Machining Of Aluminum Alloys Research Articles

COMPARATIVE STUDY OF DIFFERENT CUTTING FLUIDS ON TOOL-WORK INTERFACE TEMPERATURE DURING TURNING OPERATION ON 6061 ALUMINUM ALLOY

This study investigated the impact of tool-work interface temperature during the turning process of a cylindrical workpiece made of 6061 aluminum alloy. The experiment utilized four different cutting fluids: palm oil-based cutting fluid (POBCF), neem seed oil-based cutting fluid (NOBCF), orange seed oil-based cutting fluid (OSOBCF), and mineral oil-based cutting fluid (MOBCF) using uncoated carbide cutting tool of grade H13A. Various machining parameters were considered, including spindle speed (180, 250, 355, 500 rpm), feed rate (0.105, 0.116, 1.4, 1.6 mm/rev), and depth of cut (0.5, 1.0, 1.5, 2.0 mm). The experimental design followed the Taguchi specified L16 (43) orthogonal array and was conducted on a Lathe Machine XL 400. To measure the tool-work interface temperature, an infrared thermometer (KM 690) was employed during the aluminum alloy machining process. Subsequently, a mathematical model for the tool-work interface temperature values was developed through regression analysis using Minitab 16. The significance of the chosen machining parameters and their respective levels on the tool-work interface temperature was determined using analysis of variance (ANOVA) and F-test. The findings indicated that machining under orange seed oil-based cutting fluid (OSOBCF) conditions resulted in a 9.02%, 16.4%, and 21.7% lower temperature at the tool-work interface compared to palm oil-based cutting fluid (POBCF), neem seed oil-based cutting fluid (NOBCF), and conventional oil-based cutting fluid (MOBCF), respectively. This suggests a potential for producing higher-quality products under orange seed oil cutting fluid conditions compared to other wet conditions.

Machinability of recycled aluminum alloys

The use of recycled (hereafter called secondary) aluminum alloys is increasing more and more in light of sustainability as the energy needed for their production is much lower than in the case of the primary alloys and they may have mechanical properties comparable to those of the latter. Most of the secondary aluminum alloys are used to fabricate parts through casting processes, which may need further machining operations to get the part’s final shape. While the mechanical properties of the secondary aluminum alloys have been comprehensively addressed in the literature and correlated to the different intermetallic particles that characterize their microstructure, the same is not true when addressing machinability. In this framework, the paper investigates the machinability of one primary and two secondary aluminum alloys in terms of cutting forces and surface finish after turning trials carried out at fixed cutting parameters. A detailed characterization of the alloys’ microstructure was carried out making use of both optical and scanning electron microscopy to identify the size, morphology, and distribution of intermetallics. The highest cutting force was registered when machining the primary alloy, being characterized by the highest specific cutting energy. The surface damage in terms of tearings induced by cutting was comparable between the primary and secondary alloys. In contrast, the different roughness features that characterize the machined surfaces of the considered alloys can be partly ascribed to the different intermetallics they present. Nevertheless, the surface topography analysis results must be interpreted based on the specific application.

Efficient evolution mechanism of electrolytic gas products from laser-assisted electrolyte jet machining

Efficient evolution of electrolytic gas products is one of the mechanisms for increased material removal rate in laser-assisted electrolyte jet machining. However, the evolution mechanism of electrolytic gas products with multiple energy fields in laser-assisted electrolyte jet machining is not clear. In order to quantitatively characterize the laser-assisted facilitation of the evolution of electrolytic gas products, a gas quantification and detection device was designed and built in this paper. Compared to traditional electrolyte jet machining, laser-assisted electrolyte jet machining of 2024-T3 aluminum alloy increased the gas production by 52 % in 180 s. For the anode, the surface modification induced by laser texturing reduces the oxygen evolution potential and surface free energy and promotes gas nucleation and detachment. In addition, laser-induced conductive plasma can enhance transient currents for electrolyte jet machining and cause machining vibrations. The cathodic gas discharge phenomenon was characterized in conjunction with interelectrode gap visualization experiments and cathodic nozzle damage. Experimental results show that cathodic gas discharge depends on strong electric field and high laser pulse fluence. In summary, laser temperature rise, anode surface modification, laser-induced plasma, plasma recoil and cathode gas discharge can all contribute to the evolution of electrolytic gas products.

Optimization of Machining Parameters for Product Quality and Productivity in CNC Machining of Aluminium Alloy

This study focused on optimizing the process of CNC machining to enhance productivity and product quality of surface finish Ra via the process parameters of the cutting speed (vc), feed rate (vf), and cutting depth (doc). Experimentation was performed on workpieces of AA-6061 to investigate the response Ra through variation of the process parameters to analyze their best fit using RSM with 23 full factorial designs L-8 of DOE. The analysis of variance (ANOVA) was then used to find the major contributors among them that were responsible for the Ra. Based on the result, better Ra was obtained at 0.103 μm using the best fit of vf (150 mm/min), vc (220 m/min), and doc (0.1 mm). ANOVA shows vf contributed better Ra followed by vc and doc respectively. In addition, the level of Ra’s was analyzed through contour plots represented by different colours. It continued to analyze the effect of the process parameters via the main effects plot, Pareto chart, and the contour plot in the predictive desirability model, which indicated that the plots and chart confirmed the vf had more influence compared to others. The studyconfirmed that the low-level parameters provided better Ra to be used for polishing.

FABRICATION AND CHARACTERIZATION OF AA6082-T6 PRODUCED BY FRICTION STIR ADDITIVE MANUFACTURING

The machinability of aluminum alloy (Al-6082) has the scope of enhancement by integrating Friction Stir Processing (FSP) with tool geometry, tool shape and different tool materials. In this research, the role of tool pin shape has been studied by experimenting on various tool shapes (square, hexagonal and octagonal) with the aim of machinability improvement. In FSP, the tool rotation speed is 930[Formula: see text]rpm and tool travel speed is 26[Formula: see text]mm/min. The specimens have undergone tensile, microhardness and microstructure testing using optical microscope and scanning electron microscopy. The percentage elongation has been increased from 15% to 29% in the case of the octagonal-shaped tool pin; similarly, the (Vickers’) microhardness value is 14.6% higher than the square- and hexagonal-shaped tool pins. Among these tool pin shapes, the octagonal-shaped tool pin succeeded in providing the best-in-class machinability performance on Al-6082 T6 alloy with FSP. It may unveil the newer scopes of FSP on Al-6082 for automotive components assembly and other similar applications.

Workpiece surface laser regulation in laser-induced plasma electrolyte jet machining: Position, shape, and energy

Laser-induced plasma electrolyte jet machining is a new hybrid machining method. This machining method utilizes the plasma impact effect and plasma to increase the inter-pole conductivity for material removal, with low laser energy loss, high material removal rate (MRR), and good surface quality. The advantages of laser machining and electrolyte jet machining are fully utilized. However, it is difficult to avoid refraction of the laser in the electrolyte column, which affects the efficient coupling of the laser to the electrolyte jet machining. In this paper, to improve the localization and MRR, a study is carried out to address the problem of energy utilization efficiency in laser-induced plasma electrolyte jet machining. A compensation model for the refraction of the laser beam in the electrolyte is proposed. Compensation improves the shape and positional accuracy of the laser beam coupled to the electrolyte beam during aluminum alloy machining. Compared before and after compensation, the MRR was improved by 30.2 %, and the groove localization was improved by 37.4 %. Realization of high energy coupling for laser electrolyte jet hybrid machining.

CHARACTERIZATION OF SURFACE, SUBSURFACE DAMAGE AND THEIR EFFECT ON FATIGUE LIFE AFTER MACHINING OF AEROSPACE-GRADE ALUMINUM ALLOY (Al 6082-T6)

Aluminum alloys are widely used in the automotive and aerospace industries due to their lower mass-to-strength ratio than other metallic alloys. Apart from their inherent properties, aluminum alloys like other metallic alloys show a significant change in their mechanical properties according to the machining parameters. The research literature on obtaining optimum mechanical properties of aluminum alloys that undergo machining is very limited. Moreover, the combined effect of several parameters on the machinability of aluminum alloys has not yet been explored. In this paper, the effect of three machining parameters (Depth of Cut (DoC)), feed rate (FR), and cutting speed (CS) on the subsurface damage and fatigue life of aerospace-grade aluminum alloy (Al-6082-T6) is observed. Samples are prepared using a full fractional approach to effectively capture the effect of all input parameters. Thereafter, samples were subjected to surface roughness, micro-hardness, and fatigue life tests. Results of surface roughness and micro-hardness tests are compared with fatigue life. The general linear model was employed to capture the percentage effect of each input parameter on the output parameters. The results showed that DoC was the main contributing factor that caused subsurface damage, while surface roughness and fatigue life were mainly affected by FR and CS. Optical microscope images showed a white layer formation that had higher hardness than the base metal. Overall, this research work proposes the input parameters that can be used to achieve minimum surface damage and fatigue life.

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 Impact of Heating Temperature and Flux Ratio on the In-Situ Casting Technique as a Direct Recycling of AlSi7Mg Machining Chips

Recycling aluminium and its alloys is gaining popularity in reducing energy consumption and associated manufacturing expenses. The fluxing process is the preferred and often-used technique in the recycling sector for removing oxide inclusions from molten aluminium. The present study investigates the influence of flux ratio and heating temperature on the behaviour of aluminium alloy chips (AlSi7Mg) during in-situ casting. The primary objective is to create a more efficient and economical approach to recycling aluminium waste. The chips underwent heating for 30 minutes inside a controlled laboratory furnace. The temperatures used during this procedure were 750°C and 800°C. The chips were combined with flux in ratios of 1:0.1 and 1:0.2. The number of melted chips rose due to increasing the temperature and flux ratio. The heated samples were subjected to surface morphology and elemental analysis using a scanning electron microscope with energy-dispersive spectroscopy (EDS). The Energy Dispersive Spectroscopy (EDS) examination indicated the presence of oxygen on the surface of the unprocessed aluminium alloy machining chips. On the other hand, the X-ray diffraction (XRD) revealed that the observed condition may be attributed to the oxidation process, proven by the presence of Aluminium Oxide (Al2O3).

Novel methods for optimizing CNC aluminum alloy machining parameters in polymer mold cavities

The examination of the machining of 7075-T6 aluminum alloy polymer mold cavities using Taguchi optimization and analysis of variance is presented in this paper. This study identified the best CNC milling cutting parameters and used a mathematical model to quantify the surface roughness of the machined cavities. The findings showed that while using a flat endmill, the spindle speed multiplied by feed rate contributed 28.01% to surface roughness, and when using a ball endmill, the squared depth of cut contributed 41.27%. Using both flat and ball endmills, the depth of the cut contributed 98.53% to the material removal rate. A refined second-order linear regression model was employed to forecast the endmill-machined surface roughness. The Warp Surf Portable tester measured values that were outside the error range of approximately 0.257% and 2.8%, respectively, for the expected values. Surface roughness has a 99.97% correlation coefficient in the regression model, indicating a very significant link. Additionally, the study improved the cutting parameters for a ball endmill, which were 3005 Rpm, 726.7 mm/min, and 0.43 mm, and for a flat endmill, these were spindle speed (2500 Rpm), feed rate (650 mm/min), and axial cut depth (0.5 mm). The outcomes demonstrated how well the techniques enhanced mold cavity machining and cost estimation using Ra and MRR data. Consequently, these results can be applied to future academic studies and industrial applications.

Evaluation of the performance of DLC Coatings (ta-C) of WC-Co tools while machining Al7075 alloy

This study investigates the performance of DLC-coated 3-flute Ø 12 mm WC-Co (10%) end mills in the high-speed machining (226 m/min) of Al7075 Aluminum alloy, with high-pressure (20 bar) cooling applied in all tests focused on the cutting zone. Rolled blocks with dimensions of 150 × 350 × 150 mm were used as workpiece material. Slot milling was preferred as a test method for machining tests. Each end mill machined 757 slots with a length of 350 mm at a depth of 3 mm, and a total volume of 9150 cm3 was removed with each tool. In total, three groups of endmills were tested. A1 is a specially designed endmill for aluminum machining and was tested in an uncoated state, while A2 has the exact same geometry as A1 and was coated with tetrahedral amorphous carbon (ta-C), which is a form of Diamond-Like Carbon (DLC) coating. Finally, the A3 endmill was tested as an industrial comparative designed for aluminum machining, which was also ta-C coated. Performance tests have shown that ta-C coating significantly prevented the formation of the built-up edge. In addition, it reduced the average bending moment on the tool by 20%–25% by acting as a lubricant. It has been observed that DLC coating reduces tool wear. It was determined that flank wear was reduced by 28% and rake wear by approximately 58% in the A2 tool compared to the uncoated tool. The DLC-coated tools also provided smaller chips and improved chip control.

Research on Machinability of Dry Cutting 7075 Aluminum Alloy with TiN Coating Micro-Texture Turning Tool

This document suggests a cutting technique for coating the surface of micro-textured structural tools, aiming to tackle the challenges in machining aluminium alloy materials that are hard to machine and simple to soften and stain at elevated temperatures. Initially, considering the varied conditions of cutting dosage during the process caused by diverse forces, the favored parameters for cutting are; next, creating two distinct phases of the micro-texture for application on the tool’s surface, conducting cutting simulation tests with the previously mentioned ideal parameters to evaluate these micro-texture structures in enhancing tool efficiency; ultimately, the focus is on the optimal surface coating of the micro-texture tool to examine how the coating material enhances at the tool’s cutting temperature. Studies have shown that the effectiveness of concave elliptical micro-textured tools in decreasing cutting force and temperature is markedly superior to that of convex elliptical micro-textured tools; experiments revealed that applying a coating to the concave elliptical micro-textured surface of the tool markedly enhances the friction level of both the cutting tool’s front surface and the workpiece’s cutting layer in dry cutting scenarios, thereby decreasing the cutting temperature and chip damage, markedly enhancing the aluminium alloy material’s ability to heat stain the tool.

ENHANCING MILLING PERFORMANCE OF 6061 ALUMINUM ALLOY WITH NANOCUTTING FLUID AND MQL

During the machining of aluminum alloys, the adhesion of chips to the tool affects the performance characteristics. Today, different cooling systems are used to eliminate these negativities. In this study, the effects of end milling using HSS and carbide cutting tools of 6061-T6 aluminum alloy on surface roughness, chip thickness ratio and tool wear were examined using different cooling techniques (dry, minimum quantity lubrication (MQL) and nanocutting fluid). Different cutting speeds (180, 200, 220 m/min) and different feed rates (0.05, 0.06, 0.07 mm/rev) were used in the experiments. According to experimental findings, tool wear and surface roughness decreased at low cutting speed and feed rate by using nanocutting fluid with carbide cutting tools. It has been observed that the chip thickness ratio increases with high cutting speeds using nanocutting fluid and decreases with dry machining and high feed rates. The best milling performance of the aluminum alloy was achieved in experiments using carbide cutting tools and nanocutting fluid.

Investigation on the damage mechanism of micro-cutting surface layer of aluminum alloy 7075 with different heat treatment processes

This paper investigates the machinability and microscopic surface damage mechanisms of aluminum alloy 7075 subjected to different heat treatment processes during micro-cutting. Focusing on T6 and T73 heat-treated aluminum alloy 7075, the study employs a combination of experiments and simulations to analyze micro-cutting surface morphology, cutting forces, microstructure evolution, residual stress, and damage characteristics. The findings reveal that micro-cutting of aluminum alloy 7075 after T6 heat treatment exhibits no apparent speed sensitivity in the primary cutting force, while T73 heat treatment demonstrates noticeable speed sensitivity. The surface microscopic damage during micro-cutting is influenced by both cutting speed and depth, resulting in distinct forms and degrees of damage under varying conditions. An analysis of the residual compressive stress field indicates that the magnitude of residual compressive stress on the micro-cutting surface determines crack initiation, and the maximum depth of residual compressive stress dictates crack propagation. Microscopic damage in T6 heat-treated aluminum alloy 7075 predominantly occurs in the second and third deformation zones, while T73 heat treatment leads to most damage in the third deformation zone. Notably, crack propagation is evident in T6 heat treatment at any cutting speed with a cutting depth of 150 μm, whereas it is inconspicuous in T73 heat treatment at cutting speeds below 540 m/min. Furthermore, at cutting speeds below 540 m/min and a cutting depth of 150 μm, T6 heat-treated micro-cutting is prone to oxidative wear. In contrast, at a cutting speed of 72 m/min and a cutting depth of 150 μm, the predominant wear form in T73 heat-treated micro-cutting is oxidation wear. This study underscores the intricate interplay between heat treatment processes, cutting conditions, and microscopic damage in aluminum alloy 7075 micro-cutting. The nuanced insights provided contribute to a deeper understanding of the machining characteristics of this alloy, paving the way for improved processing methodologies.

Artificial neural network in the prediction of surface roughness: A comparative study

Surface roughness is a key parameter to consider in the machining of aluminum alloy. It is rendered as one of the important determinants of the performance of mechanical instruments or components. Owing to its excellent mechanical properties, and ease of machinability, Aluminum 6061 (Al6061) is rendered a popular choice in many industries. Achieving a desired surface finish is crucial for the performance and longevity of machined components. This study aimed to compare the predictive performance of the artificial neural network (ANN) model versus the response surface methodology (RSM) in the prediction of surface roughness in the turning process of Al6061. ANN performed better than RSM in the prediction of surface roughness (A20 index 0.93 and 0.86 for ANN and RSM models respectively). MAPE and sMAPE were also found to be lower in the ANN model compared with the RSM model (8.06 versus 9.69, and 0.039 versus 0.047 respectively) indicating that the ANN model had a better predictive performance compared with the RSM model. Both ANN and RSM models showed that cutting speed and feed rate were the most important determinants of surface roughness in the turning process of Al6061 in other words to achieve a smoother surface during the turning process of Al6061 high cutting speed and low feed rate should be used. The findings of this study reflect the potential utility of ANN in the prediction and subsequently optimizing cutting parameters to achieve a smoother surface.

Toward clean manufacturing: an analysis and validation of a modified Johnson–Cook material model for low and high-speed orthogonal machining of low-carbon aluminum alloy (Al 6061-T6)

Toward clean manufacturing: an analysis and validation of a modified Johnson–Cook material model for low and high-speed orthogonal machining of low-carbon aluminum alloy (Al 6061-T6)

Thermodynamic and Microstructural Analysis of Lead-Free Machining Aluminium Alloys with Indium and Bismuth Additions.

The present study comprises an investigation involving thermodynamic analysis, microstructural characterisation, and a comparative examination of the solidification sequence in two different aluminium alloys: EN AW 6026 and EN AW 1370. These alloys were modified through the addition of pure indium and a master alloy consisting of indium and bismuth. The aim of this experiment was to evaluate the potential suitability of indium, either alone or in combination with bismuth, as a substitute for toxic lead in free-machining aluminium alloys. Thermodynamic analysis was carried out using Thermo-Calc TCAL-6 software, supplemented by differential scanning calorimetry (DSC) experiments. The microstructure of these modified alloys was characterised using SEM-EDS analysis. The results provide valuable insights into the formation of different phases and eutectics within the alloys studied. The results represent an important contribution to the development of innovative, lead-free aluminium alloys suitable for machining processes, especially for use in automatic CNC cutting machines. One of the most important findings of this research is the promising suitability of indium as a viable alternative to lead. This potential stems from indium's ability to avoid interactions with other alloying elements and its tendency to solidify as homogeneously distributed particles with a low melting point. In contrast, the addition of bismuth does not improve the machinability of magnesium-containing aluminium alloys. This is primarily due to their interaction, which leads to the formation of the Mg3Bi2 phase, which solidifies as a eutectic with a high melting point. Consequently, the presence of bismuth appears to have a detrimental effect on the machining properties of the alloy when magnesium is present in the composition.

Experimental Analysis of Machining Parameters In Turning of Aluminum 7075

High-Speed Machining (HSM) of aluminum alloys represents a critical area in manufacturing industries, including automotive, aerospace and consumer electronics. This research presents an in-depth investigation into the effects of key process parameters on the HSM of Aluminum 7075, a high-strength alloy with superior mechanical properties. Utilizing the central composite design of Response Surface Methodology (RSM), the study scrutinizes the impact of process parameters, including cutting speed, feed, depth of cut and tool nose radius on surface roughness. The findings reveal feed and nose radius as primary factors influencing surface roughness while cutting speed and depth of cut play secondary roles. This comprehensive analysis contributes to the knowledge base for efficient machining practices and lays the groundwork for future optimization strategies. It also underscores the necessity for further research into understanding the intricate dynamics of machining parameters to enhance operational efficiency and product quality in the machining of Aluminum 7075 and similar alloys.

Solid state recycling of aluminium chips: Multi-technique characterization and analysis of oxidation

Solid-state recycling of metallic alloys is a promising alternative method which avoids remelting and reduces significantly the CO2-emission compared to remelting. Contamination being critical for solid-state recycling, the objective of this study is to quantify and localize oxygen contamination at each step of the process. Pre-compacted AA6060 aluminium alloy machining chips were hot-extruded. The oxygen intake and the formation of oxides were quantified and localized through a multi-technique characterization, including X-ray photoelectron spectroscopy and transmission electron microscopy. The overall oxygen content is found to increase at every step of the recycling process. Analyses reveal that the initial thin Al2O3 layer on the chips gets covered by a MgO layer during pre-annealing and hot extrusion. Thus, alloying elements, as Mg, are involved in oxidation. It results in a tenfold increase of the oxygen content in the extrudate accompanied by a complex network of oxides with an average thickness of 290 nm and a low density. Thus, tuning processing parameters is a promising leverage to limit oxidation and improve chip welding, which will probably depend on the alloying elements.

A novel deep learning method with partly explainable: Intelligent milling tool wear prediction model based on transformer informed physics

With the trend of lightweight in the field of intelligent electric vehicles and 3C, the demand for high precision machining of aluminum alloy parts is growing. And tool condition monitoring (TCM) is very important for quality control of parts, so intelligent high-accuracy wear prediction of aluminum alloy high precision machining tools has great industrial application value at present and in the future. This paper presents a novel TCM model (Conv-PhyFormer) of Transformer with physics informed. The model has excellent ability to capture short-term and long-term dependencies from nonlinear cutting time series data when there are few training samples. The embedded hard physical constraint and soft physical constraint in the model make the model partially interpretable. Soft physical constraint in the form of one-dimensional causal convolution can help the proposed model better learn the local context. Hard physical constraint in the form of the mathematical equation representing cutting physical knowledge are embedded, thus the model does not need to learn this knowledge from time series data from scratch. A large number of analysis results of aluminum alloy machining experimental data show that the proposed Conv-PhyFormer has significantly superior prediction accuracy and robustness compared with the current three popular deep learning models for TCM. Embedded soft and hard physical constraints can significantly reduce the training epochs of Transformer prediction model.