Optimizing dry milling of stir-cast and heat-treated AZ80 magnesium alloy using multiple criteria optimization technique: an experimental study | Scientific Reports

Oct 29, 2024

Scientific Reports volume 14, Article number: 25801 (2024) Cite this article

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The primary aspects of this research are to evaluate surface roughness, cutting force, and material removal rate and optimize it with dry milling process parameters for heat-treated and stir-cast AZ80 magnesium alloy. Multiple methodologies are utilized in the research, which includes the Integration of design of experiments—response surface methodology for experimental design with the technique for order of preference by similarity to ideal solution for multi-criteria optimization. In order to evaluate the effect of process parameters on the response, the experimental design manipulates the depth of cut, feed rate, and cutting speed in a systematic manner. An evaluation of the machined surface’s quality is conducted via surface roughness measurements. Likewise, insights into the forces exerted during milling can be obtained through continuous monitoring of cutting forces. The calculation of material removal rate is predicated on weight reduction. The interaction between the depth of cut and feed rate has a significant impact on the critical-to-quality characteristics of the alloy, which has contribution percentage greater than 25%. This finding validates that despite the heat-treated alloy having a similar composition to the as-cast alloy (where the closeness coefficient is 0.9843), the optimal process parameters of the former are not applicable to the latter. Nevertheless, the technique used to prepare the specimen has no bearing on the material removal rate, which is a process parameter-specific effect.

The potential of magnesium (Mg) alloys in the engineering of lightweight structures has garnered increased interest, especially in light of the worldwide energy crisis. These materials present numerous benefits when implemented in structural contexts. These materials are distinguished by their advantageous combination of characteristics, which includes a lower density of 1.74 gm/cm3 and weights approximately 35% and 78% lighter than iron (Fe) and aluminum (Al), respectively. In addition to these attributes, magnesium alloys possess favorable suspension capacities, castability, weldability, machinability, and recyclability. In the twenty-first century, magnesium alloys are considered “the green material” with the most application potential due to their light weight, high specific strength and impact resistance, effective electromagnetic shielding, simple recycling, and thermal conductivity1. However, its cold workability at room temperature is significantly restricted due to its hexagonal close-packed (HCP) crystal structure, which impedes its effectiveness as a wrought material. The extensive range of benefits associated with magnesium (Mg) alloy milling renders it a critical process in numerous sectors. By achieving this weight reduction without compromising the alloy’s exceptional specific strength, this method has become an essential component in sectors where weight is critical, such as aerospace and automotive2. The automotive and aerospace industries have been identified as the primary users of the casting process to produce components with improved mechanical properties using light metals3. The evaluation of the impacts of casting process parameters has been primarily concentrated on composites of magnesium alloys3. In addition, it guarantees accuracy by providing components with precise measurements and stringent tolerances. It is of utmost importance in industries where precision is core. Furthermore, superior surface finishes can be achieved through milling, which eliminates the need for additional finishing stages. Among the diverse range of magnesium alloys that are commercially accessible, AZ alloys, which belong to the Mg–Al–Zn ternary system, have been extensively utilized in industrial contexts. Illustratively, Mg AZ80 is a prominent constituent of this category, habitually comprising 8.0 wt% Al, 0.5% Zn, and a minute quantity of Mn employed as an alloying element. This Mg alloy of moderate strength is favoured for numerous industrial applications due to its exceptional forging capabilities and resistance to corrosion4,5. Magnesium alloy can have its mechanical properties improved via extrusion, forging, and rolling. In order to further enhance the mechanical properties of structural materials to meet specific requirements, subsequent thermal treatment may be applied. AZ80 alloy, which is distinguished by the substantial addition of zinc and aluminum, is amenable to thermal treatment for the purpose of reinforcement. In recent times, a range of thermal treatment methodologies have been devised with the intention of augmenting the mechanical characteristics of deformed magnesium alloy, including T6 and T4 treatment. A systematic investigation was conducted to examine the impact of solution and ageing thermal treatment on the microstructure and mechanical properties of as-cast AZ80 magnesium alloy. Ultimately, an optimal solution and two-stage ageing heat treatment process were determined6. Recently, the cyrogenic milling behaviour of AZ91 Mg alloy was analyzed7. The machinability of AZ80 based composites were studied and the process parameters were optimized based on Teaching-Learning based algorithm (TLBO)8. AZ80, a high-strength magnesium alloy consisting predominantly of magnesium (Mg), aluminum (Al), and zinc (Zn), demonstrates additional advantageous characteristics, including reduced hardness and enhanced ductility. The exceptional machinability of this alloy facilitates the fabrication of complex and accurate parts, thereby substantially diminishing manufacturing expenses and time. While AZ80 can be utilized for effective material removal through milling, this process may result in tool fatigue and surface damage. As a process of rapid material removal, milling provides an ideal environment in which to investigate the Material Removal Rate (MRR) of AZ80 magnesium alloy. By providing operators with precise control over the machining process, it is possible to attain the desired surface finishes and dimensional accuracy. Milling is a suitable process for AZ80 magnesium alloy due to its favorable surface finishes, high material removal rates, and precision. By generating attribute evaluations, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is of critical importance. Response Surface Methodology (RSM) is an effective approach for approximating the functional relationship between independent variables and the response variable in a practical manner. RSM is utilized to determine the optimal process parameter combination that yields the highest or lowest response value9. The research on the tribological characteristics of AZ80 indicates that the wear resistance of T6 heat-treated alloy is superior. At both room temperature and elevated temperatures, this alloy undergoes delamination and corrosive (oxidation) wear mechanisms10. In the meantime, the rate at which the tool advances across the workpiece is determined by the feed rate, which has a direct impact on the rates at which material is removed and the attrition of the tool. It is necessary to conduct extensive experimentation and analysis in order to tailor these parameters to the specific needs of each milling application11. Exploration and optimization of the dry milling process parameters for heat-treated AZ80 magnesium alloy is the purpose of this research paper. The objective of the study was to comprehensively examine the impact of T4 and T6 heat treatment and milling process parameters on critical response variables by employing response surface methodology. The study specifically investigates the effects of these variables on key performance indicators, including cutting force, material removal rate, and surface roughness. The study’s objective is to offer a thorough comprehension of the complex correlation between heat treatment conditions and milling process parameters. By doing so, it will provide valuable insights that can be utilized to optimize the end-milling of AZ80 magnesium alloy. This, in turn, will enable the achievement of improved surface quality and material removal efficiency while requiring minimal cutting force. An investigation into the impact of the heat treatment procedure on quality attributes was also provided, along with a novel method for examining the relationship between cutting speed, feed rate, and depth of cut and response variables.

An AZ80 Mg alloy, fabricated via stir-casting techniques, serves as the specimen under investigation. The elemental composition of the prepared alloy is estimated using EDS analysis, which is Mg-8.2Al-0.61Zn-0.2Si-0.09Mn-0.07Cu. This study utilizes three separate sets of AZ80 samples: one in its as-cast condition and two after T4 and T6 heat treatments. The sample heat treated at a solutionizing temperature of 420 °C for 10 h in a muffle furnace and water quenching is termed as T4 heat treatment process. The age-hardening process utilized to treat the solutionized specimen for an additional 10 h at 200 °C is known as the T6 heat treatment process. The microstructure of the prepared alloy was analysed by Field Emission Scanning Electron Microscopy (FE-SEM). The comprehensive methodology of the research is illustrated in Fig. 1. The critical input parameters for dry milling AZ80 Magnesium Alloy are as follows: spindle speed (rpm), feed rate (mm/min), and depth of cut (mm). The upper and lower limits for each of these parameters are specified in Table 1.

Schematic representation of the methodology of this study.

The study utilized an experimental design that was founded upon the fundamental principles of response surface methodology (RSM). The RSM method is extensively recognized and implemented in experimental design due to its ability to evaluate the influence of multiple factors and their interactions on one or more response variables. By employing RSM in this study, the effect of varying cutting speed, feed rate, and depth of cut on the surface quality (as measured by Ra, Rz, Sa, Sz), Material Removal Rate (MRR), and Resultant Force (Fr) will be investigated12,13. The DoE for the Dry Milling of AZ80 Magnesium alloy is given in Table 2. Dry milling was performed on AZ80 utilizing a CNC vertical milling machine. It measured 50 mm by 30 mm by 5 mm in size. The machining was accomplished utilizing a high-speed steel (HSS) end mill tool with a diameter of 6 mm. With the intention of quantifying the cutting force, the specimen was positioned above the dynamometer (9255 B, Kistler – Singapore). The experimental setup is shown in Fig. 2. The calculation of the material removal rate (MRR) for dry milling AZ80 involves dividing the discrepancy in specimen weight between the conditions before and after machining by the total machining time. The unit of expression is gm/min. The optical profilometry system was employed to determine the surface roughness of the machined specimens (Surtronic 25, Talysurf CCI, Taylor Hobson) with a 20X objective lens at 0.8 mm2.

Experimental setup of end milling of AZ80 Mg alloy.

The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methodology is a widely used multi-criteria decision-making (MCDM) approach that helps in identifying the best option from a set of alternatives based on their relative closeness to an ideal solution. The core idea of TOPSIS is to determine the geometric distance of each alternative from both the ideal (best possible) and the nadir (worst possible) solutions. The alternative closest to the ideal solution and farthest from the nadir solution is considered the best. The TOPSIS process involves several steps: constructing the decision matrix by listing all alternatives and criteria, normalizing the decision matrix to convert criteria values into a comparable scale, applying weights to the normalized criteria based on their importance, identifying the ideal and nadir solutions by selecting the best and worst values for each criterion, computing the Euclidean distance of each alternative from the ideal and nadir solutions, determining the relative closeness of each alternative to the ideal solution, and finally ranking the alternatives based on their relative closeness, with the highest-ranked alternative being the optimal choice. The significance of TOPSIS over other optimization methods lies in its simplicity, efficiency, and ability to handle both qualitative and quantitative data. Unlike other MCDM methods such as Analytical Hierarchy Process (AHP) or Elimination and Choice Expressing Reality (ELECTRE), TOPSIS directly accounts for the relative closeness to an ideal solution, making it more intuitive and easier to understand. Additionally, TOPSIS is less computationally intensive and can handle large datasets efficiently. It provides a clear and straightforward ranking of alternatives, which is particularly beneficial in practical applications where decision-makers need quick and reliable solutions.

Schematic representation of TOPSIS methodology.

The preceding steps of the TOPSIS methodology are illustrated in Fig. 3. In this study, the TOPSIS method is utilized to resolve the results of multi-objective experiments through a singular optimization procedure. By giving equal weight to each response variable, this study attempts to achieve a maximum material removal rate (MRR) while minimizing surface irregularity and cutting force (Fr). The ultimate determinations are reached upon achieving the utmost quantifiable term known as the closeness coefficient (Ci).

Figure 4 shows the microstructure of as-cast, T4, and T6 AZ80 Mg alloy. Figures 5, 6, 7, 8, 9 and 10 exhibit the optical profilometry results for AZ80 Mg alloys in their as-cast, T4, and T6 states. Figures 5, 6 and 7 display the 2D surface morphology, while Figs. 8, 9 and 10 demonstrate the 3D surface morphology. Surface roughness after heat treatment is significantly higher than that of as-cast alloy, according to optical profilometry findings. As the alloy undergoes heat treatment, its strength is directly proportional to the increase in surface roughness. The tensile strength of T4 alloy improved by 14.01% and that of T6 alloy increased by 5.16% when compared with as-cast alloy, respectively, according to our prior experimental results on the mechanical properties of AZ80. Grain coarsening is also seen in the alloy that has been heat treated14. According to the literature, surface roughness is determined by grain size. Nevertheless, the correlation between grain size and roughness remains ambiguous15,16. The experimental findings of this investigation indicate that the heat-treated alloy develops a substandard surface finish due to the coarsening of granules and the increase in strength. Tables 2 and 3, and 4 present the experimental outcomes pertaining to dry milling of as-cast, T4, and T6 AZ80 alloy, respectively. Our previous study shows that “the grain size of the as-cast, T4, and T6 AZ80 are 44.43 ± 17 µm, 71.48 ± 29 µm and 72.64 ± 25 µm, respectively”14. The cutting force obtained is as follows: as-cast > T6 > T4. The cutting force observed in T4 AZ80 was the lowest. As-cast Mg17Al12 intermetallic phases are discernible, whereas T6 AZ80 is predominately composed of Mg2Al3 intermetallic. Conversely, a greater volume percentage of α-Mg is present in T4 AZ80, rendering it gentler in comparison to intermetallic phases. This characteristic diminishes the cutting force required to sever heat-treated alloy14. The cutting forces diminish as the number of grain boundaries per unit surface area decreases in conjunction with an increase in grain size16. The surface integrity is affected by the alloy’s strength, and the cutting force required to process the material is affected by the grain size. In all test settings, the MRR of as-cast, T4, and T6 AZ80 is similar, regardless of the specimens.

FE-SEM micrograph of (a) as-cast; (b) T4; (c) T6 AZ80 Mg alloy.

2D surface morphology of as-cast AZ80 Mg alloy after dry milling under all experimental trials.

2D surface morphology of T4 heat-treated AZ80 Mg alloy after dry milling under all experimental trials.

2D surface morphology of T4 heat-treated AZ80 Mg alloy after dry milling under all experimental trials.

3D surface morphology of as-cast AZ80 Mg alloy after dry milling under all experimental trials.

3D surface morphology of T4 heat-treated AZ80 Mg alloy after dry milling under all experimental trials.

3D surface morphology of T6 heat-treated AZ80 Mg alloy after dry milling under all experimental trials.

The experimental outcomes presented in Tables 2, 3 and 4 are optimized in relation to the process parameters specified for each test condition and are regarded as response variables. The determination of the weighting of response variables is contingent upon the particular requirements of the research. The weights allocated to response parameters are distributed uniformly and are allotted an equal value of 0.167. The methodology is as follows: as illustrated in Fig. 3, the response variables are normalized prior to calculating the weighted normalized decision values matrix. In order to ascertain the degree of proximity to the ideal value solution, the calculations for the separation measures from both the positive ideal value solution and the negative ideal value solution are executed. The values of the closeness coefficients and the corresponding rankings of the different alternatives are presented in Table 5. The feasibility and impracticability of the trials are determined by comparing the Ci values of As Cast, T4 and T6 heat-treated samples (with the maximum Ci value being preferable). The TOPSIS plainly indicates that the as-cast, T4, and T6 alloys achieved their maximum Ci values of 0.9846, 0.9395, and 0.9374 at Trial No. 06, 14, and 11, respectively. Notably, the optimal process parameters for both heat-treated alloys are as follows: S = 1000 rpm; f = 100 mm/min; and d = 1.0 mm, as detailed in Table 6. The thermal treatment process exerts a substantial influence on various characteristics of a material, including grain size, phases, and tensile strength17,18. Furthermore, under all test conditions, Trial 08 exhibits undesirable responses: S = 1500 rpm, f = 100 mm/min, and d = 1.5 mm.

The contour plots of feed rate versus spindle speed, depth of cut versus spindle speed, and depth of cut versus spindle speed on Ci of as-cast, T4, and T6 AZ80 alloy are displayed in Fig. 11. The contour plot is a valuable tool for determining the impact of distinct process parameters on the response Ci over a specified range of process parameters. When working with as-cast AZ80, the minimum speed, maximum depth of cut, and minimum feed rate are all desirable. Lower feed rates resulted in improved surface roughness due to the reduced tool deflection and minimized vibration, which contribute to a more stable cutting process and finer surface finish. - Optimal depth of cut was found to improve surface roughness by balancing the cutting forces and minimizing tool wear, thereby maintaining a consistent and smooth cutting action. In contrast, T4 and T6 exhibit comparable patterns in the contour plot. For a consistent depth of cut and spindle speed, the optimal feed rates are 50–75 mm/min and 125–150 mm/min, respectively. It is desirable to have the highest depth of cut at the lowest spindle speed with a constant feed rate, and, conversely, for both T4 and T6 heat-treated alloys. Although the MRR increases regardless of feed rate, the rate of increase is significantly greater with a higher feed rate19,20.

Contour plot of closeness coefficient of as-cast, T4, and T6 heat-treated AZ80 Mg alloy.

As shown in Table 7, the ANOVA results offer a comprehensive understanding of the significance of machining parameters and their interactions in determining Ci under the three conditions of AZ80 Magnesium alloy: as-cast, T4, and T6. The ANOVA analysis indicates that the model is statistically significant in the as-cast condition, accounting for 76.19% of the total variation (Ci). Within the realm of linear effects, depth of cut and feed rate each make significant contributions of 14.04% and 10.21%. Analogous to the current investigation, the examination of high-speed milling of magnesium alloy revealed that the depth of cut is a crucial determinant, given that an increased depth of cut leads to compromised surface quality as a consequence of vibration21. It is worth mentioning that the statistical analysis reveals that the interaction between feed rate and depth of incision accounts for 6.78% of the variance observed in the closeness coefficient. This emphasizes the significance of taking into account the cumulative impact of these variables. The model maintains its significance under the T4 condition, accounting for 72.23% of the overall variation. Significant factors that contribute 13.84% and 11.17%, respectively, to the closeness coefficient are depth of cut and spindle speed. A prior investigation concerning the machining of magnesium alloy demonstrated that the depth of cut is an inconsequential factor when d = 0.3, 0.4, and 0.5 mm in the pursuit of optimizing surface roughness. Additionally, the study validated the importance of feed rate22. It is deemed invalid when d ranges from 0.5 to 1.5 mm and when multiple responses are being optimized.

Likewise, the model maintains its significance in the T6 condition, accounting for 72.87% of the overall variation. Spindle speed and depth of cut remain influential factors, contributing 11.40% and 8.61% to the closeness coefficient, respectively. While the statistical significance of individual interaction effects is low, the combined effect underscores the potential influence that these effects may have on heat-treated alloys T4 and T6. The interaction between feed rate and depth of cut has a significant impact on the Ci under all test conditions; the Ci values for as-cast, T4, and T6 alloys are 25.60%, 31.46%, and 26.70%, respectively. In contrast to spindle speed, it verifies the effect of feed rate and depth of cut on quality-critical characteristics. On end milling, however, the as-cast AZ80 exhibits a distinct behavior from the alloy that has been heat-treated. The influence of spindle speed is substantial in the case of T4 and T6 alloy, but this is not the case with as-cast alloy.

The study examined dry milling of AZ80 under various conditions, including as-cast, T4, and T6 heat treatment. To optimize multiple responses, the study utilized the TOPSIS method and conducted experiments based on the RSM-Box-Behnken method. Optimizing multi-criteria issues is facilitated by the hybrid analysis (RSM – TOPSIS – ANOVA).

The experimental findings indicate that the strength of the alloy influences the surface integrity of the material, while the cutting force needed to manufacture the material is determined by the grain size. Maximum spindle speed and depth of cut are undesirable under all test conditions when the feed rate remains constant.

The following parameters must be met in order to achieve the desired surface quality while minimizing cutting force and maximizing MRR: S = 1000 rpm, f = 150 mm/min, d = 1.5 mm for as-cast; S = 1000 rpm, f = 100 mm/min, d = 1.0 mm for T4 and T6.

The interaction between depth of cut and feed rate has a significant impact on critical-to-quality characteristics of the alloy, according to the ANOVA results. Under the provided experimental conditions, the impact of cutting speed was observed to be comparatively insignificant in this particular condition.

The experimental investigation of dry milling of AZ80 Mg alloy revealed that under comparable milling conditions, the as-cast and heat-treated (T4 and T6) specimens exhibit distinct properties. Despite having a similar alloy composition, it verifies that the heat-treated alloy cannot be processed using the optimal process parameters of the as-cast alloy. However, the method of specimen preparation has no bearing on the material removal rate, which is a process parameter-specific characteristic.

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

Depth of cut (mm)

Design of experiments

Feed rate (mm/min)

Field emission scanning electron microscopy

Resultant force (N)

Hexagonal close-packed crystal structure

Material removal rate (gm/min)

Arithmetic average surface roughness (µm)

Response surface methodology

Maximum height of surface roughness (µm)

Spindle speed (rpm)

Arithmetic mean height (3D surface roughness) (µm)

Maximum height of 3D Surface Roughness (µm)

ASM heat treatment process-solutionizing

ASM heat treatment process-solutionizing + age hardening

Technique for order preference by similarity to ideal solution

Wu, Y. J., Zhang, B. H., Yang, Y. B. & Zhang, Z. M. Effects of homogenizing and extrusion on elongation of As-cast AZ80 magnesium alloy. Adv. Mater. Res. 626, 386–390. https://doi.org/10.4028/www.scientific.net/AMR.626.386 (2012).

Article CAS Google Scholar

Zheng, B. et al. High strength, nano-structured Mg–Al–Zn alloy. Mater. Sci. Eng. A 528(4–5), 2180–2191. https://doi.org/10.1016/j.msea.2010.11.073 (2011).

Article CAS Google Scholar

Guo, Z., Hou, H., Zhao, Y. & Qu, S. Optimization of AZ80 magnesium alloy squeeze cast process parameters using morphological matrix. Trans. Nonferrous Met. Soc. China 22(2), 411–418. https://doi.org/10.1016/S1003-6326(11)61192-8 (2012).

Article CAS Google Scholar

Liao, Q. et al. The evolution of microstructure and texture of AZ80 and AZ80-LaMM magnesium alloy wheel hubs processed by single-pass radial forging. J. Mater. Res. Technol. 22, 2473–2488. https://doi.org/10.1016/j.jmrt.2022.12.105 (2023).

Article CAS Google Scholar

Song, Y., Dai, J. & Sun, S. A comparative study on the corrosion behavior of AZ80 and EW75 Mg alloys in industrial atmospheric environment. Mater. Today Commun. 38, 108263. https://doi.org/10.1016/j.mtcomm.2024.108263 (2024).

Article CAS Google Scholar

Deng, P. H., Li, T. C. & Cai, G. Y. Effects of solution and ageing treatment process on the microstructure and properties of AZ80 magnesium alloy. Adv. Mater. Res. 239–242, 238–242. https://doi.org/10.4028/www.scientific.net/AMR.239-242.238 (2011).

Article CAS Google Scholar

Marakini, V., Pai, S. P. & D’Mello, G. Effect of liquid nitrogen assisted milling on AZ91 magnesium alloy. Results Eng. 22, 102207. https://doi.org/10.1016/j.rineng.2024.102207 (2024).

Article CAS Google Scholar

Dhinakarraj, C. K., Senthilkumar, N., Palanikumar, K. & Deepanraj, B. Machinability evaluation of magnesium composite using response surface methodology and nature-inspired metaheuristic algorithms. Int. J. Interact. Des. Manufact. https://doi.org/10.1007/s12008-024-01866-3 (2024).

Article Google Scholar

Joseph Fernandus, M., Senthilkumar, T., Balasubramanian, V. & Rajakumar, S. Optimizing diffusion bonding parameters in AA6061-T6 aluminum and AZ80 magnesium alloy dissimilar joints. J. Mater. Eng. Perform. 21(11), 2303–2315. https://doi.org/10.1007/s11665-012-0190-7 (2012).

Article CAS Google Scholar

Todkar, U. et al. Investigating the impact of heat treatment on the tribological behaviour of AZ80 magnesium alloy at high temperatures. Results Eng. 21, 101661. https://doi.org/10.1016/j.rineng.2023.101661 (2024).

Article CAS Google Scholar

Reddy, U. et al. Effect of surface roughness induced by milling operation on the corrosion behavior of magnesium alloys. J. Mater. Eng. Perform. 30(10), 7354–7364. https://doi.org/10.1007/s11665-021-05933-8 (2021).

Article CAS Google Scholar

Matin, A., Saniee, F. F. & Abedi, H. R. Microstructure and mechanical properties of Mg/SiC and AZ80/SiC nano-composites fabricated through stir casting method. Mater. Sci. Eng. A 625, 81–88. https://doi.org/10.1016/j.msea.2014.11.050 (2015).

Article CAS Google Scholar

Khdair, A. I. & Melaibari, A. A. Experimental evaluation of cut quality and temperature field in fiber laser cutting of AZ31B magnesium alloy using response surface methodology. Opt. Fiber Technol. 77, 103290. https://doi.org/10.1016/j.yofte.2023.103290 (2023).

Article CAS Google Scholar

Pargavi, S. B. et al. Mechanical, electrochemical corrosion, and high-temperature oxidation properties of stir-cast and heat-treated AZ80 magnesium alloy. Mater. Today Commun. 38, 108181. https://doi.org/10.1016/j.mtcomm.2024.108181 (2024).

Article CAS Google Scholar

Perron, A., Politano, O. & Vignal, V. Grain size, stress and surface roughness. Surf. Interface Anal. 40(3–4), 518–521. https://doi.org/10.1002/sia.2849 (2008).

Article CAS Google Scholar

Derlet, P. & Swygenhoven, H. V. The role played by two parallel free surfaces in the deformation mechanism of nanocrystalline metals: A molecular dynamics simulation. Philos. Mag. A 82(1), 1–15 (2002).

Article ADS CAS Google Scholar

Mohammadi Zerankeshi, M., Alizadeh, R., Gerashi, E., Asadollahi, M. & Langdon, T. G. Effects of heat treatment on the corrosion behavior and mechanical properties of biodegradable Mg alloys. J. Magnes. Alloys 10(7), 1737–1785. https://doi.org/10.1016/j.jma.2022.04.010 (2022).

Article CAS Google Scholar

Somasundaram, M., NarendraKumar, U., Annamalai, A. R. & Muthuchamy, A. High-temperature tribological performance of stir-cast and heat-treated EV31A magnesium alloy: Experiments and predictions. Heliyon https://doi.org/10.1016/j.heliyon.2023.e19055 (2023).

Article PubMed PubMed Central Google Scholar

Varatharajulu, M., Duraiselvam, M., Arun Kumar, K., Gabrial Kanniyan, C. & Sathiyamurthy, R. Experimental investigation of the effect of independent parameters in the face milling of aluminum 6082 alloy. Trans. Indian Inst. Met. 74(3), 659–677. https://doi.org/10.1007/s12666-020-02161-x (2021).

Article CAS Google Scholar

Varatharajulu, M., Duraiselvam, M., Krishna Pradeep, G. V. & Jagadeesh, B. Tool temperature thermographic study on end milling magnesium AZ31 using carbide tool. Mater. Chem. Phys. 295, 127077. https://doi.org/10.1016/j.matchemphys.2022.127077 (2023).

Article CAS Google Scholar

Jouini, N., Ruslan, M. S. M., Ghani, J. A. & Che Haron, C. H. Sustainable high-speed milling of magnesium alloy AZ91D in dry and cryogenic conditions. Sustainability 15(4), 3760. https://doi.org/10.3390/su15043760 (2023).

Article CAS Google Scholar

Pradeepkumar, M., Venkatesan, R. & Kaviarasan, V. Evaluation of the surface integrity in the milling of a magnesium alloy using an artificial neural network and a genetic algorithm. Mater. Tehnol. 52(3), 367–373. https://doi.org/10.17222/mit.2017.198 (2018).

Article CAS Google Scholar

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School of Mechanical Engineering, Vellore Institute of Technology, Vellore, TamilNadu, 632014, India

M. Tamil Selvan & M. Somasundaram

Centre for Innovative Manufacturing Research, Vellore Institute of Technology, Vellore, TamilNadu, 632014, India

A. Raja Annamalai

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TS : Data curation, Formal analysis, Writing—original draft. MS: Investigation, Writing—Writing—original draft, review and editing. A.R.A: Conceptualization, Investigation, Formal analysis, Methodology, Writing—original draft, Writing—review and editing, Supervision.

Correspondence to A. Raja Annamalai.

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Tamil Selvan, M., Somasundaram, M. & Raja Annamalai, A. Optimizing dry milling of stir-cast and heat-treated AZ80 magnesium alloy using multiple criteria optimization technique: an experimental study. Sci Rep 14, 25801 (2024). https://doi.org/10.1038/s41598-024-77174-3

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Received: 09 April 2024

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DOI: https://doi.org/10.1038/s41598-024-77174-3

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