Optimal Combination Of Process Parameters Research Articles

An Experimental Study in Laser-Assisted Machining of AerMet100 Steel.

To solve the problems of poor surface quality and low tool life in conventional machining (CM) of AerMet100 steel, an experimental study was conducted in laser-assisted machining (LAM) of AerMet100 steel. The effects of laser power, cutting speed, feed rate, and depth of cut on the surface roughness of AerMet100 steel were studied based on a single-factor experiment. The degree of influence of each factor on the surface roughness was evaluated by analyses of variance and range in the orthogonal experiment, and the combination of process parameters for the optimal surface roughness was obtained. The order of influence was as follows: laser power > cutting speed > depth of cut > feed rate; the optimal combination of process parameters was laser power 200 W, cutting speed 56.5 m/min, feed rate 0.018 mm/rev, and depth of cut 0.3 mm. Compared to CM, the surface morphology of the workpiece under the optimization of LAM was relatively smooth and flat, the surface roughness Ra was 0.402 μm, which was reduced by 62.11%, the flank wear was reduced from 208.69 μm to 52.17 μm, there were no tipping or notches, and the tool life was significantly improved. The study shows that the LAM of AerMet100 steel has obvious advantages in improving surface quality and reducing tool wear.

Multi-objective optimization of high-power fiber laser cutting process using data augmentation-based ANN-Adam model

During fiber laser cutting of sheet metal, excessive heat-affected zone (HAZ) and recast layer thicknesses (RLT) can adversely affect subsequent precision machining and welding processes on the parts. As a result, it is necessary to optimize the process parameters to control the HAZ and RLT. In this work, laser power, gas pressure, focal plane position (FPP), and cutting speed are selected as input parameters. The cutting experiments are conducted using response surface methodology, and the factors that significantly affect the cutting quality are identified by analysis of variance (ANOVA). To improve the generalization ability and robustness of the model, a data augmentation-based artificial neural network (ANN) model is developed to achieve accurate prediction of cut quality characteristics. Further, this study performs a multi-objective optimization of the cutting process parameters using the Adam optimizer. The research results show that the mean relative errors of the ANN model in predicting the HAZ and RLT are 2.44 % and 1.55 %, respectively. The determination coefficients R2 are 0.9542 and 0.9779, respectively. The optimal combinations of process parameters obtained through optimization are laser power 8.9 kW, gas pressure 0.8 bar, FPP 11.8 mm, and cutting speed 1.3 m/min. Experimental validation shows that the relative errors between the predicted and the experiment values of the cut quality characteristics are kept within 5 % under the optimal process parameters. This fully verifies the effectiveness of the optimization model in guiding the selection of laser-cutting process parameters and improving cutting quality.

Orthogonal experimental study of ultrasound-assisted fused deposition molding

To improve the quality of fused deposition molding (FDM), we introduced ultrasonic vibration into the molding process to enhance the strength of the tensile samples. We used the response surface method to optimize the FDM molding process. We carried out orthogonal experiments to investigate the effects of ultrasonic power and process parameters (extrusion temperature, hotbed temperature, and delamination thickness) on the mechanical properties of fused deposition acrylonitrile butadiene styrene products. We obtained the quadratic regression equations and response surfaces of the data through analysis of variance and significance analysis by software. Last, we received the optimum combination of process parameters for tensile strength as extrusion temperature 233.85 °C, hotbed temperature 93.38 °C, delamination thickness 0.1 mm, and ultrasonic power 43.09 W.

Parametric analysis, modeling and optimization of the process parameters in electric discharge machining of aluminium metal matrix composite

Optimizing electric discharge machining (EDM) for aluminum/SiCp metal matrix composites poses challenges due to intricate machine parameters and process complexity, impacting process economy and elevating product costs. The research aims to find the optimal combination of process parameters which include pulse on-time, pulse current, duty cycle (%), gap voltage, sensitivity and flushing pressure for EDM of Al/SiCp-MMC using a copper electrode for the selected response factors such as material erosion rate and surface roughness, Ra. The experiments were designed using the central composite design of response surface methodology and an advanced optimization technique known as Teaching–learning-based optimization (TLBO), is applied to find the optimal combination of process parameters to obtain maximum material erosion rate subject to the desired range of surface roughness (SR), Ra. The combination of the high pulse on-time (i.e. 150 μs) and high pulse current (i.e. 12A) results in high material removal rate with deep craters on the machined surface clearly visible in SEM images contrasting the minimized surface roughness at lower values of pulse on-time (50 μs) and the pulse current (4A). Pulse on - time (Ton) is found to be the most significant factor for material erosion rate and surface roughness with percentage contribution of 70.86 and 54.9 respectively for optimization of the response. The regression models were developed at 95% confidence level for material removal rate and surface roughness with R2 value of 0.93 and 0.95 respectively signifying high degree of accuracy in predicting the response. Confirmation tests conducted to check the adequacy of the established models revealed that the percentage error between the predicted and experimental responses is found to be within acceptable levels. Electron discharge machining of the aluminium metal matrix composite at the optimized conditions could provide economical aspect in the aerospace and automobile industry.

Research on cutting force characteristics of laser ultrasonically assisted turning of cemented carbide

Cemented carbide possesses excellent characteristics such as high strength and hardness, but its poor machinability has restricted its widespread application. Laser ultrasonically assisted turning (LUAT) can enhance the machinability of workpieces and is widely used in precision machining of difficult-to-machine materials. In this paper, LUAT is applied to the cutting of cemented carbide, and the characteristics of cutting force in the machining process are studied. A theoretical model of cutting force is established by analyzing the interaction between the cutting tool, chip and workpiece during the turning process. The maximum error was found to be 18.6% by comparing with the experimental data, which better predicted the changing trend of cutting force and verified the accuracy of the model. Furthermore, experiments on LUAT of cemented carbide are carried out using the Taguchi method. The influence of cutting tools and processing parameters on cutting force is analyzed, revealing that the amplitude has the most significant impact on cutting force. The optimal combination of processing parameters is also obtained. Finally, a comparison is made between different turning methods. Compared to conventional turning, laser assisted turning, and ultrasonically assisted turning, the total cutting force in LUAT is reduced by 53.44%, 45.62%, and 21.64%, respectively, resulting in improved surface quality of the workpiece.

Numerical Simulation of Lost-Foam Casting for Key Components of A356 Aluminum Alloy in New Energy Vehicles.

The intricate geometry and thin walls of the motor housing in new energy vehicles render it susceptible to casting defects during conventional casting processes. However, the lost-foam casting process holds a unique advantage in eliminating casting defects and ensuring the strength and air-tightness of thin-walled castings. In this paper, the lost-foam casting process of thin-walled A356 alloy motor housing was simulated using ProCAST software (2016.0). The results indicate that the filling process is stable and exhibits characteristics of diffusive filling. Solidification occurs gradually from thin to thick. Defect positions are accurately predicted. Through analysis of the defect volume range, the optimal process parameter combination is determined to be a pouring temperature of 700 °C, an interfacial heat transfer coefficient of 50, and a sand thermal conductivity coefficient of 0.5. Microscopic analysis of the motor housing fabricated using the process optimized through numerical simulations reveals the absence of defects such as shrinkage at critical locations.

Optimization of porosity behavior of hybrid reinforced titanium metal matrix composite through RSM, ANN, and GA for multi-objective parameters

Titanium matrix composites (TMCs) have high specific strength and stiffness, and high-temperature TMCs can reduce weight by up to 50% when compared with monolithic super alloys while preserving equal stiffness and strength in jet engine systems for propulsion. The purpose of this work examines the use of mathematical models and learning approaches to optimize response such as porosity and control variables in synthesized hybrid titanium metal matrix composites (HTMMCs) reinforced by B4C-SiC-MoS2-ZrO2. To further understand the impacts of process factors on porosity reduction, the study employs methodologies such as the response surface methodology (RSM), integrated artificial neural networks (ANN), and genetic algorithm (GA). The findings indicate that these strategies have the potential to contribute to the industry. The optimal combination of 7.5wt.% SiC, 7.5wt.% B4C, 7.5wt.% ZrO2, 4wt.% MoS2, and 73.5wt.% Ti compositions was determined utilizing process factors such as milling period (6h), compaction pressure (50MPa), compact duration (50min), sintering temperature (1200°C), and sintering time (2h) as compared to pure Ti grade 5. The mechanical properties of the optimum combination of reinforcement weight percentage and process parameters resulted in a minimum porosity of 0.118%, density of 4.36gcm3, and micro-hardness of 63.4HRC boosted by 1.76%, and compressive strength of 2500MPa increased by 2.6%. In addition, these HTMMCs had a minimal wear rate of 0.176mm3/Nm and a corrosion resistance rate of 2.15×10-4mmpy. The investigation result analysis discovered that the RSM and combined ANN-GA models considerably enhanced the forecasting of multidimensional interaction difficulties in composite material production that were highly statistically connected, with R2 values of 0.9552 and 0.97984. The ANN-GA model provided a 95% confidence range for porosity predictions, which increased the production use of titanium-based particle composites. Furthermore, HMMCs can be utilized in the automotive and aviation industries with enhanced corrosion and wear resistance.

OPTIMASI MULTI PARAMETER MESIN 3D PRINTER FDM UNTUK MATERIAL THERMOPLASTIC POLYURETHANE GUNA MENGHASILKAN ERROR DIMENSI TERKECIL MENGGUNAKAN METODE 2K FACTORIAL DESIGN

Fused Deposition Modeling or FDM is a 3D printing technology that is commonly used in manufacturing processes based on Additive Manufacturing Technology. One of the materials that can be used on FDM machines is thermoplastic polyurethane (TPU). One measure of the quality of printed results is the machine's accuracy in producing workpieces that have the smallest possible dimensional error. So this research aims to obtain the printing parameters that have the most influence on print results and find out the optimal combination of printing process parameters on the machine to produce the smallest dimensional error for TPU material. The design of experiment method used is 2k factorial design using 4 process parameters, 2 levels and 3 responses. The parameters used are nozzle temperature, print speed, fill density, and enclosure box. The optimization results obtained an optimal combination of printing process parameters to produce the smallest dimensional error, namely nozzle temperature of 210°C, print speed of 30 mm/s, fill density of 100%, and not using an enclosure box.

Modelling and Prediction of Process Parameters with Low Energy Consumption in Wire Arc Additive Manufacturing Based on Machine Learning

Modelling and Prediction of Process Parameters with Low Energy Consumption in Wire Arc Additive Manufacturing Based on Machine Learning

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.

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.

Parameters Optimization and Physicochemical Properties Characterization of Nanobiochar Prepared from Apple Branches by Ball Milling

Nanobiochar exhibits promising applications across a spectrum of fields due to its exceptional physicochemical properties. Ball milling, a prominent nanobiochar synthesis method, is influenced by process factors and biochar pretreatments, yet dominant controls still remain uncertain. In this study, the apple branch bulk biochar (pyrolyzed under [Formula: see text][Formula: see text]C) was ball milled under different milling speeds, ball-to-powder mass ratios, and milling durations to investigate the optimal process parameter combinations for ball milling method. Then, we evaluated the impact of pyrolysis temperature and pretreatment temperature of bulk biochar on ball milling effect using optimized parameter combinations. Subsequently, the physicochemical properties of the bulk biochar and nanobiochar were measured. The results showed that high speed with low ball-to-powder mass ratio, low speed with high ball-to-powder mass ratio, and high pyrolysis temperature could significantly decrease the particle sizes of nanobiochar. Ball milling duration and pretreatment temperature had no significant effect on the particle sizes of biochar. The optimal combination of the rotational speed, ball-to-powder mass ratio and milling duration was 400 RPM, 100:1, 2[Formula: see text]h and 700 RPM, 20:1, 2[Formula: see text]h. The bulk biochar pyrolyzed at [Formula: see text][Formula: see text]C could be milled into biochar nanoparticles with an average particle size smaller than 100[Formula: see text]nm via above parameters. Nanobiochar shows improved properties such as specific surface area, cation exchange capacity, and surface functional groups over bulk biochar. This study provides a scientific basis for the widespread application of ball milling in the field of preparing nanobiochar.

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.

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.

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.

Finite Element Simulation and Microstructural Evolution Investigation in Hot Stamping Process of Ti6Al4V Alloy Sheets.

Titanium alloy hot stamping technology has a wide range of application prospects in the field of titanium alloy part processing due to its high production efficiency and low manufacturing cost. However, the challenges of forming titanium alloy parts with large depths and deformations have restricted its development. In this study, the hot stamping process of a Ti6Al4V alloy box-shaped part was investigated using ABAQUS 2020 software. The thermodynamic properties of a Ti6Al4V alloy sheet were explored at different temperatures (400 °C, 500 °C, 600 °C, 700 °C, 800 °C) and different strain rates (0.1 s-1, 0.05 s-1, 0.01 s-1). In addition, the influence law of hot stamping process parameters on the minimum thickness of the formed part was revealed through the analysis of response surface methodology (RSM), ultimately obtaining the optimal combination of process parameters for Ti6Al4V alloy hot stamping. The experimental results of the hot stamping process exhibited a favorable correlation with the simulated outcomes, confirming the accuracy of the numerical simulation. The study on the microstructure evolution of the formed parts showed that grain refinement strengthening occurred in the part with large deformation, and the formed box-shaped parts exhibited a uniform and fine microstructure overall, demonstrating high forming quality. The achievements of the work provide important guidance for the fabrication of titanium alloy parts with large depths and deformations used in heavy industrial production.

Optimizing the Preparation Process of Refined Asphalt Based on the Response Surface and Preparation of Needle Coke.

Refined asphalt was prepared by solvent extraction sedimentation based on the response surface design, using washing oil and kerosene as solvents and the coal tar pitch as raw materials. The mathematical models of the refined asphalt yield, quinoline insoluble (QI) content, ash content, solvent-to-oil ratio, aromatic-to-aliphatic hydrocarbon ratio, extraction temperature, and sedimentation time were proposed, analyzing the influence of each factor and their interactions on the response values. Therefore, the optimal combination of preparation process parameters and better operation window was obtained by optimizing the experiment. Meanwhile, refined asphalt with high QI content and low QI content was selected as raw material, and the needle coke was prepared through the process of carbonization and calcination. The influence of QI content on the composition and the structure of green coke and needle coke was investigated by X-ray diffraction (XRD), Raman spectra, and polarizing microscopy characterizations. The results showed that the solvent-to-oil ratio is 1.2, aromatic-to-aliphatic hydrocarbon ratio is 1.1, sedimentation time is 2 h, and extraction temperature is 110 °C, resulting in the yield of refined asphalt being 76%, QI content being less than 0.1%, and ash content being less than 0.05%, which meets the requirement of the high-quality needle coke. Otherwise, refined asphalt with lower QI content easily generates a mesophase with more fibers and a large structure in the thermal conversion process, and the corresponding green coke and needle coke have a relatively regular carbon microcrystalline structure.

Experimental optimization of machining GH4145 by atomizing discharge ablation milling

Atomizing discharge ablation milling (ADAM) technology is an efficient discharge machining technology derived from the traditional electrical discharge machining (EDM) method, which can be used to efficiently machine hard-to-machine materials such as nickel-based superalloy. In this present, the performance of machining nickel-based superalloy GH4145 by ADAM and Air near-dry EDM were compared, and the experimental results showed that the material removal rate (MRR) obtained by ADAM was nearly double that of the latter. A single-factor experiment were conducted to investigate the effect of electrode rotation speed on ADAM’s processing performance. Subsequently, an orthogonal experimental method was used to design the experiment. The signal-to-noise ratio analysis method was used to systematically study the performance characteristics of ADAM, including the influence of atomization amount, oxygen pressure, discharge current, duty ratio on MRR and tool electrode relative wear rate (TWR). The results showed that discharge current was the most influential processing parameter on MRR and TWR. Finally, the optimal combination of processing process parameters that met the requirements of various processing effect evaluation indicators were obtained and the correctness of the single objective optimization results was verified through experiments.

EXPERIMENTAL INVESTIGATIONS ON THE ELECTROCHEMICAL MACHINING OF D3 DIE STEEL MATERIAL AND MULTI-OBJECTIVE PARAMETER OPTIMIZATION

The removal of metal in the form of sludge can be termed an ECM process, having primary salient features consisting of the tool, workpiece, power supply and electrolyte solution. ECM is a potential nontraditional machining process that includes too many factors contributing to process performance. Therefore, obtaining optimal factor combinations for higher efficiency is complex. In this research work, machining parameters are voltage, powder concentration and electrolyte concentration, based on which material removal rate (MRR) and surface roughness (SR) are derived. Regression analysis, signal-to-noise ratios and analysis of variance (ANOVA) are performed to determine the optimal levels and to examine the effects of machining parameters on MRR and SR. The results derived from the experiments noticed that the MRR decreases when the voltage level is increased and surface roughness decreases for the applied voltage. In case of an increase in powder concentration, MRR decreases from range 4 to 6 and further reduces to 6–8. In case of SR, it additionally decreases on the maximum level of powder concentration. In electrolyte concentration, the MRR decreases on the increment of electrolyte and increases after a short period of time; simultaneously SR is increased from range value 10–20 and decreased with a maximum level 20–30 of NaCl solution. A comparison of MOORA-PCA and TOPSIS-PCA demonstrates the superiority of TOPSIS over MOORA technique. Statistical results (95% confidence level) indicate that the voltage, powder concentration, and electrolyte concentration affect the surface roughness by 69.50%, 8.67% and 14.59% in the electrochemical machining of d3 die steel. Therefore, the optimum combination of process parameters corresponding to voltage: 45[Formula: see text]V, powder concentration: 4[Formula: see text]g/L, and electrolyte concentration: 10 NaCl/L, respectively, were found to yield the desired result. The prediction accuracy of the TOPSIS-PCA hybrid approach model is found better than MOORA-PCA technique.