Optimal Combination Of Process Parameters Research Articles

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.

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

Wire arc additive manufacturing (WAAM) has attracted increasing interest in industry and academia due to its capability to produce large and complex metallic components at a high deposition rate. One of the basic tasks in WAAM is to determine appropriate process parameters, which will directly affect the morphology and quality of the weld bead. However, the selection of process parameters relies heavily on empirical data from trial-and-error experiments, which results in significant time and cost expenditures. This paper employed different machine learning models, including SVR, BPNN, and XGBoost, to predict process parameters for WAAM. Furthermore, the SVR model was optimized by the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithms. A 3D laser scanner was employed to obtain the weld bead’s point cloud, and the weld bead size was extracted using the point cloud processing algorithm as the training data. The K-fold cross-validation strategy was applied to train and validate machine learning models. The comparison results showed that PSO–SVR predicted process parameters with the highest precision, with the RMSE, R2, and MAE being 1.1670, 0.9879, and 0.8310, respectively. Based on the process parameters produced by PSO–SVR, an optimal process parameter combination was chosen by taking into comprehensive consideration the impacts of power consumption and efficiency. The effectiveness of the process parameter optimization method was proved through three groups of validation experiments, with the energy consumption of the first two groups decreasing by 10.68% and 11.47%, respectively.

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 350[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 h and 700 RPM, 20:1, 2 h. The bulk biochar pyrolyzed at 550[Formula: see text] C could be milled into biochar nanoparticles with an average particle size smaller than 100 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.

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.

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.

Optimization of process parameters for laser cladding Stellite6 cobalt-based alloy

As a new remanufacturing technology, laser cladding can repair damaged parts in large valves in nuclear power plants. The objective is to enhance the surface morphology quality of parts post-remodeling, the paper optimizes the key process parameters in the laser cladding process. Numerical simulation was used to analyze the influence of each process parameter ( laser power, scanning speed, powder feeding speed) on the morphology of the cladding layer. The laser power and powder feeding rate were positively correlated with the width of the cladding layer, and the scanning speed was negatively correlated with the width of the cladding layer. The orthogonal experiments are designed based on the Taguchi method, and the Stellite6 single cobalt-based alloy cladding layer was prepared on the WCB low-carbon steel substrate. Analysis of variance and RSM was used to analyze the experimental results. The influence order of process parameters on the morphology of the cladding layer was determined. The regression prediction model was established to analyze the relationship between the process parameters and the morphology quality (width and height) of the cladding layer. The errors of the model are 5% and 3.5%, respectively. The optimal combination of process parameters is determined as follows: P = 1700 W, Vs = 8 mm/s, Vf = 26 g/min. The cladding layer prepared by the optimal process parameters has fine structure and good bonding. The accuracy of the predictions was validated using this model, which offered valuable theoretical guidance for predicting and controlling the geometric characteristics of the laser cladding of Stellite6 alloy.

Developing a data-driven operational guide for the texturized yarn production process: data mining and intelligence approach

PurposeThe textile industry holds immense significance in the economy of any nation, particularly in the production of synthetic yarn and fabrics. Consequently, the pursuit of acquiring high-quality products at a reduced cost has become a significant concern for countries. The primary objective of this research is to leverage data mining and data intelligence techniques to enhance and refine the production process of texturized yarn by developing an intelligent operating guide that enables the adjustment of production process parameters in the texturized yarn manufacturing process, based on the specifications of raw materials.Design/methodology/approachThis research undertook a systematic literature review to explore the various factors that influence yarn quality. Data mining techniques, including deep learning, K-nearest neighbor (KNN), decision tree, Naïve Bayes, support vector machine and VOTE, were employed to identify the most crucial factors. Subsequently, an executive and dynamic guide was developed utilizing data intelligence tools such as Power BI (Business Intelligence). The proposed model was then applied to the production process of a textile company in Iran 2020 to 2021.FindingsThe results of this research highlight that the production process parameters exert a more significant influence on texturized yarn quality than the characteristics of raw materials. The executive production guide was designed by selecting the optimal combination of production process parameters, namely draw ratio, D/Y and primary temperature, with the incorporation of limiting indexes derived from the raw material characteristics to predict tenacity and elongation.Originality/valueThis paper contributes by introducing a novel method for creating a dynamic guide. An intelligent and dynamic guide for tenacity and elongation in texturized yarn production was proposed, boasting an approximate accuracy rate of 80%. This developed guide is dynamic and seamlessly integrated with the production database. It undergoes regular updates every three months, incorporating the selected features of the process and raw materials, their respective thresholds, and the predicted levels of elongation and tenacity.

Process Parameter Optimization for CO2 Laser Polishing of Fused Silica Using the Taguchi Method.

Fused silica was polished to a high quality by a CO2 laser beam with a rapid scanning rate. The rapid scanning rate produced a line laser heat source, resulting in a "polishing line" during the polishing process. The Taguchi method was used to evaluate the comprehensive influence of polishing process parameters on the polishing qualities. Four factors, namely the length of laser reciprocating scanning (A), laser beam scanning speed (B), feed speed (C), and defocusing amount (D), were investigated in this study. The optimal process parameter combination (A1B1C1D1) was obtained. The surface roughness of fused silica was reduced from Ra = 0.157 μm to 0.005 μm. Through analysis of variance (ANOVA), it was found that laser beam scanning speed (B) had a significant influence on the polishing quality. The interaction of the two factors plays a decisive role in the determination of the best process parameters, and the influence of other multi-factor interaction can be ignored; the interaction between A × B is the largest, with a contribution of 42.69%.

Improvement of the Dimensional Accuracy of a Ti-6Al-4V Ripple Disc During Electric Hot Incremental Sheet Forming

The edge warpage of a Ti-6Al-4V ripple disc is a major forming defect during electric hot incremental forming, which can lead to a significant dimensional error. In this paper, a novel manufacturing method, namely the combination of electric hot incremental forming and electrically assisted sizing, has been proposed to improve the forming defect. The effect of process parameters on forming fracture was analysed in detail, and then an optimal combination of process parameters was obtained to ensure the successful forming of a Ti-6Al-4V ripple disc. On this basis, a sizing device and a sizing current were separately designed and analysed to eliminate the warpage defect of Ti-6Al-4V ripple discs. According to the experimental result, Ti-6Al-4V ripple discs can be satisfactorily fabricated through the method proposed.

Efficient low-carbon manufacturing for CFRP composite machining based on deep networks

ABSTRACT The drilling quality of carbon fibre reinforced polymer (CFRP) components is a key factor affecting the service life of the components, while energy saving and emission reduction in industrial production are crucial. In this study, drilling experiments were conducted on T300 plywood using a 55° coated tungsten steel drill bit, and CNN-LSTM neural network models were used to construct mapping relationships between process parameters (spindle speed, feed rate, and fibre lay-up sequence) and delamination factor and machine energy consumption. A new method of predicting the delamination factor by process parameters is proposed, and explored the optimal process parameter combinations that reduce the energy consumption of machine tools and minimise the delamination factor at the same time. The research results show that within the parameter settings, a spindle speed of 7000 r/min, a feed rate of 40 mm/min, and a lay-up sequence of [0°, 0°, −45°, 90°]6s ensure both low power consumption in the drilling process and the highest possible hole quality. This paper clearly demonstrates the feasibility of achieving low-power, high-quality drilling of CFRP through parameter optimisation, providing guidance to the manufacturing industry to improve the quality of CFRP hole-making while easing the pressure on carbon emissions.

Multi-objective Optimization of Injection Molding Process Based on One-Dimensional Convolutional Neural Network and the Non-dominated Sorting Genetic Algorithm II

<div>In the process of injection molding, the vacuum pump rear housing is prone to warping deformation and volume shrinkage, which affects its sealing performance. The main reason is the improper control of the injection process and the large flat structure of the vacuum pump rear housing, which does not meet its production and assembly requirements (the warpage deformation should be controlled within 1.1 mm and the volume shrinkage within 10%). To address this issue, this study initially utilized orthogonal experiments to obtain training samples and conducted a preliminary analysis using gray relational analysis. Subsequently, a predictive model was established based on a one-dimensional convolutional neural network (1D CNN). Input parameters from the injection molding process, including melt temperature, mold temperature, packing pressure, packing time, injection pressure, injection time, and cooling time, were used while warping deformation and volume shrinkage were considered as outputs. Global optimization was performed using the non-dominated sorting genetic algorithm II (NSGA-II), and the optimal combination of process parameters was evaluated using the criterion importance through intercriteria correlation—technique for order preference by similarity to ideal solution (CRITIC-TOPSIS). Moldflow analysis demonstrated that the obtained indicators outperformed the optimization results from orthogonal experiments, confirming the effectiveness of the injection molding process parameter optimization method based on 1D CNN-NSGA-II. In comparison to the pre-optimization results, product warping deformation decreased by 40.68%, and volume shrinkage reduced by 18.14%, and all of them meet the production requirements.</div>

Optimizing micro-EDM with carbon-coated electrodes: A multi-criteria approach

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