints

Purpose This study aims to investigate the effects of process input parameters (welding current, welding time, electrode pressure and holding time) on the output responses (nugget diameter, peak load and indentation) that control the mechanical properties and quality of the joints in dissimilar resistance spot welding (RSW) for the third generation of advanced high-strength steel (AHSS) quenching and partitioning (Q&P980) and (SPFC780Y) high-strength steel spot welds. Design/methodology/approach Design of experiment approach with two level factors and center points was adopted. Destructive peel and shear tensile strengths were used to measure the responses. The significant factors were determined using analysis of variance implemented by Minitab 18 software. Finally, multiresponse optimization was carried out using the desirability function analysis method. Findings Holding time was the most significant factor influencing nugget diameter, whereas welding current had the greatest impact on peak load and indentation. Multiresponse optimization revealed that the optimal settings were a welding current of 12.5 KA, welding time of 18 cycles, electrode pressure of 420 Kgf and holding time of 10 cycles. These settings produced a nugget diameter of 8.0 mm, a peak load of 35.15 KN and an indentation of 22.5%, with a composite desirability function of 0.764. Originality/value This study provides an effective approach for multiple response optimization to the mechanical behavior of RSW joints, even though there have been few studies on the third generation of AHSS joints and none on the dissimilar joints of the materials used in this study.

Parametric optimization and clad characterization of composite coating prepared on Ti-6Al-4V alloy using TiB[formula omitted]-Ni-Yb[formula omitted]O[formula omitted] precursor powders

In this work, TIG cladding on Ti-6Al-4V substrate is executed by Taguchi L9 orthogonal array with three input process parameters viz.current, scanning speed and wt% of rare earth (RE). The fluctuating output responses (clad layer thickness (CLT), microhardness (MH) and wear rate (WR)) are optimized by using the desirability function analysis technique. The developed linear regression models are found highly accurate with 97.57%, 88.60% and 96.76% adjusted R2 values for CLT, MH and WR respectively. The multi-objective function of output responses is converted into a combined objective known as composite desirability (CD). The main effect on CD gives current as the most influencing factor followed by wt% of RE and scanning speed. The analysis of variance method is also applied to check the significance of input parameters on individual responses. Pair-wise input parameter variation of individual output responses are studied through contour plots. Sub-surface morphology of coating prepared at optimum condition shows dendritic, needle-like, coralline and short-columnar type microstructure with hard phases like TiB and NiTi. The aforesaid coating has a coefficient of friction of 0.24. The mechanism for wear is adhesion with ploughing and shearing of materials.

Optimization of specific energy consumption in CNC turning of a hardened alloy steel roll at low cutting speeds

Purpose This study was conducted to analyze the effects of machining parameters on the specific energy consumption in the computerized numerical control lathe turning operation of a hardened alloy steel roll at low cutting speeds. The aim was to minimize its consumption. Design/methodology/approach The design matrix was based on three variable factors at three levels. Response surface methodology was used for the analysis of experimental results. Optimization was carried out by using the desirability function and genetic algorithm. A multiple regression model was used for relationship build-up. Findings According to desirability function, genetic algorithm and multiple regression analysis, optimal machining parameters were cutting speed 40 m/min, feed 0.2 mm/rev and depth of cut 0.50 mm, which resulted in minimal specific energy consumption of 0.78, 0.772 and 0.78 kJ/mm3, respectively. Correlation analysis and multiple regression model found a quadratic relationship between specific energy consumption with power consumption and material removal rate. Originality/value In the past, many researchers have developed mathematical models for specific energy consumption, but these models were developed at high cutting speed, and a majority of the models were based on the material removal rate as the independent variable. This research work developed a mathematical model based on the machining parameters as an independent variable at low cutting speeds, for a new type of large-sized hardened alloy steel roll. A multiple regression model was developed to build a quadratic relationship of specific energy consumption with power consumption and material removal rate. This work has a practical application in hot rolling industry.

Improved geometric accuracy in single-point incremental forming of aerospace superalloy 625 using Taguchi desirability function analysis

PurposeThis present research aims to identify the optimum process parameters for enhancing geometric accuracy in single-point incremental forming of aviation-grade superalloy 625.Design/methodology/approachThe geometric accuracy has been measured in terms of half-cone-angle, concentricity, roundness and wall-straightness errors. The Taguchi Orthogonal-Array L9 with desirability-function-analysis has been used to achieve improved accuracy.FindingsTo achieve maximum geometric accuracy, the optimum setting having a tooltip diameter of 10 mm, a step-size of 0.2 mm and a tool rotation speed (TRS) of 900 RPM has been derived. With this setting, the half-cone-angle accuracy increases by 42.96%, the concentricity errors decrease by 47.36%, the roundness errors decline by 45.2% and the wall straightness errors reduce by 1.06%.Practical implicationsSuperalloy 625 is a widespread nickel-based alloy, finding enormous applications in aerospace, marine and chemical industries.Originality/valueIt has been recommended to increase TRS, reduce step-size and use moderate size tooltip diameter to enhance geometric accuracy. Step-size has been found to be the governing parameter among all the parameters.