Machining Parameters Research Articles

Experimental study on the performance in milling operations of Inconel® 718 with TiN/TiAlN coated tools

Abstract The Inconel® 718 alloy presents superior mechanical properties, which is why it is considered a difficult-to-machine material. To improve the milling process performance, multilayer TiN/TiAlN coated tools were deposited by PVD HiPIMS. Moreover, there is a gap in the literature regarding the milling of Inconel® 718 and the selection of ideal machining parameters. Based on this, the surface roughness (Ra), the evolution of flank wear, wear mechanisms, and the tool lifespan were analyzed. The parameters varied in the process were: cutting speed (Vc), feed per tooth (fz), and cutting length (Lcut). It was possible to verify the influence of the cutting length on the aspects evaluated and to observe that the cutting speed has a great impact, since the higher this parameter, the greater the roughness and wear obtained, and the shorter the tool life. The main wear mechanisms were abrasive, followed by material adhesion, with the onset of built-up edge development.

Parametric optimization of abrasive waterjet machining for aluminum 7075/boron carbide/zirconium silicate hybrid composites

In the present work, an attempt was made to study the machining characteristics of Hybrid Metal Matrix Composites (HMMCs) using Abrasive Waterjet Machining (AWJM). For preparing the composites, Aluminum alloy 7075 is cast with boron carbide (B4C) and zirconium silicate (ZrSiO4) reinforcement by the method of stir casting. Experimental investigation of the machining parameters on Depth of Cut (DoC), Material Removal Rate (MRR), and Surface Roughness (SR) was done. The independent parameters include Abrasive Mesh Size (AMS), Abrasive Flow Rate (AFR), Water Jet Pressure (WJP), and Traverse Rate (TR). The results of experiments reveal that low TRs increase SR, while short AMSs, high flow rates, and large WJPs increase DoC and MRR. Surface maps made by SEM exposed information about the surface texturing and the material removal processes. It was found that the optimal DoC was achieved at an AMS 80#, AMS of 340 g/min, WJP of 200 MPa, and TR of 60 mm/min for both unreinforced Al and the composite of Al + 5% B4C + 5% ZrSiO4. The results enable the development of proper AWJM procedures for HMMCs to ensure better surface finish and workability. From the experiments, it was observed that while smaller mesh sizes and greater AFR will improve MRR and DoC, it can at the same time have a negative effect on the overall performance of Kerf Taper angle (KT) and Surface Roughness (SR).

Review and Intercomparison of Machine Learning Applications for Short-term Flood Forecasting

Abstract Among natural hazards, floods pose the greatest threat to lives and livelihoods. To reduce flood impacts, short-term flood forecasting can contribute to early warnings that provide communities with time to react. This manuscript explores how machine learning (ML) can support short-term flood forecasting. Using two methods [strengths, weaknesses, opportunities, and threats (SWOT) and comparative performance analysis] for different forecast lead times (1–6, 6–12, 12–24, and 24–48 h), we evaluate the performance of machine learning models in 94 journal papers from 2001 to 2023. SWOT reveals that the best short-term flood forecasting was produced by hybrid, random forest (RF), long short-term memory (LSTM), artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS) approaches. The comparative performance analysis, meanwhile, favors convolutional neural network, ANFIS, multilayer perceptron, k-nearest neighbors algorithm (KNN), hybrid, LSTM, ANN, and support vector machine (SVM) at 1–6 h; hybrid, ANFIS, ANN, and LSTM at 6–12 h; SVM, hybrid, and RF at 12–24 h; and hybrid and RF at 24–48 h. In general, hybrid approaches consistently perform well across all lead times. Trends such as hybridization, model selection, input data selection, and decomposition seem to improve the accuracy of models. Furthermore, effective stand-alone ML models such as ANN, SVM, RF, genetic algorithm, KNN, and LSTM, provide better outcomes through hybridization with other ML models. By including different machine learning models and parameters such as environmental, socio-economical, and climatic parameters, the hybrid system can produce more accurate flood forecasting, making it more effective for early warning operational purposes.

Effects of Machining Parameters on Abrasive Flow Machining of Single Crystal γ-TiAl Alloy Based on Molecular Dynamics

Observing the intricate microstructure changes in abrasive flow machining with traditional experimental methods is difficult. Molecular dynamics simulations are used to look at the process of abrasive flow processing from a microscopic scale in this work. A molecular dynamics model for micro-cutting a single crystal γ-TiAl alloy with a rough surface in a fluid medium environment is constructed, which is more realistic. The evolution of material removal, cutting force, temperature, energy, and dislocation during micro-cutting are analyzed. The impact of cutting depth, abrasive particle sizes, and abrasive material on the micro-cutting process are analyzed. The analysis shows that the smaller cutting depth and abrasive particle sizes are beneficial to obtain a better machining surface, and the cubic boron nitride (CBN) abrasive is an effective substitute material for diamonds. The purpose of this study is to provide unique insights for improving the material removal rate and subsurface quality by adjusting machining parameters in actual abrasive flow precision machining.

Research on Helical Electrode Electrochemical Drilling Assisted by Anode Vibration for Jet Micro-Hole Arrays on Tube Walls

The electrochemical cutting technique, utilizing electrolyte flushing through micro-hole arrays in the radial direction of a tube electrode, offers the potential for cost-effective and high-surface-integrity machining of large-thickness, straight-surface structures of difficult-to-cut materials. However, fabricating the array of jet micro-holes on the tube electrode sidewall remains a significant challenge, limiting the broader application of this technology. To enhance the efficiency and quality of machining these jet micro-holes on the tube sidewall, a helical electrode electrochemical drilling method assisted by anode vibration has been proposed. The influence of parameters, such as the rotational direction and speed of the helical electrode, as well as the vibration amplitude and frequency of the workpiece, on the machining results was investigated using fluid field simulation and machining experiments. It was found that these auxiliary movements could facilitate the renewal of electrolytes within the machining gap, thereby enhancing the efficiency and quality of electrochemical drilling. Using the optimized machining parameters, an array of 10 jet micro-holes with a diameter of 200 μm was machined on the metal tube sidewall. Electrochemical cutting with radial electrolyte flushing tests were then performed through these micro-holes.

Optimization of Material Removal Rate in EDM Machining of OHNS Steel

The experimental investigation of material removal rate during machining of OHNS steel using EDM machine was study in this paper. The input parameters include peak current, pulse on time, Pulse off time were used for experimental work S/N ratio graphs have been used to optimize the machining parameters of EDM on OHNS steel using the Taguchi method and ANOVA methods. The study will carry out to investigate the effect of which parameter has the largest effect on the material removal rate of OHNS steel by machining. The optimum machining condition for material removal rate (MRR) has been find out with the ANOVA analysis and regression equation. This study is carried out by using the experimental investigation method and Taguchi analysis, at the end of this study we come to the conclusion of optimum conditions of the machining for OHNS material. Key Words: S/N, ANOVA, MRR, Taguchi, EDM, OHNS.

Benchtop Machining of Self-Standing Alumina Doughs for Low-Number Fabrication and Prototyping.

Cold isostatic pressing, gel casting, and protein coagulation are the most common techniques to produce green bodies prior to computer numerical control (CNC)-based machining for the near-net-scale shaping of ceramics. These methods typically involve various additives and entail several steps to create a green body that is capable of withstanding machining forces. Here, utilizing a single additive, we first introduced a facile benchtop method to generate self-standing, malleable doughs of alumina in under 2 min. We then optimized the parameters of CNC machining to obtain surfaces with minimum surface roughness and produced custom-sized crucibles as a showcase for low-number production. To consolidate a dough from highly loaded suspensions of alumina, we employed a poly(ethylene glycol)-grafted random copolymer of acrylic acid and N-[3-(dimethylamino)propyl]methacrylamide at 0.75 wt % with respect to the weight of alumina powder. We surveyed machining parameters with spindle speeds ranging from 5000 to 30000 rpm and cutting speeds from 1000 to 1800 mm/min using 1 and 2 mm tool sizes. The highest surface quality, characterized by the minimal surface roughness as evaluated by profilometry, was achieved at a spindle speed of 20000 rpm and a cutting speed of 1200 mm/min with a 1 mm tool and at a spindle speed of 15000 rpm and a cutting speed of 1800 mm/min with a 2 mm tool. Upon sintering, the hardness of the machined samples was measured to be 15.16 ± 1.15 GPa. Additionally, we demonstrated the recycling of alumina (up to 30 wt % of alumina content) sourced from intentionally broken parts in the green state. The recycling scheme contributes to the lowering of the use of resources and emphasizes the possibility of a greener future for ceramic production on a broader scale. Overall, this cost-effective and easy-to-implement methodology starts at the materials formulation level and parametrization of the machining paves the way for immediate industrial adaptation.

Influence of Machining Parameters on the Surface Roughness and Tool Wear During Slot Milling of a Polyurethane Block.

The aim of the work was to investigate the influence of the machining parameters on the surface roughness and tool wear during slot milling of a polyurethane block (PUB). In the experiment, the influence of the cutting speed, the feed per tooth and the depth of cut on the roughness Ra and Rz of the milling slot surface and wear of the end mill was analyzed. A three-axis CNC milling machine Emco Concept Mill 55 was used to perform the study. After the machining, the values of parameters Ra and Rz were measured using the Hommel Tester T500 induction profilometer. Three polyurethane materials of different densities were considered: the Labelite 45, the Prolab 65 and the LAB 1000. The wear of the end mill was also examined for each of the tested materials by a workshop microscope. In conclusion, it was indicated how and to what extent the variation in the machining parameters affects the surface geometrical structure of a polyurethane plate. Moreover, the research results for the tested materials were compared with each other.

Gaussian barebone mechanism and wormhole strategy enhanced moth flame optimization for global optimization and medical diagnostics.

Moth Flame Optimization (MFO) is a swarm intelligence algorithm inspired by the nocturnal flight mode of moths, and it has been widely used in various fields due to its simple structure and high optimization efficiency. Nonetheless, a notable limitation is its susceptibility to local optimality because of the absence of a well-balanced exploitation and exploration phase. Hence, this paper introduces a novel enhanced MFO algorithm (BWEMFO) designed to improve algorithmic performance. This improvement is achieved by incorporating a Gaussian barebone mechanism, a wormhole strategy, and an elimination strategy into the MFO. To assess the effectiveness of BWEMFO, a series of comparison experiments is conducted, comparing it against conventional metaheuristic algorithms, advanced metaheuristic algorithms, and various MFO variants. The experimental results reveal a significant enhancement in both the convergence speed and the capability to escape local optima with the implementation of BWEMFO. The scalability of the algorithm is confirmed through benchmark functions. Employing BWEMFO, we optimize the kernel parameters of the kernel-limit learning machine, thereby crafting the BWEMFO-KELM methodology for medical diagnosis and prediction. Subsequently, BWEMFO-KELM undergoes diagnostic and predictive experimentation on three distinct medical datasets: the breast cancer dataset, colorectal cancer datasets, and mammographic dataset. Through comparative analysis against five alternative machine learning methodologies across four evaluation metrics, our experimental findings evince the superior diagnostic accuracy and reliability of the proposed BWEMFO-KELM model.

Development of MHz X-ray phase contrast imaging at the European XFEL.

We report on recent developments that enable megahertz hard X-ray phase contrast imaging (MHz XPCI) experiments at the Single Particles, Clusters, and Biomolecules and Serial Femtosecond Crystallography (SPB/SFX) instrument of the European XFEL facility (EuXFEL). We describe the technical implementation of the key components, including an MHz fast camera and a modular indirect X-ray microscope system based on fast scintillators coupled through a high-resolution optical microscope, which enable full-field X-ray microscopy with phase contrast of fast and irreversible phenomena. The image quality for MHz XPCI data showed significant improvement compared with a pilot demonstration of the technique using parallel beam illumination, which also allows access to up to 24 keV photon energies at the SPB/SFX instrument of the EuXFEL. With these developments, MHz XPCI was implemented as a new method offered for a broad user community (academic and industrial) and is accessible via standard user proposals. Furthermore, intra-train pulse diagnostics with a high few-micrometre spatial resolution and recording up to 128 images of consecutive pulses in a train at up to 1.1 MHz repetition rate is available upstream of the instrument. Together with the diagnostic camera upstream of the instrument and the MHz XPCI setup at the SPB/SFX instrument, simultaneous two-plane measurements for future beam studies and feedback for machine parameter tuning are now possible.

Optimization of cutting temperature and surface roughness in CNC turning of Ti-6Al-4V alloy using response surface methodology.

This study investigates the optimization of cutting conditions for machining titanium alloy (Ti-6Al-4V) using Response Surface Methodology (RSM), with the goal of minimizing tool-chip interface temperature and surface roughness. The research focuses on key cutting parameters to investigate the most effective combinations for enhancing surface finish and reducing thermal impact during machining. The present study deals with the dry turning of Ti-6Al-4V alloy with carbide alloy inserts in a way to utilize the Analysis of Variance (ANOVA) to develop predictive models for minimum surface roughness and optimum temperature. The findings reveal that both cutting speed and depth of cut are critical in determining machining outcomes. Specifically, a low cutting speed of 88m/min coupled with a high depth of cut of 0.20mm was found to elevate the cutting temperature to approximately 835°C, resulting a surface roughness of 0.59μm. Conversely, increasing the cutting speed to 120m/min while reducing the depth of cut to 0.10mm significantly lowered the temperature to around 607°C, resulting a surface roughness of 0.19μm; thus, thereby improving surface finish and reducing thermal stress on the tool. Additionally, a 27% reduction in cutting temperature and a minimum surface roughness of 0.19μm were achieved with optimal settings of 120m/min cutting speed, 0.08 mm/rev feed rate, and 0.10mm depth of cut. The study demonstrates the effectiveness of RSM in optimizing machining parameters for optimum temperature and better surface finish in the titanium alloy machining.

Optimization of milling conditions for AISI 4140 steel using an integrated machine learning-multi objective optimization-multi criteria decision making framework

This work targeted the development of a framework for optimizing cutting conditions by integrating Machine Learning – Multi-Objective Optimization – Multi-Criteria Decision-Making (ML-MOO-MCDM) to find out the specific cutting condition in milling AISI4140 steel. Four ML models (Extreme Gradient Boosting (XGB), CatBoost (CAT), Gradient Boosting (GBR), and Light Gradient Boosting (LGB) were evaluated for predicting performance objectives, including surface roughness (Ra), tool wear (VB), cutting force (Fc), and material removal rate (MRR). Root Mean Square Error (RMSE) and coefficient of determination (R2) metrics were employed to identify the best-performance ML models. The SHapley Additive exPlanations (SHAP) technique was used to understand and interpret machining parameters on model predictions. The NSGA-III optimization algorithm was generated, followed by Pareto-optimal solutions, offering achievable values for all objectives (Ra, VB, Fc, and MRR) under various cutting conditions. Different Multi-Criteria Decision-Making Methods (MCDM) were then evaluated to deduce the best performance. With a given milling performance objectives: Ra of 0.816 µm, VB of 45.5 µm, Fc of 286.59 N and MRR of 3.889 cm3/min, the result from the ML-MOO-MCDM framework indicated that the optimal cutting conditions were: cutting speed (Vc): 428.46 rpm, feed rate per tooth (fz): 0.285 mm/tooth, depth of cut (ap): 0.208 mm, nose radius (rt): 0.4 mm, cutter coating: PVD-coated Nano-TiAlN. Experimental validation was finally conducted to confirm the effectiveness of the proposed ML-MOO-MCDM framework in optimizing milling processes. This approach allows for balancing multiple performance objectives and achieving the most appropriate cutting conditions according to the weight of objectivity. The proposed research methodology can lead to improved machining efficiency and product quality.

Investigation of the Effect of Cutting-Edge Rounding on Surface Quality in Milling of Al 7075 and Al 6082 Alloys

Al 7075 and Al 6082 alloys are alloys that are widely used in many sectors, especially aviation and automotive. During their processing along with the widespread use of those alloys, significant machinability problems such as low surface quality due to chip accumulation (Built Up Edge-BUE) on the cutting edge are observed. In this study, the effect of cutting-edge corner rounding and machining parameters on surface roughness in milling of Al 7075 and Al 6082 alloys were experimentally investigated. To determine the effect of cutting-edge rounding, two different corner rounding values (5.5-6 µm and 7-9 µm) were applied and compared with standard/coated and standard/uncoated tools in terms of machining performance. In the experimental studies, cutting parameters were determined as two different cutting speeds (300 m/min, 400 m/min) and two different feed rates (0.085 mm/tooth, 0.11 mm/tooth). With each tool, peripheral milling was performed on four sides of the prismatic test specimens under dry machining conditions. Taguchi L8 orthogonal array experimental design was used as the experimental design. Analysis of variance (ANOVA) was performed to evaluate the effects of cutting parameters on surface roughness and the reliability of all results was above 85%. The results of the analysis of variance revealed that the alloy type was more effective on the surface roughness. In the surface roughness measurements, the best surface roughness values (Ra=0.14 µm) were obtained in the experiments performed with a standard/coated cutting tool using a cutting speed of 300 m/min and a feed rate of 0.11 mm/tooth.

ASSESSMENT OF TOOL NOSE WEAR USING SCANNED IMAGES OF CUTTING INSERTS

The surface quality of final product in machining is governed by many intimately related factors such as tool conditions and machining parameters. Amongst these factors, tool wear is essentially one of the most prominent influences on dimensional accuracy, surface roughness and tool life. Since the measurement of tool wear in manufacturing is still done manually, automated and intelligent measurement of wear are gaining more interest in the perspective of reducing human interference and hence, the accurate assessment of tool condition. This research work proposes a fast and reliable image processing method in measuring the nose wear of cutting inserts. Two image digitization methods were used, which are flatbed scanner (CanoScan5600F) and 3-D metrology system (Alicona InfiniteFocus). A sub-pixel edge detection algorithm was developed in the segmentation of nose area to improve the measurement accuracy. Scanning of tool nose was conducted before and after the machining for the measurement of wear area. Results show that about 5% to 6% of average absolute deviations were obtained from the measurement of nose wear area (Ap)and nose flank wear (VBc(max)) using images from InfiniteFocus and scanner.

METHODS OF ANALYSIS OF DYNAMIC PROCESSES IN DISCRETE-CONTINUUM SYSTEMS UNDER PULSE EXCITATIONS

This paper addresses the enhancement of methods for calculating vibration-impact machines, particularly concerning adjustments to prevent potential shock resonances. This design criterion for substantiating the parameters of vibration-impact machines had not been previously considered. However, the likelihood of shock resonance increases significantly with the mass of the process load, operating frequencies of vibration excitation, and overall dimensions of the thin-walled bodies of vibrating machines. Hence, prioritizing the criterion of tuning out resonant modes through the appropriate choice of housing parameters becomes crucial for modern, heavily loaded machines. This novel approach is proposed in this paper for calculating vibration-impact machines and armored vehicles. To analyze the conditions for the onset of resonant modes under periodic shock loading, single-mass, multi-mass, and continuum dynamic systems were sequentially considered. It was shown that, in the absence of friction, shock resonance occurs when any natural frequency of the machine body oscillations matches a multiple of the excitation frequency from the drive. Additionally, numerical integration of the system of differential equations of motion was used to confirm shock resonance conditions. The numerical results were consistent with the analytical ones. It was also determined that tuning away from the resonant frequency by 5% to 10% significantly alters the steady-state mode of the vibration machine, notably reducing oscillation amplitudes. Therefore, to substantiate the parameters of vibrating machines, it is essential to maximize the adjustment of the natural frequency spectrum of their bodies, considered as elastically deformable structures, away from frequencies that are multiples of the drive frequency. To implement this algorithm, we propose adopting a generalized parametric description. Variable parameters include the thickness of housing elements, their cross-sections, reinforcement schemes, and the number of stiffeners. It was found that varying these parameters diversely affects the migration of natural frequencies in the spectrum. Specific parameters with the greatest impact on detuning from shock resonance frequencies were identified. This set of parameters is used to determine the optimal set that satisfies the criterion of maximum detuning from shock resonance. Similar developments and studies have been conducted for the armor hulls of lightly armored vehicles.

Validation of a rapid algorithm for repeated intensity modulated radiation therapy dose calculations

As adaptive radiotherapy workflows and deep learning model training rise in popularity, the need for repeated applications of a rapid dose calculation algorithm increases. In this work we evaluate the feasibility of a simple algorithm that can calculate dose directly from MLC positions in near real-time. Given the necessary machine parameters, the intensity modulated radiation therapy (IMRT) doses are calculated and can be used in optimization, deep learning model training, or other cases where fast repeated segment dose calculations are needed. The algorithm uses normalized beamlets to modify a pre-calculated patient specific open field into any MLC segment shape. This algorithm was validated on 91 prostate IMRT plans as well as 20 lung IMRT plans generated for the Elekta Unity MR-Linac. IMRT plans calculated using the proposed method were found to match reference Monte Carlo calculated dose within98.02±0.84%and96.57±2.41%for prostate and lung patients respectively with a 3%/2 mm gamma criterion. After the patient-specific open field calculation, the algorithm can calculate the dose of a 9-field IMRT plan in 1.016±0.284 s for a single patient or 0.264 ms per patient for a parallelized batch of 24 patients relevant for deep learning training. The presented algorithm demonstrates an alternative rapid IMRT dose calculator that does not rely on training a deep learning model while still being competitive in terms of speed and accuracy making it a compelling choice in cases where repetitive dose calculation is desired.

Statistical Analysis of Cutting Force and Vibration in Turning X5CrNi18-10 Steel

X5CrNi18-10 is a corrosion-resistant steel that has become popular in the automotive, marine, food, nuclear, and other industries. Chromium alloyed in the X5CrNi18-10 increases the material’s toughness, which influences the cutting phenomena such as the cutting force and vibration. It is necessary to investigate the effect of the machining parameters on the X5CrNi18-10 turning, particularly the feed, which has significant effects on the cutting phenomena. The objective of this research is to investigate the correlation between the feed and cutting phenomena to improve the product quality, reduce machining disruptions, and optimize the parameters for a low cutting speed and vibration. Statistical analysis has shown promise in identifying the impact of variables using correlation analysis and estimated marginal means plots. This study highlights the findings of the Pearson’s correlation analysis between the feed, active cutting force, and active vibration as well as the estimated marginal means plots between the machining parameters and cutting phenomena. The results indicate that there is a strong correlation between the feed and active cutting force with a coefficient of correlation of 0.688, as well as the feed and active vibration with a coefficient of correlation of 0.697. The estimated marginal means plots indicate that as the cutting speed increases, the value of the active vibration and the active force decreases.

Experimental investigation, modeling and optimization of wire EDM process parameters for machining AA2024-B4C self-lubricating composite

Abstract Metal matrix composites (MMCs) are increasingly used across various manufacturing sectors, including automotive, defense, and aerospace, due to their exceptional strength-to-weight ratio, lightweight properties, high strength, and appreciable hardness when combined with suitable reinforcing materials. MMCs reinforced with carbide particles not only enhance the mechanical properties, but also exhibit self-lubricating characteristics, providing exceptional wear resistance. The self-lubricating properties of MMCs contribute significantly to minimizing the maintenance requirements, reducing operational costs, and advancing sustainability goals, rendering them indispensable for sectors such as aerospace, automotive, medical equipment, and energy. The present work addresses the challenges associated with machining advanced composite materials and proposes optimal machining parameters to overcome these difficulties. Here in the current investigation, aluminium alloy (AA2024) + 10 wt% B4C composite was selected as the workpiece material, and it was machined using a wire electric discharge machine. Response surface methodology was employed to develop predictive models for the output responses, namely surface roughness (R a ) and material removal rate (MRR). The accuracy of the predictive models was found to be 98.78% for R a and 93.54% for MRR, demonstrating their reliability. To optimize the machining performance, both single-objective and multi-objective optimization approaches were used. Taguchi’s signal-to-noise (S/N) ratio analysis was applied for single-objective optimization, while Pareto optimal fronts generated using the genetic algorithm facilitated the multi-objective optimization to maximize MRR and minimize R a effectively.

Optimizing conventional machining process parameters for A713 aluminum alloy using Taguchi method and passive optical network for data transmission

Abstract This study focuses on optimizing machining parameters for A713 aluminum alloy, renowned for its high strength-to-weight ratio but challenging to machine with precision. Using the Taguchi method, key parameters – cutting speed, feed rate, and depth of cut – are optimized to improve surface finish, tool life, and dimensional accuracy. An L9 orthogonal array is applied to enhance experimental efficiency. Furthermore, the integration of Passive Optical Network (PON) technology facilitates real-time data monitoring and transmission, enabling continuous feedback and timely adjustments. This hybrid approach of Taguchi optimization and PON technology achieves superior machining performance, boosting surface quality and operational efficiency. The research highlights how modern data transmission solutions like PON can enhance traditional optimization methods, providing a scalable framework for advanced machining processes in industrial applications.

EXPERIMENTAL OPTIMIZATION OF SURFACE ROUGHNESS IN MILLING OF AISI 304 STAINLESS STEEL ON A CNC VERTICAL MACHINING CENTER

One of the most critical factors affecting the service life of machine elements is the condition of their surfaces. In this study, the impact of machining parameters on the surface roughness during the milling of AISI 304 stainless steel was investigated. Experimental work was carried out using the Taguchi L18 design of experiments, and changes in surface roughness were analyzed. The optimal machining conditions were determined based on various experimental results. The findings of the study indicate that the surface roughness values in experiments conducted under dry machining conditions were lower compared to those where coolant was used.