Optimization Of Machining Parameters Research Articles

A novel hole performance index to evaluate the hole geometry and drilling time in the electrochemical drilling process.

Electrochemical Drilling (ECD) is an unconventional method aimed at creating holes in metallic workpieces characterized by high hardness and complex structures. This study analyzes the influence of process variables, including machining voltage, electrolyte concentration, electrode rotational speed, electrolyte flushing pressure, and workpiece material, on the novel hole performance index (HPI) in electrical discharge machining (ECD). The HPI was identified as a suitable metric for simultaneously evaluating hole geometry and drilling time across various machining parameters and workpiece materials. The analysis of variance (ANOVA) method was employed to determine the significance of each machining parameter and workpiece material on the HPI. The research employed signal-to-noise ratio analysis to identify the optimal machining parameters. The findings demonstrated that the workpiece material and machining voltage were significant factors influencing HPI. The validation tests demonstrated that the proposed statistical method can significantly reduce HPI.

Investigating and Multi-Objective Optimizing WEDM Parameters for Al6061/Mg/MoS2 Composites Using BBD and NSGA-II

This study aims to optimize the Wire Electrical Discharge Machining (EDM) process parameters for aluminum 6061 alloy reinforced with Mg and MoS2 using the Box–Behnken (BBD) design and the non-dominated sorting genetic (NSGA-II) algorithm. The objective is to enhance the machining efficiency and quality of the composite material. The Box–Behnken (BBD) design was utilized to design a set of experiments with varying levels of process parameters, comprising pulse-on time, servo volt, and current. The material removal rate and surface roughness were considered as machining responses for optimization. These responses were measured and used to develop a mathematical model. The NSGA-II, a multi-objective optimization algorithm, was then applied to search for the optimal combination of process parameters that simultaneously maximizes the material removal rate and minimizes the electrode wear rate and surface roughness. The algorithm generated and evolved a set of Pareto-optimal solutions, providing a trade-off between conflicting objectives. The results of the optimization process were analyzed to identify the optimal process parameters that lead to improved machining performance. The study revealed optimal Wire Electrical Discharge Machining (WEDM) parameters for Al6061/Mg/MoS2 composites using NSGA-II. The optimized parameters, including a pulse-on time (Ton) of 105 µs, servo voltage (SV) of 35 V, and peak current (PC) of 31 A, resulted in a Material Removal Rate (MRR) of 7.51 mm3/min and a surface roughness (SR) of 1.97 µm. This represents a 15% improvement in the MRR and a 20% reduction in the SR compared to non-optimized settings, demonstrating the efficiency of the BBD-NSGA-II approach.

Modelling and analysis of the impact of chatter and cutting forces on machined surfaces in milling

The Chatter vibrations are a critical issue in milling, affecting workpiece quality and potentially damaging machines. This study analyzes the causes of chatter and proposes preventive methods. After reviewing the existing research, the transfer function is analyzed using an orthogonal cutting model, highlighting its evolution concerning system stability. A dynamic stability map is developed to help select optimal spindle speeds and cutting depths. Experiments using an innovative chatter detection device, based on noise analysis, demonstrate real-time identification of chatter, enhancing operational stability. The second part focuses on modeling cutting forces, essential for optimizing machining conditions and mitigating vibrations. A thermo-mechanical approach is applied using the Abaqus simulator and the Arbitrary Lagrangian-Eulerian (ALE) method to study the impact of cutting conditions on mechanical stress and temperature at the tool-workpiece interface. The results show that cutting parameters significantly influence both stress distribution and thermal behavior. By combining real-time chatter detection with precise cutting force modeling, this study offers an effective way to optimize machining parameters, improving process stability and operational efficiency. This integrated approach enhances the accuracy of predictive models and contributes to better machining outcomes.

Machining characteristics and optimization of TC4 alloy by mixed gas atomization discharge ablation milling (MA-DAM)

A method using mixed gas atomization discharge ablation milling (MA-DAM) for machining titanium alloys was proposed to address the processing efficiency and quality issues in conventional near-dry electrical discharge milling (N-EDM). By analyzing the discharge waveform, a pulse discharge physical model was established to reveal the machining mechanisms of N-EDM and MA-DAM. It was found that the latter had a unique ablation process compared to the former, resulting in better machining performance. The results of oxygen concentration experiments showed that compared to N-EDM, MA-DAM improved the material removal rate by 55.6 % and reduced the relative electrode wear rate by 50 %, but increased the width of the overcut by approximately 10 % and the average roughness by approximately 17 %. Orthogonal experiments and variance analysis were conducted to study the effects of the current, duty cycle, gas pressure, and atomization rate on the machining performance indicators. Finally, grey relational analysis was used for the multi-objective optimization of machining parameters to obtain the optimal process configuration for MA-DAM, which was validated through experiments.

The Machinability of Different Albromet W130 Plates Thicknesses by WEDM to the Required Surface Roughness Value.

Wire Electrical Discharge Machining (WEDM) technology represents an unconventional but vital manufacturing technology in many different industrial branches. The automotive industry and its many significant requirements bring the need to manufacture inserts and mould segments for plastic injections from Albromet W130 material, with a required roughness, Ra, from 4.5 to 5 µm so that subsequent profile etching can be eliminated. A planned experiment of 60 rounds was carried out to discover the optimal machining parameters, namely, the pulse-off time, gap voltage, discharge current, pulse-on time, and wire speed in order for the thickness of 10 to 100 mm (after 10 mm) to demonstrate the required roughness. The goal was to evaluate the surface roughness, maximise the cutting speed, and manufacture it without surface or subsurface defects. The evaluation of the planned experiment led to the establishment of optimised WEDM machining parameters with which thicknesses of 10-100 mm will always be produced with the required roughness, Ra, from 4.5 to 5 µm and with the highest possible cutting speed. It was also proven that the machining does not lead to surface or subsurface defects, and thus, the service life of the manufactured parts will not be affected.

Review of Modelling the Compaction of High Aspect Ratio AgriculturalFeedstock Materials

Agriculture feedstock materials have low bulk density and are difficult to handle and transport. Compaction can enhance the agricultural feedstock density and could be an effective solution for handling problems. To develop a better compaction process, mathematical modelling can help understand the compaction mechanism and can help predict the mechanical behaviour of feedstock materials. This paper presents a review on the energy consumption, applied pressure, as well as rheological models to predict their compaction behaviour and thus help improve compacts’ mechanical strength and reduce densification costs. In summary, energy requirement for densification of biomass depends primarily upon the pressure applied, holding on time and properties of the material. From the published literature, it could be found that the Cooper-Eaton and the Kawakita-Ludde models that considers particle rearrangement and deformation mechanism can fit pressure and density of feedstock materials during compaction. The parameters in the rheological models were correlated to the properties of the feedstock materials and can be used for optimization of machine and operational parameters.

Machining-Induced Burr Suppression in Edge Trimming of Carbon Fibre-Reinforced Polymer (CFRP) Composites by Tool Tilting

Several challenges arise during edge trimming of carbon fibre-reinforced polymer (CFRP) composites, such as the formation of machining-induced burrs and delamination. In a recent development, appropriate-quality geometric features in CFRPs can be machined using special cutting tools and optimised machining parameters. However, these suitable technologies quickly become inappropriate due to the accelerated tool wear. Therefore, the main aim of our research was to find a novel solution for maintaining the machined edge quality even if the tool condition changed significantly. We developed a novel mechanical edge-trimming technology inspired by wobble milling, i.e., the composite plate compression is governed by the proper tool tilting. The effectiveness of the novel technology was tested through mechanical machining experiments and compared with that of conventional edge-trimming technology. Furthermore, the influences of the tool tilting angle and the permanent chamfer size on the burr characteristics were also investigated. A one-fluted solid carbide end mill with a helix angle of 0° was applied for the experiments. The machined edges were examined trough stereomicroscopy and scanning electron microscopy. The images were evaluated through digital image processing. Our results show that multi-axis edge-trimming technology produces less extensive machining-induced burrs than conventional edge trimming by an average of 50%. Furthermore, we found that the tool tilting angle has a significant impact on burr size, while permanent chamfer does not influence it. These findings suggest that multi-axis edge trimming offers a strong alternative to conventional methods, especially when using end-of-life cutting tools, and highlight the importance of selecting the optimal tool tilting angle to minimize machining-induced burrs.

Multimodal machine learning-based technical success prediction in patients undergoing transcatheter aortic valve implantation

Abstract Background Procedural success is fundamental to achieving optimal clinical outcome, and prediction of procedural success and failure is of key importance in the selection of the appropriate treatment strategy in patients with severe aortic stenosis requiring aortic valve replacement. Purpose We aimed to develop a prediction model for procedural success in patients undergoing transcatheter aortic valve implantation (TAVI) using multimodal information and employing machine learning algorithms. Method In a prospective TAVI registry, patient baseline demographics, clinical history, medication, laboratory, procedural characteristics, electrocardiogram, angiography, transthoracic and transesophagel echocardiography, and computed tomography information were collected prospectively and extracted as numeric data. Data preprocessing included the imputation of missing values and the elimination of features with high collinearity. We incorporated several feature selection (FS) and classification algorithms for model development. Datasets for each target were randomly split into train/validation sets (70/10%) and hold-out test sets (20%). All machine learning processes, including preprocessing, feature selection, parameter, and hyperparameter optimization, were carried out on training and validation sets in 10-fold cross-validation. Different metrics, including area under the curve (AUC) in receiver operating characteristic curve analyses, sensitivity, and specificity, were calculated for model evaluation on the holdout test set. Procedural success was defined as technical success in the Valve Academic Research Consortium-3 (VARC-3). Technical failure differentiated between vascular and cardiac complications. Results In a total of 2,576 patients undergoing TAVI with a contemporary device between March 2012 and June 2023, 242 (9.4%) had technical failure: 70 (2.7%) had cardiac and 178 (6.9%) had vascular technical failure. The sensitivity and specificity for overall technical failure were 0.71 and 0.77, respectively, with AUC of 0.76 (95% CI: 0.69-0.84) achieved by the recursive feature elimination and AdaBoost algorithm. For cardiac technical failure, the ANOVA FS and Random Forest (RF) model yielded sensitivity and specificity of 0.71 and 0.77, with an AUC of 0.76 (95% CI: 0.65 to 0.87). Vascular technical failure showed AUC of 0.76 (95% CI: 0.69 to 0.84), with sensitivity and specificity of 0.72 and 0.73, respectively, with achieved using the ANOVA FS and RF model. Conclusion The initial results of the novel prediction model for technical success are promising. Further improvements and external validation are warranted to support the use of the proposed model in clinical practice.Central Illustration

Predicting wood moisture classes by sound frequency spectra and Explainable Machine Learning during milling

ABSTRACT Varying moisture content in wood lead to problems in the automatic selection of optimised machining parameters. Therefore, production in the wood sector can be improved by the automatic identification of moisture classes during machining. In this work, wood milling was monitored by a novel optical microphone to detect differences in wood moisture classes. A full factorial randomised design was executed along with four moisture classes (dried, conditioned, wet and frozen) and two cutting speeds as independent variables. The measured signals were segmented by extracting information from each cut and were transformed to the frequency domain. A supervised univariate feature ranking based on χ ²-tests was applied to select the most important frequencies. Machine Learning was applied to the selected spectral data to classify the cutting speeds and moisture classes. After testing several classification models, ensemble models with regression-tree learners were the best-performing model type achieved an average validation accuracy of 97.2%. The model explanation was done by Local Interpretable Model-Agnostic Explanation based on regression trees as a simple model, where the frequencies that are responsible for the separation of the data could be identified and were used for retraining the model, achieving a validation accuracy of 90.5%.

Machining performance analysis of wire-EDM for machining of titanium alloy using multi-objective optimization and machine learning approach

This study investigates the multi-objective optimization of process parameters in wire electrical discharge machining (WEDM) using titanium alloy grade 5 and zinc-coated brass microwire. The novelty lies in the comprehensive investigation of the effects of varying pulse on time (Ton), wire tension (WT), and wire feed rate (WF) on output responses, including surface roughness (SR), surface crack density (SCD), corner diameter (CD), and kerf width (KW). The Box-Behnken design, a sophisticated response surface methodology (RSM) technique, is employed to efficiently explore the complex relationships and interdependencies between these input and output parameters. The study's novel approach incorporates machine learning algorithms, including decision tree, light gradient boosting machine (LightGBM) and random forest, to predict the characteristics of the laser-cut components based on the input parameters. The contour plots generated provide valuable insights into the underlying patterns and associations between input and output features, aiding in the understanding of the WEDM process. The results reveal that the optimized values for SR, SCD, CD, and KW are obtained at specific machining conditions: Ton of 10 μs, WF of 3.79 m/min, and WT of 8 gf. Among the machine learning algorithms employed, random forest outperforms the others in predicting KW, SR, SCD and CD, exhibiting significantly lower mean squared error (MSE) and mean absolute error (MAE) values. This study contributes to the field of WEDM by providing a comprehensive analysis, incorporating advanced experimental design techniques and machine learning algorithms, which can aid in process optimization and quality control in manufacturing applications.

Multi-objective optimization of machining parameters of mild steel AISI 1018 under compressed air-assisted cooling by using genetic algorithm

Multi-objective optimization of machining parameters of mild steel AISI 1018 under compressed air-assisted cooling by using genetic algorithm

Experimental study of the surface finishing of CNC magnetic abrasive finishing based on ANN

ABSTRACT CNC Magnetic Abrasive Finishing (CNC MAF) is famous for aluminium sheet processing. This study used an artificial neural network (ANN) analytical model with a Bayesian regularisation algorithm (BR) to examine how machine parameters (rotation speed, feed rate, depth of cut, and several passes) affect surface roughness during CNC MAF of aluminium sheet. Experimental and quantitative data proved its prediction ability. ANN model to select the best machine parameters for low surface roughness. The perfect numerical solution was experimentally tested, and surface morphology and roughness were used to evaluate processing quality under optimal machine parameters. The ANN model is also compared to Taguchi L16 for prediction and optimisation. Compared to ANN, prediction and optimal surface finishing mean absolute error are 96% lower. The results show that the ANN model is better at studying machine configuration during CNC MAF of Al sheet. SEM and 3D Optical profilometer were also used to analyze the Al sheet. The UV-VIS Spectrophotometer measures Al polished sample reflectance at 200–700 nm.

Electrochemical Machining of Micro-Pit Arrays on a GH4169 Alloy with a Roll-Print Mask Using a C6H5Na3O7-Containing NaNO3 Mixed Electrolyte.

GH4169 alloy, a nickel-based superalloy known for its excellent high temperature resistance, corrosion resistance, mechanical properties, and high-temperature tribological properties, is widely used in industrial applications, such as in gas turbines for space shuttles and rocket engines. This study addresses the issue of electrolyte product residue in the electrochemical machining process of a GH4169 alloy by utilizing a C6H5Na3O7-containing NaNO3 new mixed electrolyte. Comparative investigations of the electrochemical behavior and electrolyte product removal mechanisms at different concentrations of C6H5Na3O7 additive in NaNO3 solutions were conducted. The effects of additives, applied voltage, and the rotating speed of the cathode tool on the processing performance of micro-pit arrays on a GH4169 alloy were analyzed. The results indicate that the mixed solution containing C6H5Na3O7 significantly improves the localization and geometric morphology of the micro-pits compared to a single NaNO3 solution. The optimal electrochemical machining parameters were identified as 0.5 wt% C6H5Na3O7 + 10 wt% NaNO3 mixed electrolyte, 12 V applied voltage, and 0.1 r/min rotating speed of the cathode tool. Under these conditions, high-quality micro-pit arrays with an average diameter of 405.85 μm, an average depth of 87.5 μm, and an etch factor (EF) of 1.67 were successfully fabricated, exhibiting excellent morphology, localization, and consistency.

Exploration of weak-PGML Method for Efficient Stability Control During Machining Operations

Stable control of the machining process can be difficult to maintain in production environments due to many factors, but especially uncertainty about selection of optimal machining parameters. The true underlying stability model for production machines on the shop floor is generally unknown, so parameters are typically selected by operators based on manufacturer recommendations or, in most cases, their accumulated shop floor experience. A class of methods referred to as physics-guided machine learning (PGML) offers a new approach for optimal parameter selection that incorporates theoretical knowledge about machining dynamics into the data-driven machine learning workflow. The focus of this paper is a version of PGML appropriate to shop floor operations in small and medium-sized machine shops (SMMs)—referred to as weak-PGML—that does not require a priori theoretical knowledge of machining dynamics to approximate the true underlying stability model for a particular machining scenario. Rather, the weak-PGML leverages the operator’s accumulated domain knowledge to uncover the true stability model and informs parameter selection. Weak-PGML reflects the limited resources and lack of technical expertise typical of most SMMs with legacy machines and a new generation of workforce without the accumulated experience of operators who have spent many years on the shop floor.

Optimization of Machining Parameters for Nimonic PE16 Using Machine Learning Models

Machining high-temperature alloys such as Nimonic PE16 demands precise control of machining parameters to achieve desired outcomes while minimizing tool wear and optimizing surface finish. In this study, we propose using machine learning regression models combined with synthetic data and response surface methodology strategies to optimize machining parameters for PE16. We aim to develop a predictive model that accurately estimates optimal cutting speeds and feed rates based on key output parameters, including cutting forces and surface roughness. Our methodology involves collecting experimental data from controlled machining tests conducted on PE16 samples under varying conditions. We used the datasets to train and validate regression models to establish correlations between input parameters and machining outcomes. The performance of each model is evaluated based on metrics such as mean absolute error and coefficient of determination. These metrics show relationships within the data and can determine a model’s success. The proposed machine learning framework offers a data-driven approach to optimize machining processes for PE16, facilitating enhanced efficiency, productivity, and quality in nuclear and other high-performance applications. Our findings contribute to understanding machining dynamics in challenging materials and provide valuable insights for intelligent machining systems.

Evaluation of entropy-coupled multi-criteria decision-making methods for enhancing machinability

Optimizing machining parameters is crucial, enhancing machinability while maintaining high product quality standards. This study bridges a critical research gap by evaluating and comparing five Taguchi-based Multi-Criteria Decision Making (MCDM) techniques—Combined Compromised Solution (CoCoSo), Grey Relational Analysis (GRA), Multi-Objective Optimization Ratio Analysis (MOORA), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and Complex Proportional Assessment (COPRAS)—coupled with the Entropy method to optimize machining parameters for enhancing machinability in turning medium carbon steel. The focus is on feed rate and cutting speed under dry and Minimum Quantity Lubrication (MQL) environments considering six critical machining responses: material removal rate, surface roughness, main cutting force, cutting temperature, cutting ratio, and tool life.The results reveal that MQL consistently improves machining performance, with COPRAS, TOPSIS, MOORA, and GRA converging on an optimal setting favoring an MQL environment, a 0.14 mm/rev feed rate, and a cutting speed of 137 m/min, whereas CoCoSo suggests a different optimal parameter setting. CoCoSo and GRA demonstrate the highest reliability, evidenced by minimal discrepancies between predicted and experimental results, with absolute percentage errors of 0.647 % and 0.659 %, respectively. The COPRAS method also shows strong predictive accuracy with a 5.573 % error, outperforming MOORA and TOPSIS. Spearman's rank correlation analysis reveals a high agreement between COPRAS, MOORA, and TOPSIS, with COPRAS emerging as a potential replacement for the latter in similar decision-making scenarios. SEM and EDX analyses confirm that MQL conditions reduce tool wear, enhance surface quality, and extend tool life compared to dry machining. This research provides insights into effective parameter optimization strategies for improving machinability and underscores the benefits of adopting MQL for sustainable manufacturing processes.

Optimizing Nd: YAG laser cutting of carbon fiber reinforcing polymer with newly developed resin using Taguchi-GRA approach and machine learning integration

PurposeThe investigation aims to improve Nd: YAG laser technology for precision cutting of carbon fiber reinforcing polymers (CFRPs), specifically those containing newly created resin (NDR) from the polyethylene and polyurea group, is the goal of the study. The focus is on showing how Nd: YAG lasers may be used to precisely cut CFRP with NDR materials, emphasizing how useful they are for creating intricate and long-lasting components.Design/methodology/approachThe study employs a systematic approach that includes complicated factorial designs, Taguchi L27 orthogonal array trials, Gray relational analysis (GRA) and machine learning predictions. The effects of laser cutting factors on CFRP with NDR geometry are investigated experimentally, with the goal of optimizing the cutting process for greater quality and efficiency. The approach employs data-driven decision-making with GRA, which improves cut quality and manufacturing efficiency while producing high-quality CFRP composites. Integration of machine learning models into the optimization process significantly boosts the precision and cost-effectiveness of laser cutting operations for CFRP materials.FindingsThe work uses Taguchi L27 orthogonal array trials for systematically explore the effects of specified parameters on CFRP cutting. The cutting process is then optimized using GRA, which identifies influential elements and determines the ideal parameter combination. In this paper, initially machining parameters are established at level L3P3C3A2, and the optimal machining parameters are determined to be at levels L3P2C3A3 and L3P2C1A2, based on predictions and experimental results. Furthermore, the study uses machine learning prediction models to continuously update and optimize kerf parameters, resulting in high-quality cuts at a lower cost. Overall, the study presents a holistic method to optimize CFRP cutting processes employing sophisticated techniques such as GRA and machine learning, resulting in better quality and efficiency in manufacturing operations.Originality/valueThe novel concept is in precisely measuring the kerf width and deviation in CFRP samples of NDR using sophisticated imaging techniques like SEM, which improves analysis and precision. The newly produced resin from the polyethylene and polyurea group with carbon fiber offers a more precise and comprehensive understanding of the material's behavior under different cutting settings, which makes it novel for kerf width and kerf deviation in their studies. To optimize laser cutting settings in real time while considering laser machining conditions, the study incorporates material insights into machine learning models.

INVESTIGATIONS ON THE MACHINABILITY CHARACTERISTICS OF Al–Zn(5.4)–Mg(2.4)–Cu(1.4) ALLOY COMPOSITE USING TOPSIS APPROACH

In this research, stir casting was used to create the aluminium alloy (AA7075) composite filled with 10[Formula: see text]wt.% zirconia (ZrO2) particles as reinforcement. Die sinking electric discharge machining (EDM) was used to examine the machining characteristics of the proposed composite. The machining experiments were executed in accordance with the [Formula: see text] orthogonal layout. Here, the material removal rate (MRR) and surface roughness (SR) were taken into account as the output responses, and the discharge current ([Formula: see text]), pulse on-time ([Formula: see text]), and pulse off-time ([Formula: see text]) were taken as the machining parameters. To determine the optimal parameter conditions for the output responses, the technique for order preference by similarity to ideal solution (TOPSIS) approach was applied. According to the experimental findings, the optimum settings of the parameters are determined at 15[Formula: see text]amps of ‘[Formula: see text]’, 750[Formula: see text][Formula: see text]s of ‘[Formula: see text]’ and 50[Formula: see text][Formula: see text]s of ‘[Formula: see text]’. ANOVA results stated that ‘[Formula: see text]’ has the most notable factor with a contribution of 90.85%. The interaction effect of parameters on the responses was shown by the contour plots. The confirmatory experiment was finally conducted at the optimum machining parameter settings, and it was found that the error only occurred in 8.4%.

Achieving High Precision and Productivity in Laser Machining of Ti6Al4V Alloy: A Comprehensive Study using a n-predictor polynomial regression model and PSO algorithm

Ti-6Al-4V, the Titanium alloy, has significant utilizations in aerospace, automotive, and marine sectors for its low density and high strength at elevated temperature. But its chemical activity and low thermal conductivity inhibits its machining by conventional method. Nd: YAG laser beam machining (LBM) finds extensive use in rapid and precise cutting of Ti6Al4V. This study has examined the influences of various LBM machining variables, including laser power, gas pressure and stand-off distance, in cutting 5mm thick Ti-6Al-4V plate. In assessing the effectiveness and performance of the LBM process, three response functions—surface roughness, angle of kerf, and material removal rate—have been designated. From the experimental data, different regression models have been established to estimate these response functions in terms of the machining parameters. Based on R2 score and RMSE, multi-dimensional polynomial regression is decided as the most suitable regression model. Subsequently, the Particle Swarm Optimization technique has been applied to identify the optimal machining parameters for reducing angle of kerf and surface roughness, while increasing material removal rate. Three individual single-objective functions underwent optimization, along with a multi-objective function. Furthermore, experimental verification was conducted for the optimal input parameters in the single-objective as well as the multi-objective optimization scenarios, resulting in an accuracy of 97% and 98%, respectively. Such a close agreement emphasizes the accuracy of the developed regression model as well as it signifies the reliability and efficacy of the optimization technique.

Optimisation of machining parameters in high-speed end milling of hardened AISI H13 die steel by integrating RSM and GA

ABSTRACT This study explores the potential of high-speed hard-end milling as a cost-effective alternative to the time-consuming grinding process for Chromium-molybdenum steel (AISI H13), widely used in cold and hot work tooling. The research aims to optimize machining parameters for achieving desired surface roughness, comparable to grinding and polishing. Cutting depth (0.4 - 0.8 mm), feed rate (1 - 4 μm per tooth), and cutting speed (80 - 140 m/min) were varied, and soybean oil replaced conventional lubricants. Employing Titanium Aluminum Nitride (TiAlN) coated end mills (3 mm diameter, 2 μm coating thickness) and response surface method (RSM) with Design Expert software, the study aimed at reducing tool wear and enhancing surface quality through multi-criteria optimization. Surface roughness analysis led to a streamlined surface by lowering mass flow rate and increasing cutting speed. The optimization approach predicted an optimal surface roughness value of 0.217475 μm at a cutting speed of 110 m/min, a feed rate of 1 μm/tooth, and a cut depth of 0.4 mm using genetic algorithm (GA), closely aligned with RSM’s ideal value of 0.215 μm. Both values converge around the 0.4 mm cut thickness.