Combination Of Process Parameters Research Articles

Optimization of parameters and formulation of numerical model employing GRA–PCA and RSM approach for friction stir welded Ti–6Al–4V alloy joints

Abstract In this work, an endeavor was made to determine the impact of the tool’s shoulder diameter, pin profile, rotational and traverse speeds on the mechanical properties of the friction stir welded namely Ti–6Al–4V alloy joints. A total of 21 experiments were carried out based on the central composite design (CCD) concept of response surface methodology (RSM). A quadratic regression based numerical model was formulated to ascertain the relationship amidst the parameters of FSW process and the mechanical properties of the fabricated Ti alloy joints. Analysis of variance (ANOVA) was employed to confirm the importance of parameters and their interactive impacts. Optimized process parameter combinations were ascertained by grey relation based analysis (GRA) was coupled together with principal component analysis (PCA), a hybrid based approach. Single score of GRG based response was determined and GRG scores were ranked for all experiments. 1st rank was attained by the experiment no. 20 possessing a GRG score of 2.9989. Optimized values of 25mm shoulder diameter having a taper cylindrical pin geometry employed at a tool traverse speed of 40 mm/min, rotational speed of 1400 rpm resulted in the generation of flaw free Ti alloy joint possessing a largest tensile strength of 809.8 MPa, yield strength of 778.7 MPa and percentage of elongation of 6.9%. SEM based fractography ofTi alloy joint specimens announced that taper cylindrical pin geometry along with 25m shoulder diameter of employed tool have an inevitable part in generating frictional heat in ideal volumes and a perfect stirring action during FSW process.

Research on Multi-Objective Process Parameter Optimization Method in Hard Turning Based on an Improved NSGA-II Algorithm

To address the issue of local optima encountered during the multi-objective optimization process with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithm, this paper introduces an enhanced version of the NSGA-II. This improved NSGA-II incorporates polynomial and simulated binary crossover operators into the genetic algorithm’s crossover phase to refine its performance. For evaluation purposes, the classic ZDT benchmark functions are employed. The findings reveal that the enhanced NSGA-II algorithm achieves higher convergence accuracy and surpasses the performance of the original NSGA-II algorithm. When applied to the machining of the high-hardness material 20MnCrTi, four algorithms were utilized: the improved NSGA-II, the conventional NSGA-II, NSGA-III, and MOEA/D. The experimental outcomes show that the improved NSGA-II algorithm delivers a more optimal combination of process parameters, effectively enhancing the workpiece’s surface roughness and material removal rate. This leads to a significant improvement in the machining quality of the workpiece surface, demonstrating the superiority of the improved algorithm in optimizing machining processes.

Multi-sensor-based process monitoring and quality assessment during laser forming of novel titanium foam to prevent cellular structure damage

Titanium foam finds its application in different fields like battery electrodes, heat exchangers, biological prosthetics, and lightweight structures for aeronautical parts. However, the inherent property of the foam to fail during mechanical loading conditions poses a challenge in providing final shape to these materials for practical applications. The present work investigates forming of Titanium foam (Ti Foam) using inline process monitoring for achieving controlled foam deformation while maintaining its quality without failure. Infrared (IR) pyrometer and laser-based displacement sensor were used to monitor the foam deformation behavior and thermal signature. The monitoring data were used to understand the process mechanism of laser forming. The bending mechanism within the foam was analysed with respect to solid sheet bending process. The correlation of the achieved bending angles, was attempted with the process parameters for surface, microstructural, elemental, and phase transformation. A critical analysis of Ti foam behavior, like surface melting, in situ oxidation, phase transformation, and formation of nano-structures on foam surface was addressed based on process parameter combinations. Micro-computed tomography (μCT) was carried out to investigate the pore deformation mechanism. Metallurgical characterisations for foam surface and phase analysis were carried out using X-ray photoelectron spectroscopy (XPS) and tunneling electron microscopy (TEM) to determine the chemical and crystallographic states of the laser-formed surface.

Parameter optimization, microstructural and mechanical properties of fiber laser lap welds of DP1200 steel sheets

In this research, the effects of laser power, laser welding speed and laser incidence angle were evaluated in terms of weld geometry, microstructure, microhardness, fracture surfaces and tensile shear load in fiber laser welding joints of DP1200 steel sheets. Response Surface Methodology (RSM) was utilized to derive mathematical relationships between the process parameters and the tensile shear load after the tensile test. The optimum combination of process parameters was a laser power of 2800 W, a laser welding speed of 40 mm/s, and a laser incidence angle of 70°. A thermal camera was used to record the laser welding process, and the cooling rates were evaluated by analyzing this data. The influence of cooling rate upon microstructure phase formation for DP1200 steel is investigated, with continuous cooling transformation (CCT) diagrams. Also, post-welding stress and displacement were simulated by Simufact Welding software. At high heat input (55.79 J/mm) occurred coarse dendritic martensitic lath growth while at low heat input (37.6 J/mm) fine dendritic martensitic structure was observed in the weld zone was obtained owing to the high rate of cooling. The microstructure consists of full martensite at a higher than 10 °C/s cooling rate. The findings reveal that the phases in FZ and HAZ, the morphology and martensite/bainite constituents differed related to the heat input and laser incidence angle, which ultimately affects the joint’s microhardness, fracture dynamics and tensile shear load.

Enhancing Electrical Conductivity of Stretchable Liquid Metal-Silver Composites through Direct Ink Writing.

Structure-property-process relationships are a controlling factor in the performance of materials. This offers opportunities in emerging areas, such as stretchable conductors, to control process conditions during printing to enhance performance. Herein, by systematically tuning direct ink write (DIW) process parameters, the electrical conductivity of multiphase liquid metal (LM)-silver stretchable conductors is increased by a maximum of 400% to over 1.06 × 106 S·m-1. This is achieved by modulating the DIW print velocity, which enables the in situ elongation, coalescence, and percolation of these multiphase inclusions during printing. These DIW printed filaments are conductive as fabricated and are soft (modulus as low as 1.1 MPa), stretchable (strain limit >800%), and show strain invariant conductivity up to 80% strain. These capabilities are demonstrated through a set of electromagnetic induction coils that can transfer power wirelessly through air and water, even under deformation. This work provides a methodology to program properties in stretchable conductors, where the combination of material composition and process parameters leads to greatly enhanced performance. This approach can find use in applications such as soft robots, soft electronics, and printed materials for deformable, yet highly functional devices.

Construction of a Cutting-Tool Wear Prediction Model through Ensemble Learning

This study begins by conducting side milling experiments on SKD11 using tungsten carbide TiAlN-coated end mills to compare the surface roughness performance between two combinations of milling process parameters (feed rate and radial depth of cut), along with three ultrasonic-assisted methods (rotary, dual-axis, and rotary combined with dual-axis). The results suggest that the rotary (z-axis oscillation) ultrasonic-assisted method may provide better performance. Subsequently, this superior ultrasonic-assisted method was applied both with and without laser locally preheating assistance, respectively. Using a Taguchi orthogonal array, milling process parameters (spindle speed, feed rate, and radial depth of cut) were planned for experiments with the same cutting tool and the workpiece just mentioned above. The surface roughness serves as the objective function while being constrained by cutting-tool life. The characteristics of the smaller-the-better in the Taguchi method were applied to determine the optimal combination of process parameters. Based on the optimal milling process parameters obtained and the superior hybrid-assisted method adopted, milling experiments were repeatedly performed to collect the data on cutting force and cutting-tool wear. Feature engineering was performed on the cutting force signals, and different domain characteristics from both the time and frequency domains were extracted. Hereafter, feature selection by random forest and data standardization were further applied to feature extractions, and the data processing was thus completed. For the processed data, a cutting-tool wear prediction model was constructed by ensemble learning. This method leverages various machine learning regression models, including decision tree, random forest, extremely randomized tree, light gradient boosting machine, extreme gradient boosting, AdaBoost, stochastic gradient descent, support vector regression, linear support vector regression, and multilayer perceptron. After hyper-parameter tuning, the ensemble voting regression prediction was performed based on these ten mentioned models. The experimental results demonstrate that the ensemble voting regression model surpasses the performance of each individual machine learning regression model. In addition, this regression model achieves a coefficient of determination (R2) of 0.94576, a root mean square error (RMSE) of 0.24348, a mean squared error (MSE) of 0.05928, and a mean absolute error (MAE) of 0.18182. Therefore, the ensemble learning approach has been proven to be a feasible and effective method for monitoring cutting-tool wear.

Process Optimization of Pellet Manufacturing from Mixed Materials in Ultrasonic Vibration-Assisted Pelleting

Achieving carbon neutrality and alleviating the rural energy predicament are crucial aspects in rural areas, particularly in the severe cold regions of northeast China. Pellets serve as clean, renewable energy sources and are ideal alternative fuels. This study investigated the influencing factors and effects of mixed raw materials in ultrasonic vibration-assisted pelleting (UV-A pelleting). Rice straw and corn stover were mixed to produce pellets, and a central composite rotatable design (CCRD) was conducted to analyze the variables and their interactions on pellet density and durability. Mathematical regression models for pellet density and durability were established and then validated through ANOVA analysis. The results showed that all variables significantly affected the density and durability of pellets. The mixing ratio had a greater impact on pellet durability compared to density due to differences in ingredients. The optimal combination of process parameters included a mixing ratio of 25%, molding pressure of 4 MPa, pelleting time of 37 s, and ultrasonic power output at 200 W, resulting in a pellet density of 1301.18 kg/m3 with a durability reaching 94.26%. The desirability value (0.997) under these optimal conditions confirmed the validity of the models; further experiments also verified their effectiveness. The combustion of the optimized pellet was analyzed using thermogravimetric (TG) and derivative thermogravimetric (DTG) analysis in an air atmosphere. Four combustion stages and ignition temperature were provided.

Enhanced formation quality and tensile shear load of 7B04 aluminum alloys lap joint by impacting flow friction stir lap welding

Avoiding the upward-bending hook is a highly pressing and challenging task in the field of similar aluminum alloys friction stir lap welding (FSLW). To enhance formation quality and then obtain the welded lap joint with high tensile shear load, a novel impacting flow FSLW (IF-FSLW) process based on the X-shape reverse-threaded pin was proposed in this study. Three welding tools with different tool geometry parameters were developed to analyze how the locations of the materials accumulated zone (MAZ) influenced the joint formation and the joint loading capacity, and 7B04 aluminum alloys were chosen as research subject. Results showed that the horizontally-impacting and vertically-impacting effects of MAZ on and above the original lap interface made the hook respectively present the forky structure at the end and the downward-bending morphology, and the horizontally-impacting effect of MAZ made the stir zone (SZ) have a protruding morphology near the MAZ. Under the tensile fracture mode, the IF-FSLW joint under configuration A had a greatly heightened tensile shear load under the combined actions of the downward-bending hook and the enlarged SZ. The tensile shear load of IF-FSLW joint reached 8.62 kN/mm under the reasonable combination of tool geometry parameters and welding process parameters. The IF-FSLW process by the X-shape reverse-threaded pin provides an effective way to obtain a superb-strength lap joint of aluminum alloys.

Study of the Targeted Regulation of Rice Drying Characteristics and Quality

As the pre-process of storage and processing, rice drying is a key link to ensuring grain quality. A scientific and reasonable drying process can maintain grain quality and realize grain loss reduction, energy savings, and emission reduction. By establishing the response surface (RSM) regression model, the relationship between various experimental factors and quality indicators was analyzed, and the reasons for the results were explained. The optimized parameter combination was a hot air temperature of 43.14 °C, hot air humidity of 48.00%, initial moisture content of 23.80%, hot air velocity of 0.70 m/s, and hysteresis ratio of 3.55. Under this parameter combination, the drying characteristics (net drying time = 157.61 min, germination rate = 94.15%), processing quality (burst waist increase rate = 3.48%, whole rice rate = 70.458%), and nutritional quality (fatty acid value = 20.93 mg, resistant starch concentration = 195.26 μg/g, protein content change = 8.53 g/100 g, fat content change = 2.37 g/100 g) of rice after drying improved. The relative error between the validation results and the optimized results was 4.68%, indicating that the optimized process parameters can improve the efficiency and quality of rice drying. According to the regression model, the process reference chart with the process retrieval and prediction function was drawn, and the corresponding high-quality control scheme was given, providing a reference for the parameter settings of actual drying operations. The optimized drying process parameter combination effectively reduced the moisture gradient inside rice grains, drying stress, and the occurrence of cracks, thereby improving the appearance, quality and nutritional value of rice after drying. Moreover, the process reference chart can provide a reference for the actual preliminary processing technology of rice after production and a theoretical basis for an in-depth exploration of the mechanism of changes in rice quality.

Kinetic modelling of anthocyanins and vitamin C degradation in a maqui-citrus beverage during storage for different sweeteners and pasteurization treatments

Kinetic models to describe the degradation of anthocyanins (first-order model) and vitamin C (system of ordinary differential equations) in a novel beverage, has been used as a tool to identify the most relevant variables for the degradation kinetics and to support shelf-life estimation. The models were based on an empirical dataset, accounting for the effect of a prior processing treatment (HHP at 400 MPa- 180s; HHP at 600 MPa-180s; thermal treatment at 85 °C for 15s), storage temperature (4 or 20 °C), and the use of two different sweeteners (sucralose or stevia). Using model selection (based on AIC), we conclude that the degradation kinetics depends on a complex combination of process and product parameters. A strong temperature dependence on anthocyanins and vitamin C was observed during pasteurization. Products treated by HHP resulted in higher vitamin C concentrations during storage than those thermally treated. Finally, an interaction with the sweetener resulted in a slight stabilization of bioactive compounds. In conclusion, this study demonstrates the complexity of the degradation process of bioactive compounds in these beverages, advocating that nonlinear kinetic models serve as a tool to identify the most relevant parameters, ultimately supporting product formulation and shelf life estimation.

Analysis and optimization of the heat transfer behavior in the melt-spinning process of Nd-Fe-B ribbons

Heat transfer plays a significant role on the quality of Nd-Fe-B ribbons in the melt-spinning (MS) process. In this paper, the heat transfer behavior in the MS process of Nd-Fe-B ribbons was investigated by experiment and simulation. A full flow channel heat transfer model with high accuracy is established using the “fluid-thermal” coupling method. The temperature distribution of the cooling roller and fluid field in steady-state is obtained. The simulated and experimental results of the outlet temperature are compared and the relative error is within 5%. The microstructures of the free surface and roller-sticking surface of Nd-Fe-B melt-spun ribbons are characterized by FE-SEM, and the refined grains are formed on the roller-sticking surface of Nd-Fe-B ribbons due to rapid solidification caused by the heat transfer from the roller. Moreover, a multi-nozzle structure is proposed for the MS process. The temperature distribution on the surface of the cooling roller in the axial direction is more uniform compared with the original single-nozzle structure, and the average convective heat transfer coefficient is increased by about 1.27 times after the modification of multi-nozzle structure, with a productivity of three times. Finally, the experimental design method of Latin hypercube sampling response surface design is used to obtain the response surface relationships of rotational speed, inlet flow rate, and water inlet temperature on the surface temperature of the cooling roller, respectively. NSGA-II multi-objective genetic algorithm (MOGA) is adopted for multi-objective optimization of process parameters. The optimized combination of process parameters is obtained with a relative error of about 0.41%. The research results are helpful to understand the heat transfer behaviors in the MS process of Nd-Fe-B ribbons, improve the utilization rate of the cooling roller and enhance the production efficiency in the actual application.

Optimizing SLM process parameters using FE analysis for distortion mitigation

The investigation aimed to determine the optimal value of process parameters to minimize distortion while reducing the overall printing time. To achieve this, different combinations of process parameters are selected, and 25 sets of FE experiments are prepared using an orthogonal matrix. The coupled thermomechanical analysis with the element birth technique is used to mimic the behavior of Selective Laser Melting (SLM). Initially, a mesh convergence study is performed to reduce computation time with the least sacrifice in results. To minimize the temperature gradient, simulations are performed at different levels of bed temperature ranging from 303 K to 843 K. The minimum distortion was observed for a higher bed temperature of 843 K, as at this temperature, the gradient in temperature is minimum, resulting in the least development of residual stresses. The interactive behavior between laser power and scanning speed is studied, revealing an approximately linear trend with respect to distortion while keeping all other parameters constant. Additionally, it was observed that laser power and layer thickness also exhibited a similar linear trend with respect to distortion. To identify the optimal process parameter value within the design space, the Multi-Island Genetic Algorithms (MIGA) technique is employed, leading to a remarkable minimum distortion of 0.45 mm for a specific set of input process parameters.

Analysis of the Curing Deformation of Polyurethane Composite Solar Cell Bezels

In the present study, we investigated the deformation of polyurethane composite solar cell bezels during the curing process. To address the problem of deformation, thermochemical and curing kinetics models were developed to investigate the mechanical behavior of the resin during the curing process. The importance of the influencing factors was determined through orthogonal experiments and simulation analysis. The results showed that holding pressure had a significant effect on the amount of deformation of the bezel, followed by curing temperature, pultrusion speed, and holding time. The optimal combination of process parameters was a curing temperature of 150 °C, a pultrusion speed of 50 cm/min, a holding time of 12 s, and a holding pressure of 0.14 MPa, which aided in significantly reducing the deformation of the bezel and achieving effective control of curing deformation.

Mitigation of Mycotoxins in Food-Is It Possible?

Among microorganisms found in food, fungi stand out because they are adaptable and competitive in a large range of water activities, temperatures, pHs, humidities and substrate types. Besides sporulating, some species are toxigenic and produce toxic metabolites, mycotoxins, under adverse biotic and abiotic variables. Microorganisms are inactivated along the food chain, but mycotoxins have stable structures and remain in ready-to-eat food. The most prevalent mycotoxins in food, which are aflatoxins, fumonisins, ochratoxin A, patulin, tenuazonic acid, trichothecenes and zearalenone, have maximum tolerable limits (MTLs) defined as ppb and ppt by official organizations. The chronic and acute toxicities of mycotoxins and their stability are different in a chemical family. This critical review aims to discuss promising scientific research that successfully mitigated levels of mycotoxins and focus the results of our research group on this issue. It highlights the application of natural antifungal compounds, combinations of management, processing parameters and emergent technologies, and their role in reducing the levels and bioaccessibility. Despite good crop management and processing practices, total decontamination is almost impossible. Experimental evidence has shown that exposure to mycotoxins may be mitigated. However, multidisciplinary efforts need to be made to improve the applicability of successful techniques in the food supply chain to avoid mycotoxins' impact on global food insecurity.

Multiobjective optimization of laser welding parameters for P92 steel based on MFO/MOEAD-Kriging

P92 steel is used in steam equipment of thermal power plants and nuclear power plants. It needs to withstand high temperature and high pressure during service, so high-quality welds are required. Laser welding can easily obtain good weld quality and reduce the probability of coarse grains and welding defects in the heat-affected zone. Post-weld deformation, residual stress, and weld hardness are important indexes for evaluating weld quality, which largely depends on the combination of laser welding process parameters. The multiobjective optimization methods of moth-flame optimization (MFO) and multiobjective evolutionary algorithm based on decomposition under the Kriging model were compared in this research. It was found that the MFO algorithm has a faster optimization speed and better overall effect. The Optimal Latin hypercube sampling method was used to design the simulation test. The welding seam-forming process was simulated by the SYSWELD software. The Kriging model was used to make the nonlinear relationship between the process parameters and the weld quality indexes significant. A new data set was designed to evaluate the accuracy of the model. On this basis, the efficiency of the combination of process parameters optimized by the two algorithms was compared. The simulation and experimental results were in good agreement with the multiobjective optimization results of the MFO. In the experimental verification, the optimized weld quality was analyzed from the aspects of microstructure and mechanical properties. The results showed that the multiobjective optimization method quickly and effectively reduces the probability of weld quality defects in actual welding, thereby improving weld quality. This multiobjective optimization method provides valuable research ideas for engineering research and development to effectively shorten its development cycle.

Investigation and Optimization on Parameters of Gas Additive Powder Mixed Near Dry –EDM (GAPMNDEDM) by using Taguchi based- Grey Relational Optimization

The Gas Additive Powder Mix near Dry Electric Discharge Machining (GAPMND-EDM) serves a manufacturing purpose, specifically for cutting hard materials. This involves the removal of material from a workpiece via fusion, ablation, and evaporation caused by the heat energy generated through electric sparks during energy supply. The process offers several advantages in terms of performance and characteristics. This research project aims to optimize key process parameters, including material removal rate, surface roughness, residual stresses, and microhardness. The study employs a methodology that combines the standard deviation-based objective weighting method with GRA (Gray Relational Analysis) optimization to enhance the hybrid GAPMND-EDM process when applied to EN-31 material. Experimental runs were conducted to evaluate the impact of various input factors, such as pulse-on time, discharge current, dielectric fluid pressure, and metallic powder concentration. The taguchi-based GRA method was utilized for this purpose, and the experimental design followed an L-27 orthogonal array with the assistance of Minitab-19 software. A total of 27 experiments were performed, encompassing diverse combinations of process parameters. Subsequently, an ANOVA (Analysis of Variance) was executed to analyze the influence of pulse-on time, discharge current, dielectric fluid pressure, and metallic powder concentration on Material Removal Rate (MRR), Surface Roughness (SR), Residual Stress (RS), and Microhardness (MH). The result shows the optimal combination of parameters, denoted as A2B2C2D1, was identified as the preferred configuration.

Modelling and numerical analysis for rotatory friction welding of U75V steel rails

This article presents a rotatory friction welding (RFW) method for U75V steel rail, aiming to mitigate challenges related to property discrepancies between the as-welded joints and the rail base metal (BM) and to narrow the heat-affected zone (HAZ) in conventional flash-butt welding (FBW) joints. A rotational intermediate plate is designed for rails with non-axisymmetric cross-sections, necessitating stationary during RFW. Advantages include achieving a relatively uniform welding heat input and maintaining the peak temperature of the contact interface near A1. To implement these concepts, a 2D finite element (FE) model for the RFW process of U75V rail steel rods was established and validated through experiments with identical process parameters. Microstructure predictions derived from continuous cooling transformation diagram confirm that ferrite microstructure is formed near A1 through rail steel RFW. Subsequently, a 3D FE model for intermediate plate RFW steel rails is developed to explore appropriate process parameter combinations. A suitable process parameters combination was identified, ensuring the peak temperature of the majority model contact interface does not exceed A1, resulting in a 76.7% reduction in HAZ (from ∼50 to 11.66 mm), and axial shortening of 8.10 mm, a significant decrease compared to the usual burn-off (30–40 mm) during FBW. These findings underscore the efficacy of this innovative welding solution and emphasize the significance of simulation technology in process optimization.

Multi-Objective Optimization of Process Parameters in Laser DED Ni-Based Powder on Steel Rail Using Response Surface Design

With the rise of global industrialization, the requirements for the operating speed and carrying capacity of high-speed trains are increasingly higher. Because the wear and tear of rails gradually increases during the running of high-speed trains, strengthening or repairing rail surfaces is of paramount significance. Laser-directed energy deposition (DED) exhibits significant advantages in improving surface hardness, corrosion resistance, and abrasion resistance. Because of the multiple interacting optimization objectives, the development of a multi-objective optimization method for process parameters is significant for improving DED deposition quality. Response surface design employs multivariate quadratic regression equations to fit the functional relationship between the factors and the responses, which can be employed to find the optimal process parameters and solve multivariate problems. This study develops a multi-objective optimization model with response surface design and 2D process mappings to visually analyze the effects of scanning speed, laser power, and powder feed rate on aspect ratio, dilution rate, and microhardness. The optimal combination of process parameters for Ni-based alloys on U71Mn rail is a laser power of 431 W, a scanning speed of 5.34 mm/s, and a powder feed rate of 1.03 r/min. In addition, a multi-physics field finite element model is developed to analyze the evolution mechanism of the microstructure from the bottom to the top of the single track. This study can provide theoretical and technical support for the surface strengthening or repair of U71Mn rail.

Friction Stirs Processing - State of The Art Process to Fabricate Metal Matrix Composites

Friction stir processing(FSP) is the solid state method to fabricate the surface metal matrix composites(MMC). This process has been derived from the concept of Friction stir welding. As this process is performed in solid state, many defects related to phase transformation can be avoided. As this process is done below the melting point temperature of metal or alloy, it is easier to control the process. Machine variables (tool rotational speed, tool traverse speed, tool tilt angle etc.), tool design variables (diameter, profile and height of probe and shoulder of the FSP tool), and material properties are the main process variables to perform FSP in order to fabricate Metal matrix composite. In addition to these variables number of passes, tool traverse patterns, reinforcement particles’ content and its’ types also can be considered as process variables. Tribological properties’ comparison can be done between alloy and MMC fabricated by FSP for different combinations of process parameters. Microstructural study also can be carried out in order to see the effects of above process parameters. In this paper the above aspects are summarized and performance assessment done by various researchers has been covered.

Optimal Constrained Groove Pressing Process Parameters Applying Modified Taguchi Technique and Multi-Objective Optimization

Most engineering problems are complicated, and developing mathematical models for such problems requires understanding the phenomena through experiments. It is well known that as processing parameters with assigned levels increase, so does the number of experiments. By minimizing the number of experiments, Taguchi’s method of experimental design will help to furnish the idea of full factorial experimental design. Taguchi’s method is more appropriate for single-objective optimization problems and needs modifications while dealing with multi-objective optimization problems. Aluminum alloys are in great demand in today’s automotive and aerospace sectors due to their low density, good corrosion resistance, and excellent machinability. The material is subjected to a constrained groove pressing (CGP) process to obtain microstructural grain refinement with enhanced mechanical behavior. This paper considers AA6061 material having major alloys such as silicon and magnesium. For this work, 3 CGP process parameters (viz., displacement rate, plate thickness and number of passes) are assigned 3 levels to each parameter, acquired the test data, viz., grain size (gs), micro hardness (hs), and tensile strength (ult) based on L9 orthogonal array of Taguchi. Using a modified version of Taguchi’s methodology, it is possible to estimate the range of grain size (gs), micro hardness (hs), and tensile strength (σult) for effective combinations of the CGP processing parameters and validate the results with existing test data. A more dependable and simpler multi-objective optimization procedure is used to choose the optimal CGP processing parameters.