Milling Aluminum Alloy Research Articles

Predicting surface roughness in machining aluminum alloys taking into account material properties

ABSTRACT This study investigates the use of machine learning models to predict surface roughness (Ra) in milling multi-grade aluminum alloys without prior knowledge of optimal cutting parameters. A diverse milling dataset encompassing material properties and cutting parameters from various aluminum alloy grades was compiled from research articles. Four machine learning algorithms, Extreme Gradient Boosting (XGB), Random Forest (RFR), Catalogical Gradient Boosting (CAT), and Gradient Boosting Regression (GBR), were employed to develop the predictive model. The dataset underwent cleaning, imputation, and outlier removal to ensure data quality. Feature engineering incorporated material properties and cutting parameters for model training. Performance metrics such as RMSE, MAPE, and R2 were used to assess the models’ accuracy. The SHapley Additive exPlanations (SHAP) technique was employed to interpret the models and identify influential features. GBR achieved the highest prediction accuracy with an RMSE of 0.2507 µm, MAPE of 23.36%, and R2 of 0.8709. Thermal conductivity, feed rate, and cutting speed were consistently identified as the most influential factors, although their rankings differed slightly. This study successfully developed a GBR model for effective Ra prediction in aluminum alloy milling, supporting advancements in smart manufacturing by enabling accurate surface quality prediction and data-driven process optimization through machine learning.

The Study on Optimization of Cutting Parameters to Enhance Surface Finish

Abstract: The Objective of this project work is to Productivity and Surface finish improvement in CNC Die and Punch machining through optimization in cutting parameters. This technical paper studies the different machining parameters i.e. DOC, cutting feed and cutting speed by using different material cutters like cemented carbide cutters, HSS cutters, Insert types cutters. In this thesis an attempt to make use of Japanese optimization technique to optimize cutting parameters during highspeed milling of aluminum alloy using cemented carbide cutting tool

OPTIMIZATION OF CUTTING PARAMETERS TO ENHANCE SURFACE FINISH

The Objective of this project work is to Productivity and Surface finish improvement in CNC Die and Punch machining through optimization in cutting parameters. This technical paper studies the different machining parameters i.e. DOC, cutting feed and cutting speed by using different material cutters like cemented carbide cutters, HSS cutters, Insert types cutters. In this thesis an attempt to make use of Japanese optimization technique to optimize cutting parameters during high-speed milling of aluminum alloy using cemented carbide cutting tool.

FEM-supported machine learning for residual stress and cutting force analysis in micro end milling of aluminum alloys

FEM-supported machine learning for residual stress and cutting force analysis in micro end milling of aluminum alloys

Особенности расчета температуры резания при высокоскоростном фрезеровании алюминиевых сплавов без применения СОЖ

Introduction. The calculation of temperature during high-speed milling of aluminum alloys is of interest, since temperature can act as one of the main limiting factors in choosing rational milling modes. This is especially important when milling thin-walled products used in aircraft construction, since its high values can lead to local warping of the structure. It is not possible to control the temperature factor in production conditions, which makes it necessary to develop a mathematical model for calculating temperature. The purpose of the work is to develop a methodology for predicting the cutting temperature during high-speed milling of aluminum alloy workpieces for cutting conditions, in which it is not possible to use cutting fluid. Methods. This paper presents experimental studies of the cutting temperature during high-speed milling of aluminum alloy workpieces without the use of cutting fluid using non-contact temperature measurement methods. The results obtained were used to determine the coefficients substituted into formulas for calculating temperatures on the front and back surfaces of the cutting blade. Results and discussions. Based on the results of experimental tests and theoretical modeling, a temperature graph is drawn up. A comparison of experimental studies of milling of aluminum alloy D16T, with changing cutting conditions (the cutting speed changed) with theoretical data, gave a satisfactory result. The average relative error when comparing experimental data with theoretical one is 6.05 %. Based on experimental data, it can be concluded that the comparison of experimental data for measuring cutting temperatures is in satisfactory agreement with the proposed method of theoretical calculation of temperatures. The advantage of this technique is that it allows, without time-consuming and costly experimental studies, theoretically calculate (forecast) the temperatures on the front and back surfaces of the cutting blade, as well as the cutting temperature, for those narrow milling conditions, where effective heat removal from the cutting zone is impossible. It can also be used for milling aluminum alloys, the mechanical and thermophysical properties of which differ.

Experimental Investigation of the Surface Roughness for Aluminum Alloy AA6061 in Milling Operation by Taguchi Method with the ANOVA Technique

The surface roughness of the machined parts is the most important parameter to predict the performance of mechanical components. Moreover, predicting the optimal machining parameters conditions is the preferable method for cost reduction and achieving the desired surface quality of the product. This study investigates three cutting parameters, such as depth of cut, spindle speed, and feed for the milling aluminium alloy AA6061, to predict the surface roughness quality. The experimental work utilized a manual milling machine with a coated carbide cutter. Furthermore, the experiments were arranged using the Taguchi L9 orthogonal array (OA) method. The average surface roughness (Ra) was measured and converted to signal-to-noise (S/N) ratio and then analyzed in the statistical method of analysis of variance (ANOVA). Finally, the optimal combination set speed, feed, and depth of cut was 2400 rpm, 30 mm/min, and 0.5 mm, respectively. Also, according to the ANOVA test, the most influential parameter was the spindle speed among the selected parameters, with the highest P value of (66.42%). In comparison, the lowest P value is a depth of cut (5.34%). Furthermore, spindle speed was the only significant factor statistically. By selecting a high spindle speed (2400 rpm), surface quality was enhanced, but the preferable level was low for depth of cut and feed.

Efficient Milling Quality Prediction with Explainable Machine Learning

This paper presents an explainable machine learning (ML) approach for predicting surface roughness in milling. Utilizing a dataset from milling aluminum alloy 2017A, the study employs random forest regression models and feature importance techniques. The key contributions include developing ML models that accurately predict various roughness values and identifying redundant sensors, particularly those for measuring normal cutting force. Our experiments show that removing certain sensors can reduce costs without sacrificing predictive accuracy, highlighting the potential of explainable machine learning to improve cost-effectiveness in machining.

Study of optimization for material processing parameters by means of probabilistic methodology for multi-objective optimization

Optimization for material processing parameters is a typical problem of multi-objective optimization, therefore selection and use of proper multi-objective optimization approach is indispensible. The inherent characteristic of newly proposed probabilistic methodology for multi-objective optimization is that it is with the feature of optimization of multiple objectives at the same time in viewpoint of system theory and in spirit of probability theory. In the present paper, the probabilistic methodology is employed to perform the designs of materials processing for improving quality and cost saving at the same time. The laser welding process of ANSI 304 austenitic stainless steel by using a pulsed Nd: YAG laser welding system and thin-wall machining of milling aluminum alloy 2024-T351 are taken as two examples. The quantitative optimum design of materials processing is performed equitably by conducting the assessment of preferable probability of each alternative. The studies indicate that: (1). the optimized parametric combination for the laser welding process of 2 mm thickness ANSI 304 austenitic stainless steel by using a pulsed Nd: YAG laser welding system is at laser parameters of 2.7 kW peak power, welding speed of 2 cm/min and pulse duration of 4 ms; (2). the optimized combination parameter for the thin-wall machining of milling aluminum alloy 2024-T351 is at tool diameter of 8 mm, feed per tooth of 0.06 mm/z, axial cut depth of 24 mm and radial cut depth of 0.625 mm. The optimal configurations guarantee the comprehensive quality of product and reducing energy consumption.

The Study of Tool Wear Mechanism Considering the Tool–Chip Interface Temperature during Milling of Aluminum Alloy

ADC12 aluminum alloy has been widely used in the aerospace, ship, and automotive fields because of its high specific strength, excellent die-casting performance, and wear resistance. Adhesion wear is the main wear mechanism of high-speed milling ADC12 aluminum alloy. The most important factor affecting adhesion wear is the tool–chip interface friction, which is directly manifested in the tool–chip interface temperature. Therefore, the temperature variation during the milling of aluminum alloy is analyzed using a temperature field model and infrared temperature measurement technology. Then, the tool wear morphology and the tool wear land width are observed using a scanning electron microscope. Finally, the tool wear mechanism considering the tool–chip interface temperature is discussed. The tool–chip interface temperature is related to the friction angle, tool–chip contact length, and friction force at the rake face, which increases first and then decreases as the cutting speed and feed rate increase. During the formation of the adhesive layer, the tool–chip interface temperature increases, the change rate of the cutting force and the tool wear rate increase, and adhesion, oxidation, and abrasive and delamination wear are generated on the tool surface. With the increase in temperature, the tool wear rate increases, the molten adhesive layer on the tool surface is accompanied by crack propagation, and adhesion wear, oxidation wear, and abrasive wear occur on the tool surface.

Experimental study on vibration suppression for robotic milling using an MRE absorber

During the robotic milling process, vibration is one of the main factors that affect the machining accuracy and surface quality due to the low stiffness of the robot structure. Intelligent material, magneto-rheological elastomer (MRE), owns the features of adjustable stiffness, reversible and fast response, has been gradually used for vibration suppression. Therefore, to suppress the forced vibration for robotic milling, this paper used the principle of vibration absorption and combined it with MRE material to design a snubber. A series of excitation and milling experiments were carried out to evaluate its vibration attenuation characteristics. In the excitation experiment, sine signals with frequencies of 40 Hz, 42.02 Hz and 60 Hz with the same amplitude of 5 N were applied to the MRE absorber. In the milling experiment, the dominant frequencies under speeds of 800 RPM, 840 RPM and 1200 RPM were analyzed first. Then, the vibration mitigation characteristics of the MRE absorber during robotic milling of aluminum alloy were explored by adjusting the current. Experimental results showed that under the action of MRE absorber, the acceleration responses are suppressed effectively. In detail, after tuning the current, the amplitude and the root mean square of vibration acceleration in three directions of sinusoidal excitation could be suppressed by more than 19.23% and 7.35% correspondingly. The power spectral density value of the peak frequency could be reduced by more than 69.13%. In milling experiments, the MRE absorber also achieved certain vibration absorption effects. Furthermore, the surface roughness of the workpiece was also fallen by more than 16.51% which provides support for further engineering application in the field of robotic milling. Undoubtedly, the absorber needs further optimization to achieve better vibration suppression ability and a larger working bandwidth.

Finite element simulation of residual stress in milling of aluminum alloy with different passes

Finite element simulation of residual stress in milling of aluminum alloy with different passes

An investigation on performance of castor oil and Pongamia oil based cutting fluid in MQL milling of aluminium alloy 6061

The expense of coolant in milling processes accounts for such a significant portion of overall production costs, research efforts are presently concentrated on finding ways to reduce or do away with the need for lubrication. The environment, the performance of the machining, and the prices of the machining can all benefit from the decrease or elimination of traditional fluid. By employing the Minimum Quantity Lubrication Method, one is able to cut down on lubrication costs while also lowering the potential danger to the health of workers. While end milling aluminium 6061 with a SINOMILL VMC300 and an HSS end mill tool at three distinct spindle speeds (1000, 1500, and 2000 rpm), three distinct doc(0.25, 0.5, and 0.75 mm), and three distinct feed(100,150,200 mm/min), surface roughness (Ra) was measured in dry milling, MQL with base fluid (Pongamia oil, castor oil), conditions. The reduction in Ra is reported as a 12.89% with base fluid (Pongamia oil), in comparison to dry milling. When compared to dry milling, the reduction in Ra that was recorded when using base fluid (castor oil) was 33.72%. In this experiment it was carried out by the Drop shape analyzer (which was manufactured by Kruss), it was found that the contact angle in castor oil is superior to the contact angle in pongamia oil, and the values of contact angle are 43.4 and 47.8 respectively.

Research on Reducing Residual Stress in Milling of Aluminum Alloy

Research on Reducing Residual Stress in Milling of Aluminum Alloy

Influence of the jet tilt angle on geometrical characteristics of the milled pocket on aluminum alloy Al6065

The abrasive water jet (AWJ) is a non-traditional process that can be employed to machine a variety of materials that are significantly difficult to machine using conventional machining processes. This paper presents an experimental investigation conducted to evaluate the influence of the jet tilt angle, a kinematic process parameter, on the characteristics of the milled pocket as milling aluminium alloy 6065. The influences of this parameter are assessed by measuring differences in the depth of the milled pocket, the width, and the slope of the pocket wall. It is found that as the jet tilt angle decreases, it has a significant influence on characteristics of the milled pocket due to changing the material removal mechanism during the erosion process. Insight the influence of the jet tilt angle, this work paves a good fundamental for developing strategies for controlled 3D AWJ machining of complex shapes and improving the quality of the milled surfaces

A comparative study on CNC contour milling performance of AA7475 with 10 % Fly Ash reinforced novel composite with AA7475 for improving surface finish

This experimental investigation aimed to investigate the machinability of 10 wt.%t Fly Ash reinforced Aluminium Alloy (AA7475) composite and compare with Aluminium Alloy (AA7475) minimize the surface roughness in CNC contour milling of Aluminium alloy, AA7475. The composite was fabricated by the Stir casting method with 90 % of base material AA7475 and 10 % of (Fly Ash) reinforcement material. After the reinforced composite was then cast into a flat plate and further sliced into pieces. The sample size is 32 (16 for each group) with a pretest ‘G’ power setting of 80 %. The prepared samples were machined by CNC contour milling, the samples including both groups such as 10 wt.%t Fly Ash reinforced aluminium alloy (AA7475) composite and aluminium alloy (AA7475). For research, green manufacturing approaches are preferred. Surface roughness testing was carried out by using a Profilometer in all machined specimens. Surface roughness values were obtained for both 10 wt% Fly Ash reinforced aluminium alloy (AA7475) composite and aluminium alloy (AA7475) machined specimens, and T-tests were performed using SPSS. The significance value obtained was 0.04 (p < 0.05). Within the constraints of this study, 10 wt% Fly Ash reinforced aluminium alloy (AA7475) recorded the minimum average surface roughness of 19.48 % in CNC Green Machining in specific contour milling for the reinforced composite (AA7475 + Fly Ash) compared with plain aluminium alloy (AA7475).

Cluster analysis approach for identifying optimal cutting parameters in end milling of aluminum alloy 7136 for improved surface roughness

Three cutting parameters are investigated in this study for their effect on the surface quality of aluminum alloy 7136 end milled. The major goal of the study is to use cluster analysis to discover the ideal cutting parameter combination to get the lowest surface roughness achievable. This paper examines cutting parameters and surface roughness for aluminum alloy end milling operations, finds literature gaps, and provides the experimental setup and research methodology. According to the findings, the data points are split into three categories depending on the characteristics of each individual data point. The findings are evaluated considering contemporary literature, and recommendations for further research are provided. As a consequence of this research, it is now clear how to optimize the cutting settings for end milling aluminum alloy 7136, potentially improving the usability and aesthetics of machined components.

Effect of Optimization Software on Part Shape Accuracy and Production Times during Rough Milling of Aluminum Alloy

Reducing production costs during machining processes can be implemented in several ways, including various methods of cutting parameters optimization. The aim of the described research was to evaluate the effectiveness of software that optimizes the NC (Numerical Control) code generated from a CAM (Computer Aided Manufacturing) system. The experiments were carried out in rough milling. For the experiment, a sample with five pockets was designed and fabricated using the original NC programs from the CAM system and optimized NC programs. Evaluated criteria were the impact on the accuracy of the produced surfaces and degree of production time savings. When using the optimization software, despite more intensive cutting conditions, deviations on the side surfaces of the pockets were reduced from 3 to 23%. On the horizontal surfaces, for two pockets, there was an increase in deviations of a maximum of 7.8%, and for the remaining three pockets, there was a decrease in deviations ranging from 8.3 to 36%. The optimized NC programs achieved time savings ranging from 12.8 to 15.9%. This knowledge is important for manufacturers, as it allows shortening production times and thereby reducing related costs.

3D coupled thermo-mechanical simulation of surface roughness and residual stress in end milling aluminum alloy

3D coupled thermo-mechanical simulation of surface roughness and residual stress in end milling aluminum alloy

Process parameter optimization for cutting tool-workpiece interface temperature during end milling of Aluminium alloy 6061 using Taguchi's experimental design

Process parameter optimization for cutting tool-workpiece interface temperature during end milling of Aluminium alloy 6061 using Taguchi's experimental design

Investigation of chatter prediction in end milling using morphological studies on aluminum alloy (Al6061)

Future machine tools have to be highly powerful systems to maintain the needed intellectual performance and stability. The machine tool systems such as Spindle/Tool-holder/Tool assembly are necessary to be optimized for their functionality or cutting performance to meet the productivity and accessibility requirements of the user. Prediction of the dynamic behavior at the spindle tool-tip is, therefore, an important criterion for assessing the machining stability of a machine tool at the design stage. In commercial practice, the machine tool system is extensively influenced by the dynamic rigidity of the spindle system. In this work, a realistic dynamic high-speed spindle/milling tool holder/tool system model is elaborated based on rotor dynamics predictions. Using the finite element modeling with the Timoshenko beam theory, the frequency response at the tool-tip has arrived initially. Further, the numerical model is validated with 3D ANSYS and experimental modal testing. The theoretical stability lobe diagram (SLD) depends on the stiffness and damping properties of the cutting tool and distinguishes the stable and unstable cutting conditions. The mechanism of chatter formation is investigated experimentally during the end milling of Aluminum alloy (Al6061) by two different techniques. Experimental cutting tests are conducted at different depths of cuts, the corresponding vibration signals and cutting samples are examined using the Scanning electronic microscope (SEM) and optical microscope. These studies provide a detailed investigation to predict the stable and unstable cutting zones at different machining conditions.