Depth Of Cut Research Articles

Effect of machining parameters on the machinability of 15‐5 precipitation hardened stainless steel during dry turning

AbstractThis research presents the studies on the effect of machining variables and the empirical modeling of machinability output responses during dry turning of 15–5 precipitation‐hardened stainless steel (PHSS) using the taguchi meta‐heuristic algorithm. L9 orthogonal array (OA) robust experimental design was selected for conducting the dry turning operations. The input variables included cutting velocity, depth of cut, and feed rate, while the output responses measured were surface roughness (Ra) and cutting force (Fc). The influence of these process variables was determined through analysis of variance (ANOVA). ANOVA results revealed that cutting speed, feed rate and depth of cut were impacting the average surface roughness by 36 %, 29 %, and 31 %, respectively and influencing the cutting force by 2 %, 16 %, and 72 %, respectively, during turning operation. Empirical models for predicting cutting force and surface roughness were developed using the Taguchi meta‐heuristic algorithm. The optimization process resulted in a significant reduction in surface roughness, with Ra decreasing by 17 % and a notable decrease in cutting force, Fc, by 8 %. These numerical improvements indicate that the proposed optimization approach substantially enhances machining performance, validating its effectiveness.

Influence of tool micro-texturing, cooling conditions and cutting conditions on workpiece surface integrity and tool condition

Improving cutting tool life is very crucial in manufacturing industry. The quality of the finished product is equally important. Investigations are being done to identify different methods to minimize tool wear and improve surface integrity. Micro-texturing of tools is one of the possible methods to improve life of the cutting tool. This work investigates the influence of type of tool: micro-textured, untextured tool; cooling conditions: dry, flood machining; process parameters like cutting speed: low and high, feed: low and high and depth of cut: low and high on surface integrity of machined low alloy steel. Micro-textured tools showed better performance over untextured tools by decreasing the tool crater wear area, roundness error and surface roughness of the finished part. Type of tool, cutting condition, feed and depth of cut influenced roundness error the most. Cutting condition influenced crater wear and feed influenced surface roughness the most.

Significance of Tool Coating Properties and Compacted Graphite Iron Microstructure for Tool Selection in Extreme Machining.

This study aims to determine the extent to which coating composition and workpiece properties impact machinability and tool selection when turning Compacted Graphite Iron (CGI) under extreme roughing conditions. Two CGI workpieces, differing in pearlite content and graphite nodularity, were machined at a cutting speed of 180 m/min, feed rate of 0.18 mm/rev, and depth of cut of 3 mm. To assess the impact of tool properties across a wide range of commercially available tools, four diverse multilayered cemented carbide tools were evaluated: Tool A and Tool B with a thin AlTiSiN PVD coating, Tool C with a thick Al2O3-TiCN CVD coating, and Tool D with a thin Al2O3-TiC PVD coating. The machinability of CGI and wear mechanisms were analyzed using pre-cutting characterization, in-process optical microscopy, and post-test SEM analysis. The results revealed that CGI microstructural variations only affected tool life for Tool A, with a 110% increase in tool life between machining CGI Grade B and Grade A, but that the effects were negligible for all other tools. Tool C had a 250% and 70% longer tool life compared to the next best performance (Tool A) for CGI Grade A and CGI Grade B, respectively. With its thick CVD-coating, Tool C consistently outperformed the others due to its superior protection of the flank face and cutting edge under high-stress conditions. The cutting-induced stresses played a more significant role in the tool wear process than minor differences in workpiece microstructure or tool properties, and a thick CVD coating was most effective in addressing the tool wear effects for the extreme roughing conditions. However, differences in tool life for Tool A showed that tool behavior cannot be predicted based on a single system parameter, even for extreme conditions. Instead, tool properties, workpiece properties, cutting conditions, and their interactions should be considered collectively to evaluate the extent that an individual parameter impacts machinability. This research demonstrates that a comprehensive approach such as this can allow for more effective tool selection and thus lead to significant cost savings and more efficient manufacturing operations.

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).

Analysis of HSS Chisel Main Cutting Edge Angle and Feeding Depth of ST 60 Steel Cylindricity in Longitudinal Turning Process

This study aims to analyze the main cutting edge angle of HSS (high speed steel) chisel and the depth of cut on the cylindricity of ST 60 steel workpiece in the longitudinal turning process. The material used is ST 60 steel with a specimen length of 200 mm and a diameter of 30 mm and turned along 200 mm using variations of the main cutting edge angle, Kr (30, 45, 60, 90o) and the depth of cut, a (0.5; 1; 1.5; 2.0 mm) and other variables are set at constant conditions. Based on the results of this study, it can be concluded that the variation of the SS chisel cutting angle in the turning process produces an average total cylindricity value at a cutting angle of Kr 30o = 0.95 mm, Kr: 45o = 0.65 mm, Kr: 60o = 0.45 mm in other words, it can be concluded that the optimal cylindricity value occurs at a cutting angle of 90o = 0.15 mm. While the average cylindricity value at a cutting depth of a: 0.5 mm = 0.25 mm, a: 1 mm = 0.35 mm, a: 1.5 mm = 0.55 mm, a: 2.0 = 0.60 in other words that the optimal total cylindricity value is obtained at a cutting depth of 0.5 mm.

Effect of Depth of Cut and Number of Layers on the Surface Roughness and Surface Homogeneity After Milling of Al/CFRP Stacks.

A multilayer structure is a type of construction consisting of outer layers and a core, which is mainly characterized by high strength and specific stiffness, as well as the ability to dampen vibration and sound. This structure combines the high strength of traditional materials (mainly metals) and composites. Currently, sandwich structures in any configurations (types of core) are one of the main directions of technology development and research. This paper evaluates the surface quality of II- and III-layer sandwich structures that are a combination of aluminum alloy and CFRP (Carbon Fiber-Reinforced Polymer) after the machining. The effect of depth of cut (ae) on the surface roughness of the II- and III-layer sandwich structures after the milling process was investigated. The surface homogeneity was also investigated. It was expressed by the IRa and IRz surface homogeneity indices formed from the Ra and Rz surface roughness parameters measured separately for each layer of the materials forming the sandwich structure. It was noted that the lowest surface roughness (Ra = 0.03 µm and Rz = 0.20 µm) was obtained after the milling of the II-layer sandwich structure using ae = 0.5 mm, while the highest was obtained for the III-layer structure and ae = 1.0 mm (Ra = 1.73 µm) and ae = 0.5 mm (Rz = 10.98 µm). The most homogeneous surfaces were observed after machining of the II-layer structure and using the depth of cut ae = 2.0 mm (IRa = 0.28 and IRz = 0.06), while the least homogeneous surfaces were obtained after milling of the III-layer structure and the depths of cut ae = 0.5 mm (IRa = 0.64) and ae = 2.0 mm (IRz = 0.78). The obtained results may be relevant to surface engineering and combining hybrid sandwich structures with other materials.

Optimization of Mechanical Machining for Heat-Resistance Ta-W Alloy by Using Orthogonal Cutting Method

Tantalum and tantalum alloys, which have superior thermal properties, including a high melting temperature, low specific heat, low thermal conductivity, and high density, are promising candidates for highperformance parts exposed to high temperatures and hazardous environments in aerospace, defense, and other industries. However, those alloys are hard to cut and mechanically machine, and the poor machinability of tantalum alloys has precluded their wide use. This study investigates mechanisms for cutting Ta-10%W alloys using orthogonal cutting methods for precision part manufacturing. During orthogonal cutting processes, the cutting forces are increased dramatically because of the surface folding phenomenon of Ta-W alloys. As a result of experiments and simulation calculations, the tool’s rake angle and depth of cut are critical parameters for controlling the cutting shear angle, which makes a significant difference in chip thicknesses and surface folding phenomenon. Based on the results, about 12 degrees of cutting shear angle is suitable for the mechanical machining of a Ta-W alloy. Additionally, cutting speed may be one of the critical parameters to consider for changing the mechanical responses of Ta-W alloys. With this perspective, the mechanical properties of Ta-W alloys at high strain rates seem to be an important factor in orthogonal cutting. Based on these results, the cutting mechanisms and optimal cutting parameters for Ta-W alloys are discussed.

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.

Ultrasonic-assisted ultra-precision turning of zinc-selenide with straight-nosed diamond tools

This study proposes a novel ultra-precision machining technology that uses ultrasonic vibration and a straight-nosed diamond tool to improve the processing of the brittle optical material zinc selenide (ZnSe). This research addresses the challenges posed by Poisson's effect in ultrasonic vibration-assisted single-point diamond turning, which causes bending vibration along the depth of cut, resulting in lower machining efficiency and surface quality. This study analyses the relationship between one-dimensional ultrasonic vibrations at the diamond tool edge and induced bending vibrations using both theoretical and experimental methods. By investigating ultrasonic vibration dynamics in the feed direction and at the straight cutting edge, the results showed that ultrasonic vibration helps to improve the ductile-brittle transition ratio of the cutting area and surface quality. These improvements are accomplished by regulating the cutting position at the tool cutting edge, adjusting cutting parameters, and optimizing ultrasonic parameters. The machined surface roughness of ZnSe is reduced by approximately 30-46% at higher feed rates under ultrasonic vibration with straight-nosed diamond tools. The findings demonstrate the potential of this novel technology to reduce tool wear and brittle fractures, resolving the challenge of ultra-precision machining for optical materials.

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.

Machine learning assisted prediction with data driven robust optimization: Machining process modeling of hard part turning of DC53 for tooling applications supporting semiconductor manufacturing

This research investigates the hard part turning of DC53 tool steel, which is engineered for better mechanical properties compared to AISI D2 tool steel, using Xcel cubic boron nitride. The machining input parameters such as workpiece hardness (different heat treatments), cutting speed, feed rate, and depth of cut are used to thoroughly evaluate process science across conflicting machinability attributes such as cutting tool life, machined workpiece surface roughness, volume of material removed, machine tool power consumption, and tool-workpiece zone temperature. A full factorial design of experiments with two levels, resulting in 16 experiments, is performed with statistical parametric significance analysis to better control process variability. Multiple artificial neural network (ANN) architectures are generated to accurately model the non-linearity of the process for better prediction of key characteristics. The optimized architectures are used as prediction models to a non-sorting genetic algorithm (NSGA-II) to determine the optimal compromise among all conflicting responses. The significance analysis highlighted that heat treatment is the most influential variable on machinability, with a significance of 74.63% on tool life, 59.03% on roughness, 66.45% on material removed, 38.03% on power consumption, and 29.60% on interaction-zone temperature. The confidence of all ANN architectures is achieved above 0.97 R2 to accurately incorporate parametric relations with physical mechanisms. The compromise against conflicting machinability attributes identified by NSGA-II optimization results in a 92.05% increase in tool life, a 91.83% increase in volume removed, a 33.33% decrease in roughness, a 26.73% decline in power consumption, and a 9.61% reduction in machining temperature. The process variability is thoroughly analyzed using statistical and physical analyses and computational intelligence, which will guide machinists in better decision-making.

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.

Investigation of Effects of Feed Rate and Cutting-Edge Angle Variation on Surface Roughness in External Cylindrical Turning Process of Ms58 Brass Material

The Ms58 brass material was machined dry by the lathe’s external cylindrical surface turning method. Three different feeds (0.155, 0.088, and 0.028 mm/rev), 3 different cutting-edge angles (70°, 80°, and 90°), and three-spindle speeds (480, 630, and 1250 rpm) were used in machining experiments. The depth of the cut value was taken as a 0.5 mm constant. The effects of these different parameters on surface roughness values were investigated. For each surface roughness value measured from parts machined with other parameters, measurements were made five times, the largest and smallest values were discarded, and the average of the remaining 3 values was taken. This rigorous process of data collection ensured the reliability of the results. Graphs were prepared and examined with working parameters and surface roughness values. As a result, it was determined that the surface roughness value decreased with the decrease in feed and the increase in another parameter, the “cutting-edge angle” of the tool. When external cylindrical surface turning was performed with the experimental parameters of 480 rpm, a large cutting-edge angle of 90°, and a depth of cut of 0.5 mm within this study plan, the lowest average surface roughness was obtained as 1.228 µm.

Enhancement of Machining Performance of Ti-6Al-4V Alloy Though Nanoparticle-Based Minimum Quantity Lubrication: Insights into Surface Roughness, Material Removal Rate, Temperature, and Tool Wear

In competitive industry, economical and environmentally friendly production techniques are essential. In this sense, cleaner and more sustainable machining techniques are the industry’s focus. In addition to green methods, effective parametric control is necessary for hard-to-cut materials, particularly titanium Ti-6Al-4V, which is extensively used in a diversity of industries, including aerospace, medical, and military applications. Therefore, the current study aims to improve the machining performance of Ti-6Al-4V alloy using sustainable lubrication conditions. The effect of Al2O3 nanoparticles based on the minimum quantity lubrication (N-MQL) condition on surface quality and productivity are compared with minimum quantity lubrication (MQL). The performance measures, including surface roughness (Ra), material removal rate (MRR), and temperature, are evaluated at three machining variables, i.e., cutting speed (Vc), feed rate (f), and depth of cut (ap). These performance measures are further assessed by tool wear and surface morphology analysis. ap, f, and Vc are the most influencing parameters for Ra, MRR, and temperature, regardless of lubrication mode. The optimized values of RA of 0.728443 µm, MRR of 2443.77 m3/min, and temperature of 337 °C are achieved at N-MQL. For the N-MQL state, the optimized values of Ra of 0.55 µm, MRR of 2579.5 m3/min, and temperature of 323.554 °C are attained through a multi-response optimization desirability approach. Surface morphology analysis reveals a smooth machined surface with no obvious surface flaws, such as feed marks and adhesion, under N-MQL conditions, which significantly enhances the surface finish of the parts. The machining performance under the N-MQL condition has been enhanced considerably in terms of an improvements in surface finish of 32.96% and MRR of 11.56%, along with a decrease in temperature (17.22%) and higher tool life (326 s) than MQL. Furthermore, Al2O3 is advised over MQL because it uses less energy and has reduced tool wear and improved surface quality, and it is a cost-effective and sustainable fluid.

COMPREHENSIVE EVALUATION OF DIMENSIONAL DEVIATION, FLANK WEAR, SURFACE ROUGHNESS AND MATERIAL REMOVAL RATE IN DRY TURNING OF C45 STEEL

The study investigated the turning of C45 steel in a dry environment. The input parameters that were varied were cutting speed, feed, depth of cut, corner radius and insert type. The experimental investigations were carried out according to a custom experimental design using the D-optimality criterion. The measured output parameters were dimensional deviation, flank wear and surface roughness, while the material removal rate was calculated. A detailed analysis and evaluation of the effects of the input parameters on the output parameters was carried out. The model was diagnosed and appropriate regression equations was established. Based on the obtained regression equations, multi-objective optimisation was performed using particle swarm optimisation. The objective function was to simultaneously minimise dimensional deviation, flank wear and surface roughness and maximise material removal rate. The optimisation was carried out for different weighting coefficients of each output function for different production requirements. The obtained models and optimal values were verified by additional confirmation experiments.

Investigation on the effect of probe stiffness on surface roughness in single crystal copper nanomachining process

Surface roughness is one of the most important features in nanoscale machining. In this paper, molecular dynamics simulations have been performed to investigate the combination effect of the tool stiffness, machining parameters and the tool tip geometry on the surface roughness of the single-crystal copper workpiece in the nano machining process by diamond tool. To consider the effects of probe beam dynamics in calculating the workpiece surface roughness, the probe cantilever is modeled as an equivalent spring-mass system that the tip mass is added to its end. The differential equation of the probe motion is solved simultaneously with the molecular dynamics equations for the tool tip and the workpiece and the effect of probe stiffness on the surface roughness is obtained. The result shows that a higher cutting speed and depth of cut and lower tool radius lead to a larger effect of probe stiffness on the surface roughness of the workpiece. Also, for the negative rake angles, considering the tool as rigid creates a large error in determining the surface roughness.

An Exploratory Study of the Surface Integrity of Various FRP Composites in Face Milling Operation

The use of glass, carbon and hybrid /glass/carbon epoxy polymer composites is widely used in the automotive, manufacturing and aerospace sectors. Milling is a secondary production process in which the final shape of the product is produced with the desired shape and high dimensional accuracy. The objective of this study is to enhance our knowledge of the machinability properties of composite materials in the secondary manufacturing industry. In this research study, performing face milling operations with different tools on bidirectional (BD) glass, unidirectional (UD) glass, carbon/epoxy, and glass/carbon/epoxy (hybrid epoxy) polymer composites. This experimental work derives conclusions that contribute to the improvement of the milling process for better surface quality. Taguchi’s L16 Design of Experiments (DOE) and Analysis of Variance (ANOVA) are used to determine the effect of tool type, speed of spindle, rate of feed, cut depth on surface roughness and delamination factor. And it was concluded that glass/carbon hybrid epoxy polymer laminates showed improved surface quality when milled with a Poly Crystalline Diamond (PCD) tool at parametric combinations of rate of feed is 300 mm/min, speed of spindle is 1000 rpm, and cutting depth is 0.5 mm. In addition, the experimental results of the scanning electron microscope examination were also verified. And found excellent machined surface quality and least damage with the above optimal process parameter combinations.

Numerical modelling and simulation studies of laser beam heating parameters on depth of cut and surface temperature in laser-assisted machining of Ti6Al4V

Laser-assisted machining (LAM) is a hybrid machining process which uses a laser heat source to heat the narrow zone of a work part material ahead of the cutting tool. Surface temperature and heat-affected depth (effective depth of cut) achieved during localised laser heating play an important role in the machinability of the workpiece. In the current work, moving laser heat source simulation studies are performed using COMSOL Multiphysics software. The effect of laser power, beam diameter, scan speed, convective heat transfer coefficient and absorptivity are studied by simulating different test cases. Results show that the average maximum surface temperature increases when the laser power is increased from 100 W to 160 W by 55%. It increases with the increase in absorptivity from 0.3 to 0.39 by 18%. The average maximum surface temperature decreases when the laser diameter is increased from 1 to 2.5 mm by 46%. It decreases with the increase in scan speed from 50 to 200 mm/min by 5%. In the case of effective depth of cut, similar trends were observed with the variation of the above parameters. Further, the variation of convective heat transfer coefficient from 5 to 20 W/m-K does not affect the average maximum surface temperature and effective depth of cut. It was found that the laser power has a significant effect on average maximum surface temperature and effective depth of cut compared to other parameters.

Experimental Investigation on the Machining Behaviour, Surface Integrity and Tool Wear Analysis in Environment Friendly Milling of Inconel 825

The study investigated end milling of Inconel 825 with varying spindle speed (N), feed rate (f) and axial depth of cut (da) with Minimum Quality Lubricant (MQL) and flooded lubrication. Molybdenum disulfide (MoS2) with the average particle size of 10 µm was used as lubricating agent. Work considered, center line average of roughness profile as a measure of surface roughness which was measured with a surface roughness tester. Material Removal Rate (MRR) was also measured experimentally using weight difference. The influence of spindle speed (N), feed rate (f) and axial depth of cut (da) during end milling of Inconel 825 on surface roughness and MRR were studied. Prediction of surface roughness by ANOVA linear model for MQL condition was found functionally adequate with R2 = 89.25% which fits with the experimental values. Also, the prediction and optimization of surface roughness using Response Surface Methodology (RSM) was proposed. It was found that, RSM model for MQL condition produced good agreement with the measurement of the given range of input cutting conditions with the prediction capability of 91.66%. Further, the machined surfaces and tool wear were analyzed using Scanning Electron Microscope (SEM) to understand the mechanisms of wear.

PENGARUH PARAMETER PEMESINAN TERHADAP KEKASARAN PERMUKAAN AI6061BE PADA PEMESINAN CNC MINI 500 WATT MENGGUNAKAN PAHAT KARBIDA STRAIGHT ENDMILL

The role of automatic machining with the help of computer programming such as Computer Numerically Controlled (CNC) has become commonplace today in both large and small industrial sectors or micro industry. CNC is a manufacturing process where previously programmed computer software is converted into code that can automate machining movements, one of which is the milling or lathe process. CNC performance is determined by machining output power. For micro industrial or laboratory scale, CNC with low power is usually used to cut soft materials such as wood, acrylic, aluminum and others. This is the basis for this research to look at the influence of machining parameters such as speed, depth of cut and feed rate on the surface roughness (Ra) of AI6061BE aluminum workpieces in the dry machining process on a mini CNC unit with a maximum spindle power of 500 watts. This research uses an experimental design based on the Taguchi method and takes the form of an L16 (43) orthogonal matrix. The chisel used is a Straight End Mill 2 fluke carbide chisel. The experiment was carried out 4 times cutting in the positive x-axis direction and 4 times in the negative x-axis direction for each variation of machining parameters. From the test results, surface roughness, feed rate is a parameter that is more influential than speed and depth of cut.