Multi-response Optimization Of Process Parameters Research Articles

Investigations and Multi-response Optimization of Process Parameters for SS316L Cold Metal Transfer-Wire Arc Additive Manufactured Samples

Investigations and Multi-response Optimization of Process Parameters for SS316L Cold Metal Transfer-Wire Arc Additive Manufactured Samples

Multi-response Optimization of Process Parameters in Stretch Blow Molding of PET Plastic Bottles

Multi-response Optimization of Process Parameters in Stretch Blow Molding of PET Plastic Bottles

Experimental investigation to assess the surface integrity in WEDM of Al-based hybrid composite material

Aluminum-based hybrid composites are used in various engineering applications due to their unique properties, such as high strength, good wear resistance, excellent corrosion resistance, and low density. Aluminum metal matrix (AMMC) composites reinforced with ceramic particles can be more difficult to machine than traditional aluminum alloys because of their increased hardness and abrasiveness. Wire electric discharge machining is a non-traditional machining process that uses a thin metal wire, typically made of molybdenum, brass or tungsten to cut through a workpiece using electrical discharge sparks. This process is useful for precision machining of complex shapes and profiles in hard or difficult to machine materials. WEDM is a commonly used process for cutting Al-SiC-ZrO2 HAMMCs due to their hardness and difficulty in machining using conventional techniques. The present study examines the relationship between machining parameters like pulse on time (TON), pulse off time (TOFF), peak current (IP) and wire feed (WF) and material removal rate (MRR) and surface roughness ( Ra) for Al-SiC-ZrO2 in WEDM. The Box-Beckham Design was used to plan the experiments, and response surface method was employed to develop the models. The pulse duration, TOFF, IP, and quadratic terms of [Formula: see text] and [Formula: see text] are the most influential factors of the MRR. The TON, IP and interaction term (TON × IP), and [Formula: see text] were the most significant factors for Ra. Multi-response optimization of process parameters was obtained using the desirability function approach. Furthermore, the machined samples were analyzed for surface integrity using SEM techniques. The prediction from this model validated the confirmation results.

Multi-response optimization of process parameters during friction stir welding of AA2014-AA7075 using TOPSIS Approach

In this study, multi-objective optimization for Friction stir welding of dissimilar AA2014-AA7075 has been presented to provide optimum tensile strength, hardness, and % of elongation. The input parameters considered for the analysis are tool rotational speed, feed, and tilt angle. Experiments are designed based on Taguchi L9 orthogonal array. Investigative analysis on the effect of input parameters on the responses is carried out using the MINITAB14 software package. The parametric influence on responses is discussed through the main effects plot. Further, multi-objective optimization is performed with the Technique for order of preference by similarity to ideal solution (TOPSIS). Results demonstrated that tool rotational speed is the most significant factor affecting the response followed by feed and tilt angle. The optimum cutting parameters obtained are tool rotational speed 710 rpm, Feed 30 mm/rev, and Tilt angle 2o.

Multi-response optimization of process parameters in nitrogen-containing gray cast iron milling process based on application of non-dominated ranking genetic algorithm

As a new, high-strength and clean cast iron material, nitrogen-containing gray cast iron has excellent properties and a wide range of application prospects. However, the excellent material properties of the material not only make the machinability challenging, but also the high efficiency and quality of the machining process is a pressing issue. Therefore, it is necessary to study the machining characteristics of nitrogen-containing gray cast iron to obtain the optimal machining parameters to enrich the research work on nitrogen-containing gray cast iron. In this paper, the machining characteristics of nitrogen-containing gray cast iron are systematically studied, and the effects of cutting parameters on milling force, milling temperature, and surface roughness are analyzed. And, based on the machinability assessment, the objective function weights under different production requirements are determined by using hierarchical analysis trade-offs, and an integrated optimization model based on non-dominated ranking genetic algorithm and hierarchical analysis (AHP) is proposed. The model outputs the optimal combination of milling parameters by inputting the cutting speed (vc), feed rate per tooth (fz) and cutting depth (ap), surface roughness and cutting efficiency as the objective functions. The experimental results show that cutting depth has the greatest effect on the cutting force and cutting speed has the greatest effect on the cutting temperature and the surface roughness. The passivation effect of nitrogen on the graphite tip resulted in an increase in both cutting force and cutting temperature. The parameter optimization results indicated that the optimized roughing parameters significantly improve the surface quality while machining efficiently; the optimized finishing parameters improve Ra by 23.53% while ensuring higher MRR, which can achieve efficient and high-quality machining under different production requirements and provide an experimental basis for practical engineering applications of nitrogen-containing gray cast iron.

A PDCA Framework towards a Multi-Response Optimization of Process Parameters Based on Taguchi-Fuzzy Model

Multi-response optimization problems investigation is a crucial element in initiatives designed to enhance quality and overall productivity for manufacturing processes. Since no particular algorithm can be employed for all multi-response problems, defining the method that is utilized as a problem-solving technique is a vital step in the process factors optimization. Identifying a formal procedure of implementing the improvement approach in a multi-criteria decision-making problem is a critical need to ensure the consistency and sustainability of the enhancement methods. In this study, a Plan–Do–Check–Act (PDCA) framework is implemented for a case study in the food industry under which a multi-response optimization problem is investigated. The design of experiment (DOE) is used to examine the effect of process parameters on the quality responses by using the Taguchi method to find the optimal setting for each parameter. An orthogonal array (OA) and signal-to-noise (SNR) ratio is employed to investigate the performance characteristics. Each performance characteristic is then converted into a signal-to-noise ratio, and all the ratios are then fed into a fuzzy model to produce a single comprehensive output measure (COM). The average COM values for various factor levels are calculated, and the level that maximizes the COM value for each factor is identified as the optimal level. Results indicated the effectiveness of the applied method to find the optimal factor levels for the multi-response optimization problem under study. The global optimal factor levels that are driven from the fuzzy logic for the studied parameters are 1250, 40, 7.5, and 1:2, for the speed, frying time, cooking time, and the coating ratio, respectively. Means of all the studied quality characteristics were closer to the target values when compared with the initial factors’ settings.

Multi-response Optimization of Process Parameters in Fabricating Al 2124/GO Metal Matrix Composite by Friction Stir Processing

Multi-response Optimization of Process Parameters in Fabricating Al 2124/GO Metal Matrix Composite by Friction Stir Processing

Optimization of the pre-application process of a reactive resin before direct dyeing for imparting reserve effects on Elastane/cotton pant garments

The present study investigated multi-response optimization of process parameters of a new formula based on reactive resins applied on Elastane/cotton pants. Bleached samples were treated with a reserve agent based on reactive resins, then were dyed with a direct dye to impart reserve and contrast effects. The response surface methodology (RSM) and desirability functions were employed to obtain simultaneous optimum settings of input parameters including liquor ratio bath, resin concentration, curing time and temperature. Different tests were carried out on the resin treated and dyed samples: color strength of the front and the back side of the pant garment, tear strength, washing, and rubbing color fastness.The main goal of this work is to propose a new solution: the application of a resin followed by a dyeing process to obtain special reserve and contrast effects on pants and to propose optimized conditions to succeed. Under the optimized conditions, the experimental values of liquor ratio bath, resin concentration, curing temperature and curing time agreed closely with the predicted values. The optimized process parameters for maximum tear strength, maximum color fastness properties and a minimum color strength of the front side of the pant garment were found to be: liquor ratio bath as 12.22:1, concentration of resin as 183 g/L, curing temperature as 140 °C and curing time as 12 min. 20 s.

Multi-Response Optimization of Process Parameters in Nitrogen-Containing Gray Cast Iron Milling Process Based on Application of Non-Dominated Ranking Genetic Algorithm

Multi-Response Optimization of Process Parameters in Nitrogen-Containing Gray Cast Iron Milling Process Based on Application of Non-Dominated Ranking Genetic Algorithm

Multi-Response Optimization of process Parameter in Vertical Milling Machine of EN 31 Using Taguchi Method

The purpose of this experimental research is to compare the effectiveness of using Taguchi approaches for multi-response optimization of process parameters in Vertical Milling Machine of EN 31 Material intending to minimize surface roughness and tool wear rate while maximizing material removal rate to improve the productivity of the process with coated carbide insert. Taguchi L9 and Annova have been applied for experimental design and analysis. This experiment shows that feed and depth of cut are factors that are important for tool wear, Depth of cut is a notable factor for Material Removal Rate and feed is the most notable factor for surface roughness. Spindle speed has little effect on tool wear rate, surface roughness, and material removal rate. Mathematical models for three response parameters i.e. tool wear rate, surface roughness, and material removal rate were obtained by regression analysis

Multi-response optimization of process parameters in friction stir welded aluminum 6061-T6 alloy using Taguchi grey relational analysis

PurposeFriction stir welding (FSW) is considered an environmentally sound process compared to traditional fusion welding processes. It is a complex process in which various parameters influence weld strength. Therefore, it is essential to identify the best parameter settings for achieving the desired weld quality. This paper aims to investigate the multi-response optimization of process parameters of the FSWed 6061-T6 aluminum (Al) alloy.Design/methodology/approachThe input process parameters related to FSW have been sorted out from a detailed literature survey. The properties of weldments such as yield strength, ultimate tensile strength, percentage elongation and microhardness have been used to evaluate weld quality. The process parameters have been optimized using the Taguchi-based grey relational analysis (GRA) methodology. Taguchi L16 orthogonal array has been considered to design the experiments. The effect of input parameters on output responses was also determined by the analysis of variance (ANOVA) method. Finally, to corroborate the results, a confirmatory experiment was carried out using the optimized parameters from the study.FindingsThe ANOVA result indicates that the tool rotation speed was the most significant parameter followed by tool pin profile and welding speed. From the confirmation test, it was observed that the optimum FSW process parameters predicted by the Taguchi method improved the grey relational grade by 13.52%. The experimental result also revealed that the Taguchi-based GRA method is feasible in finding solutions to multi-response optimization problems in the FSW process.Originality/valueThe present study is unique in the multi-response optimization of FSWed 6061-T6 Al alloy using the Taguchi and GRA methodology. The weld material having better mechanical properties is essential for the material industry.

Multi-response optimization of process parameters of GMA welding of dissimilar AA 6061-T6 and AA 5083-O aluminium alloy for optimal mechanical properties

The sufficient tensile strength and hardness are important parameters to decide soundness of the joint; that is dependent on the welding parameters. The influence of welding parameters of gas metal arc (GMA) welding on mechanical properties (tensile strength and hardness) of the joint of dissimilar aluminium alloys 6061-T6 and 5083-O has been investigated. A mathematical model is developed to predict tensile strength and weld hardness of the joint. Response surface methodology is applied in the study. Three factors (welding current, welding voltage and gas flow rate), three levels and rotatable central composite design with full replications technique was used. The results indicate that welding current has the greater impact on tensile strength and weld hardness of the joint; followed by welding voltage and gas flow rate. Tensile strength and weld hardness are increasing with increase in welding current. Welding voltage and gas flow rate have negative effect on tensile strength and weld hardness. The optimum welding parameters is investigated to maximize tensile strength and hardness of the joint under the constraint of used GMA welding machine. A welding current of 182 Amp, welding voltage of 20 V and Ar gas flow rate of 17 lit/min are found optimum welding process parameters.

Multi-response optimization of process parameters using Desirability Function Analysis during machining of EN31 steel under different machining environments

Abstract In recent years, industries are opting for machining at high cutting speed to gain higher productivity without compromising the quality of the product. High-speed machining results in higher cutting zone temperature which adversely affects the surface qualities, dimensional accuracy of the components and also the tool performance. Thus, it becomes essential to determine the optimal value of machining parameters and cutting conditions that can provide superior results in terms of tool performance and surface qualities. In this study, Face milling operations were performed on EN-31 alloy steel as per Taguchi orthogonal L18 array using tungsten carbide inserts in different cooling environments by varying the lubricant flow rates. Desirability Function Analysis (DFA) was used for concurrent optimization of Tool flank wear and Surface Roughness. The optimal parameter combination for multi-responses is obtained and results show an improvement in composite desirability (CD) at optimum parameter setting. Further, Regression analysis was utilized to develop the mathematical model for the determination of the relationship between process variables and output responses.

Multi-response optimization of process parameters for laser beam welding of AA6061-Ti6Al4V by grey relational analysis

Laser beam welding (LBW) is employed for consolidating distinct materials, aluminium (AA6061) and titanium (Ti6Al4V). LBW, attributable to appealing highlights, to be specific, low heat input, high heat focus, high-power density, and low contortion, is a more useful technique for welding and fixing airplane and turbine motor components built from superalloy. This investigation concentrates on optimizing process parameters for laser beam welding of AA6061-Ti6Al4V utilizing factorial-based grey relational analysis. The welding input parameters assume a fundamental part in optimized preferred weld quality. The input parameter picked were laser power, welding speed, and offset. The welding is done for 27 specimens to elevate and endorse the mechanical and metallurgical properties. This welding is completed with three sets of process parameters with three levels of values. The three-process parameter with three sets of values are welding power (1600–2000 W), welding speed (2–2.4 m/min), and offset (0.2–0.4 mm). ANOVA technique was utilized to evaluate the centrality of variables on the finest quality of the weldment. ANOVA results demonstrated that laser power assumed a significant part of the quality targets (Tensile strength and hardness) of the weldments trailed by welding speed and offset. Grey relational analysis is implemented to optimize the input parameters at the same time considering multiple output variables.

Multi-response optimization of process parameters in friction stir welding of dissimilar aluminum alloys by Grey relation analysis (AA 6061-T6 & AA5083-H111)

In the fusion of different solid-state alloys (150X150X6 mm) friction stir welding techniques are carried out using rotational speeds, transverse speeds and varying pin profiles. Emerging trends to join different alloys, heat-treated (AA 6061-T6) and non-heat can be treated (AA 5083-H111) is an important part of the joining dissimilar alloys. In this investigation, the experimental design using the response surface method must predict the parameters of the optimized welding method by using non-linear multi responsive optimization techniques. This technique, while using applications for experimental observations developed the mathematical model, must achieved the welding standards using optimal input and output parameters. These results use a confirmation process with reference to the ANOVA model which is used to develop and find out the optimal value of defect-free welding. It has developed a multivariate mathematical analysis for the relationship between the mechanical properties of the model. And using a scanning electron microscope from a microstructure must reveal dominant problems such as tunnel formation defects, voiding, plunging, etc.

Study of Surface Characterization and Parametric Optimization during Wire Electric Discharge Machining for Inconel 625

This presented article focuses on surface characterization and assessing the satisfactory machining condition of WEDMed Inconel 625. This work material has been received remarkable attention to the industrial and academic organization for its end use applications. WEDM is well-known machining process for intricate shape cutting and machining hard materials. The experimental design was planned according to L27 orthogonal array (OA), by varying controllable process parameter (i.e. Wire-Tension, Wire-speed, Flushing-Pressure, Discharge-Current and Spark-on Time), each parameter varied at four discrete levels, within the selected parametric domain. WEDMed surfaces have been investigated with a focus to the surface characterization of selected machined surface through captured images from scanning electron microscope (SEM). Eventually, multi-response optimization of process parameters was sought by using a combination of nonlinear regression modelling, fuzzy inference system (FIS) with Teaching Learning-Based Optimization (TLBO) algorithm. The obtained TLBO result was compared with the Genetic algorithm (GA). The results show that optimization algorithms are effective tools for getting satisfactory optimal machining conditions during WEDM process of Inconel 625.

Experimental investigation of surface integrity after wire electro-discharge machining of Ti–6Al–4V

This article presents the relationship of machining parameters containing pulse-on time (Ton), pulse-off time (Toff), peak current (IP) and servo voltage (SV) on surface integrity characteristics, including white layer thickness (WLT), heat-affected zone (HAZ) and surface crack density (SCD) and also on material removal rate (MRR), after wire electric discharge machining of Ti–6Al–4V. Taguchi’s method was utilized to design the experiments, and response surface methodology (RSM) was employed for developing the empirical models. Results indicated that Ton and IP played a significant role on surface integrity characteristics. In addition, the microstructure of selected machined samples was analysed using a field emission scanning electron microscope (FESEM) and energy-dispersive X-ray (EDX) analysis. Accuracy of models was examined using residual analysis and confirmation runs. Finally, multi-response optimization of process parameters was obtained using desirability approach. Results can be used to improve the quality of the machined workpiece significantly to fulfil the requirements of the various industries. The novelty of this research is mainly investigation and multi-response optimization of all the surface integrity characteristics at the same time.

Machinability model and multi-response optimization of process parameters through regression and utility concept

Over the years, it is essential to produce appropriate dimensions with quality parts for the use in various automotive components. This paper presents modelling and multi-optimisation exploration on the hard part turning of EN 24 grade steel at 48 HRC with coated carbide multilayer inserts for three roughness factors (Ra, Rz, and Rt). Taguchi L16 orthogonal design with three input parameters and three quality characteristics output was applied to suitably model the process requirements. The second model provides a high coefficient of determination (R2 = 0.98 for Ra, 0.97 for Rz and 0.95 for Rt respectively) compared with the linear model that demonstrates high significance. The ideal parametric setting for different multiple quality features was found to be the depth of cut (0.4 mm), feed rate (0.04 mm/rev) and cutting speed (200 m/min) respectively. The multi-responses optimisation has been simultaneously performed using Taguchi method and the utility concept.

Multi-Response Optimization of Process Parameters in AWJ Machining of Hybrid GFRP Composite by Grey Relational Method

In recent days, AWJ is emerged as one of the best tools for machining of fiber reinforced polymer. In this study, multi-response optimization of process parameters is carried out using grey relational method to reduce the surface taper and surface roughness while AWJ machining of graphite laced GFRP. The grey relational grades are analysed using ANOVA to determine the influence of process parameters. The feed rate showed highest influence (24.26 %) on the response followed by interaction effect of the operating pressure and feed rate (23.54 %) as-well-as operating pressure and standoff distance (16.12 %). Whereas the contribution of operating pressure and standoff distance on response is 11.50% and 8.20 % respectively. Delamination is not observed on the surfaces machined at optimum combination of process parameters.

Multi-response optimization of process parameters for GTAW process in dissimilar welding of Incoloy 800HT and P91 steel by using grey relational analysis

The dissimilar welding of Incoloy 800HT and P91 steel using Gas Tungsten arc welding process (GTAW) This material is being used in the Nuclear Power Plant and Aerospace Industry based application because Incoloy 800HT possess good corrosion and oxidation resistance and P91 possess high temperature strength and creep resistance. This work discusses on multi-objective optimization using gray relational analysis (GRA) using 9CrMoV-N filler materials. The experiment conducted L9 orthogonal array. The input parameter are current, voltage, speed. The output response are Tensile strength, Hardness and Toughness. To optimize the input parameter and multiple output variable by using GRA. The optimal parameter is combination was determined as A2B1C1 so given input parameter welding current at 120 A, voltage at 16 V and welding speed at 0.94 mm/s. The output of the mechanical properties for best and least grey relational grade was validated by the metallurgical characteristics.