Abstract
Surface integrity problems during selective material removal processes are a very common limitation for process productivity and part quality, especially in difficult-to-machine materials like 5083 aluminium alloy (AA), which is known for its remarkable performance in extreme environments. In general, tuning the cutting-part material properties with cutter geometry and cutting parameters can optimize surface texture, increase parts accuracy and resistance in corrosion, and eliminate process noise and energy waste. This work is an experimental study of surface parameter optimization during finish end milling of an AA5083 under a specific range of three cutting parameters with an optimized two-flute carbide cutter by previous work. So, twenty-seven experiments were run having varied the radial depth of cut (RDOC), feed rate (f), and cutting speed (S). Surface roughness parameters (Ra and Rt) were measured in the direction of cutting speed at three different distances by the upper edge. The signal-to-noise (SN) ratios have been calculated, and the process was optimized following the analysis of means. Then, additive models with linear interactions were fitted on SN ratios, and the analysis of variances and residual normality plots were utilized to validate the models’ goodness. The SN approach and analysis of means conclude that 0.5 mm RDOC, 6000 rpm speed, and 0.082 mm/tooth feed optimize the process and can effectively predict the Ra and Rt responses. The newly produced machinability data can benefit further applications of AA5083 in industrial applications such as shipbuilding and vehicle bodies.
Similar content being viewed by others
Avoid common mistakes on your manuscript.
1 Introduction
Thanks to their excellent corrosion resistance, weight ratios, and mechanical properties, aluminium alloys (AA) have been extensively utilized in the naval [1], aviation [2], and automotive industries [3, 4]. Specifically, the AA5083 is known for its resistance to seawater corrosion [5, 6] and tribological properties and is used broadly by the shipbuilding industry [7]. Therefore, the processing of AA5083 is of great interest in manufacturing industries, and it is processed by many cutting and diffusing–joining processes [8,9,10], for example, milling [11], turning [12], and friction stir welding [13, 14].
CNC end milling is a highly customizable material processing approach that involves rotating flute cutters to shape a workpiece until the final desired shape is achieved [15, 16]. Optimization problems in the cutting processes, such as surface finish, smoothness and flatness, are very common, especially for difficult-to-cut materials such as aluminium [17], copper [18], and titanium alloys [19,20,21], to name a few [22, 23]. Additionally, dimensional accuracy [24], wear, and tribological properties are affected by machining conditions the most, as has been observed in the literature [25, 26]. Commonly, finishing machining material removal rates are determined by surface roughness performance, which is associated with cutting build-up edge formation and chip characteristics, and, in finishing conditions, are more critical than cutting force and tool wear [27,28,29].
Therefore, even if the cutting theory has developed predictive models for static and dynamic tools [30, 31], for surface roughness parameters, when we have to deal with difficult-to-cut materials, it is prevalent to produce experimental data for specific cutters and materials varying the basic cutting parameters, i.e. the cutting speed, feed rate, and depth of cut. Debnath et al. [11] investigated five processing parameters (cutting speed, feed rate, depth of cut, tool nose shape, and number of flutes) during end milling (50 mm length slots, 8 mm cutter diameter, carbide tools, dry condition) of Al 5083-H112 and utilized the concept of desirability function analysis to determine the optimum combination of parameter values. They observed that cutting speed and feed rate contributed the most to Ra values and that increasing cutting speed or decreasing feed rate or depth of cut resulted in better Ra values. In Kalita et al. [32], a high-speed steel cutter was utilized to end mill Al1100 specimens varying cutting speed, feed rate, and depth of cut and proved that the Ra values decreased with the decrease of feed rate, cutting speed, and depth of cut.
Moreover, in the literature, the geometric cutting parameters of a two-flute cutter were optimized, varying the cutting depth, speed and feed [17, 33]. The 50% core diameter, 38° flute angle, 22° rake angle, 22° first relief angle, and 30° second relief angle were optimized for the cutting efficiency concerning the average and max depth of surface texture (Ra and Rt). Note here that optimizing the cutter geometry is of outstanding importance as it defines the cutting conditions and further the cutting forces, tool wear, cutting accuracy, process removal rates, and, consequentially, the final parts’ production cost and process planning [34].
The current work is an optimization study of cutting conditions during dry end milling of AA5083, where the optimized geometry of the two-flute cutter is manufactured by a grinding 5-axis machine, as described in detail in Kechagias et al. [17], and then the radial cutting depth, feed per tooth, and speed were tested in different dry cutting conditions. Note here that, in the literature, the majority of the research works utilized cutters from the market and not laboratory-made [35,36,37,38,39]. Therefore, the cutter geometry details were designed and optimized for the specified AA5083 alloy.
Further, in this work, the newly extracted machinability data has been analyzed by utilizing the full factorial design experimental approach, which means that all combinations of the used parameters and levels appeared in the experimental table, and three measurements have been performed in each of the twenty-seven experiments for higher experimental power [40,41,42]. The signal-to-noise ratio has been used as an objective function for the cutting condition optimization process according to the surface texture mean and max depth roughness (Ra and Rt). The theoretical basis of using the SN ratio as a performance measure to optimize the surface texture of the machined surface has to do with cutter vibrations, which affect the surface roughness in these three measurement lines in different ways and cause significant variations in Ra and Rt measurements that make optimization crucial to eliminate surface defects during the machining process [43, 44]. The proposed approach can be utilized by the manufacturing industry for better AA5083 product surface quality, corrosion performance and process planning.
2 Materials and methods
The two-flute end mill cutter (8 mm in diameter) was manufactured homemade (Hawemat 5-axis machine) as described in previous work with the optimized geometry (50% core diameter, 38° flute angle, 22° rake angle, 22° first relief angle, and 30° second relief angle) [17]. The tool geometry details can be seen in Fig. 1a. The cutting parameters are all for finish end milling conditions. As indicated in the ShopMill panel, the consuming cutting power was lower than 5% of the machine’s max power (13 kW and 14,000 rpm) in the experimental area and was not investigated. According to the finishing conditions and preliminary experimental work, the radial depth of the side cutting surfaces was decided between 0.5 and 1.5 mm, the cutting feed between 0.055 and 0.11 mm/tooth, and the cutting speed between 5000 and 7000 rpm [17]. The DMU 50 Ecoline milling 5-axis machine centre is utilized for all cuts.
The feedstock was a plate of 150 mm length × 80 mm width × 50 mm depth of AA5083 (see Fig. 1b). The composition, physical properties, and mechanical properties of a typical 5083 aluminium alloy can be seen in [33]. The G–code for the twenty-seven different cuts was prepared as a text for this experiment and fed on a 5-axis machine control unit (ShopMill). After each cut, the carbide tool geometry was inspected for wear, and no wear or build-up edges were observed for all the twenty-seven experiments, as can be seen in Fig. 1c. The cutting surfaces were 10 mm in length and 5 mm in depth, as depicted in Fig. 1d.
The tool was tangentially imported and exported on the 10 mm length cutting surface twenty-seven times, varying the cutting radial depth of cut (RDOF, mm), feed (f, mm/tooth), and speed (S, rpm) as described in Table 1. The machined surfaces are tabulated in Fig. 2. The width of each cutting surface was 5 mm (vertical distance between the upper part edge and tool cutting edge). After machining, all cutting surfaces were measured in three different positions, as shown in Fig. 2, by a nose surface tester (4.8 mm sampling length). Therefore, three absolute arithmetic average surface roughness values (Ra1, Ra2, and Ra3; µm) and three highest distances between the maximum peak and lowest valley (Rt1, Rt2 and Rt3; µm) were measured. Last but not least, two more columns are added to Table 1 to demonstrate the signal-to-noise ratio known as SN ratio, which, in the case of Ra and Rt values (positive higher than zero values; the smaller, the better case), is calculated by Eq. 1 [45].
where the SN ratio is the η (here SNRa and SNRt), n (here three times), and the number of trials measured in the performance measure y (here Ra and Rt).
The twenty-seven experiments in Table 1 were performed in three groups according to RDOC levels (0.5, 1, and 1.5 mm). In each group, a random sequence was followed to eliminate other latent factors affecting surface roughness.
3 Results and discussion
In Fig. 2, the effect on the surface texture can be observed by varying the three basic cutting parameters (RDOC, f, and S). The variation of the parameters affects the surface texture, and the different conditions are reflected in the machined surfaces for each case. Therefore, parameter optimization is necessary when the final machined parts are for high-accuracy assemblies that operate in challenging high-corrosion environments in the naval industry.
Optimal levels of the Ra and Rt can be observed by SN ratio main effect plots, as shown in Fig. 3. Main effect plots or analysis of means (ANOM) graphically show the mean values of each parameter level inside the experimental area. Therefore, the significance of each parameter’s contribution to variability (delta values) and the optimal parameters’ levels can be thoroughly seen. The 0.5 mm RDOC, 0.082 mm/tooth feed, and 6000 rpm optimize both Ra and Rt SN ratios. Note that experiment number 26 in Table 1 has slightly better SN ratios than 23, but this is due to the mean levels calculated from the values of each parameter within the experimental domain, as explained earlier.
The conclusions from reading the main effect plots in Fig. 3 are analyzed below:
-
Reducing the radial depth of cut minimizes the volume of material removed per unit time, the radial cutting force, and upper and lower limits of cutter vibration, resulting in thinner chips with smaller surface roughness Ra and Rt (improve SN ratios). This is consistent with the literature, and in particular, in AA5083, the results of the radial DOC on Ra and Rt parameters are of greater importance than in [43] for slot milling surfaces of aluminium blocks, where similar results were derived, even if the depth of cut proved to have lower effects on surface roughness in comparison with the spindle speed and feed rate. Moreover, the RDOC effects follow the simulated results extracted in [46] for the ball-end milling process.
-
Cutting feed gradually increases the SN ratios of Ra and Rt, but after the centre level, it rapidly reduces the SN ratios and gives the worst quality at the higher feed. The feed rate increase increases the chip volume, resulting in better quality cutting conditions for the values close to the centre point of 0.082 mm/tooth and worse for the left and right level values. The tribological conditions between the cutter and side surface improved in the centre point of the cutting feed, resulting in fewer vibrations and smoother surfaces. Note that in the orthogonal cutting theory, increasing the cutting feed increases the Rt values (static tool geometry; see [31]), but the theoretical models do not take into account the tribological conditions and vibrations but only the geometrical parameters.
-
Finally, the cutting speed increase has a similar attitude to the feed and optimizes the average roughness Ra for the centre level (6000 rpm) and the worst values for the higher speeds (7000 rpm). The centre levels reduce the vibrations having benefits on surface roughness. Note that, in orthogonal cutting theory, static and dynamic tool geometrical models were extracted (see [30, 47]), proving that the vibrations and machinability conditions (tribological and thermal) need optimization for optimal machining performance [21, 47].
Interaction plots (see Fig. 4) between the RDOC and feed show that all RDOC give almost the same SN ratios for Ra and Rt in 0.082 mm/tooth feed and considerably different for the lower and higher feeds with higher feeds, resulting in worse values for 1.5 mm RDOC. Similar results were observed between RDOC and S with the 1.5 mm RDOC and 7000 rpm, resulting in the worst SNRa and SNRt values. Finally, the f and S interactions show that the SN ratios for Ra and Rt improved smoothly at 6000 rpm for all feed values, while the 0.11 mm/tooth and 7000 rpm resulted in the worst SN ratios.
After the analysis of means and interaction plots, a general linear model with linear interactions cross products was applied to the SNRa and SNRt ratios (see Eq. 2; additive model; [45]).
where l is the SN output different metric (l: Ra, Rt), i and j (i ≠ j) are the variable parameters (i, j: RDOC, f, S), and k is the level value.
In addition, the ANOVA general linear model (GLM) for SNRa and SNRt are presented in Tables 2 and 3, respectively. Radial depth of cut significantly affects the SN ratios of Ra and Rt having F-values higher than 4 (12.08 and 5.48, respectively) and p-values lower than 0.05 (0.004 and 0.032, respectively). When the RDOC increases, the radial cutting forces increase, likewise increasing the vibrations, noise, and surface defects. Feed, again, affects the SNRa ratios significantly, having an F-value higher than 4 (4.76) and p-value close to 0.05 (0.044), with centre values resulting in optimum performance while insignificantly affecting the SNRt (1.53 F-value lower than 2). Speed likewise significantly affects the SNRa (4.13 F-value higher than 4, and 0.059 p-value close to 0.05) and insignificantly the Rt (0.89 F-value lower than 2 and 0.447 p-value higher than 0.05).
The cross interactions have minor effects for SNRa as presented F-values lower than 2 for all cases, with RDOC × f interaction being higher than the RDOC × S and f × S interactions (Fig. 4). Similarly, the same observations have been made for SNRt, showing all interactions to be insignificant. Note here that the orthogonal cutting theory derives analytical models that Ra and Rt values depend mainly on cutting feed and tool geometry and not on the depth of cut [48]. Here, it proved experimentally that the RDOC significantly affects the surface texture during the end milling of an AA5083 difficult-to-cut material, followed by f and then by S.
The SN ratios of Ra and Rt additive models show Rsq (coefficient of determination) values of about 87.21 and 72.77%, but the residuals were tested if they followed the normal distribution, and both showed p-values higher than 0.05 (0.373 and 0.473, respectively), which means that the linear models are adequate for predicting the process performance (see Fig. 5).
Finally, the additive models are used to calculate the mean absolute percentage errors (MAPE) for all eighty-one Ra and Rt measurements, as shown in Table 4. The MAPE values for all measurements are about 15% and 23% showing good prediction performance.
Here, note that the extracted additive models cannot predict intermediate values of predictors parameters (RDOC, f, and S), and therefore, interpolation should be utilized for these cases.
4 Conclusions
The current study optimizes the cutting condition of a two-flute cutter during dry end milling of a difficult-to-cut aluminium alloy known as AA5083. The experimental data showed that the radial depth of cut (RDOC) is the dominant predictor parameter as it defines the chip removal rate, chip cross-section area and shape, and the radial cutting force. Its increase increases noise forces and vibration limits, resulting in surface defects and lower surface quality. Feed rate likewise has moderate effects on noise, and its increase higher than 0.082 mm/tooth decreases the quality (lower Ra and Rt SN ratios) in all ranges of RDOC (0.5–1–1.5 mm) and S (5000–6000–7000 rpm) according to MEP plots (Fig. 3). Note here that for the specified 0.5 mm RDOC and 6000 rpm S, the 0.082 mm/tooth feed shows slightly lower Ra and Rt SN ratios than the 0.11 mm feed (see Table 1), which means that for 0.5 mm radial depth-of-cut and 6000 rpm speed the cutting conditions become slightly better for the 0.11 mm/tooth feed. Moreover, cutting speed moderately affects the surface performance parameters Ra and Rt, and its increase to the centre point increases the quality softly and then decreases it sharply.
Last, according to the three different measurements on each of the twenty-seven machined surfaces and the SN ratio approach (SN ratio as an objective function), additive models are adequate to predict the surface quality performance according to the residual analysis and the achieved MAPE values. Intermediate values of the predictors can be observed using linear interpolation. Further investigation to identify a more sophisticated predictive model, such as suggested in [49,50,51], incorporating more parameters and utilizing dynamic models (higher order or NN models) and optimization machine learning algorithms are suggested as future work.
Data availability
All data generated or analyzed during this study are included in the manuscript.
Code availability
Not applicable.
References
Wahid MA, Siddiquee AN, Khan ZA (2020) Aluminum alloys in marine construction: characteristics, application, and problems from a fabrication viewpoint. Mar Syst 15:70–80. https://doi.org/10.1007/s40868-019-00069-w
Li S, Yue X, Li Q et al (2023) Development and applications of aluminum alloys for aerospace industry. J Mater Res Technol 27:944–983. https://doi.org/10.1016/j.jmrt.2023.09.274
Samtaş G (2015) Optimisation of cutting parameters during the face milling of AA5083-H111 with coated and uncoated inserts using Taguchi method. Int J Mach Mach Mater 17:211. https://doi.org/10.1504/IJMMM.2015.071993
Ruban SR, Dev Wins KL, Raja Selvam JD, Rai S, R, (2023) Influence of turning parameters on the machinability of Al6061/ZrB2 & ZrC hybrid in-situ Aluminium Matrix Composite. Aust J Mech Eng 21:1218–1229. https://doi.org/10.1080/14484846.2021.1963081
Alghannam AA, Soliman MS, Seikh AH et al (2023) Investigation on mechanical properties and corrosion resistance of Ti-modified AA5083 aluminum alloy for aerospace and automotive applications. Sci Rep 13:11535. https://doi.org/10.1038/s41598-023-38510-1
Saravanakumar R, Rajasekaran T, Pandey C (2023) Underwater friction stir welded armour grade AA5083 aluminum alloys: experimental ballistic performance and corrosion investigation. J Mat Eng Perform 32:10175–10190. https://doi.org/10.1007/s11665-023-07836-2
Iyappan SK, Ghosh A (2015) Comparison of tribological properties of MoS2 and graphite-PTFE coatings and its impact on machining of aluminium by HSS end mills. Mater Manuf Process 30:912–920. https://doi.org/10.1080/10426914.2014.984212
Lathashankar B, Tejaswini GC, Suresh R, Swamy NHS (2022) Advancements in diffusion bonding of aluminium and its alloys: a comprehensive review of similar and dissimilar joints. Adv Mater Process 8:4659–4677. https://doi.org/10.1080/2374068X.2022.2079274
Xie S, He Z, Wang C et al (2023) A generic evolutionary ensemble learning framework for surface roughness prediction in manufacturing. Int J Comput Integr Manuf 1–23. https://doi.org/10.1080/0951192X.2023.2204486
Anwar J, Khan M, Farooq MU et al (2023) Effect of B4C and CNTs’ nanoparticle reinforcement on the mechanical and corrosion properties in rolled Al 5083 friction stir welds. Can Metall Q 62:1–10. https://doi.org/10.1080/00084433.2022.2054586
Debnath A, Kar S, Mandal UK, Dey V (2023) Experimental investigation and multi-response optimisation of end milling in aluminium-5083 alloy using desirability analysis. Int J Manuf Res 18:284–310. https://doi.org/10.1504/IJMR.2023.132834
Palanikumar K, Karthikeyan R (2006) Optimal machining conditions for turning of particulate metal matrix composites using Taguchi and response surfeace methodologies. Mach Sci Technol 10:417–433. https://doi.org/10.1080/10910340600996068
Behnagh RA, Besharati Givi MK, Akbari M (2012) Mechanical properties, corrosion resistance, and microstructural changes during friction stir processing of 5083 aluminum rolled plates. Mater Manuf Process 27:636–640. https://doi.org/10.1080/10426914.2011.593243
Aybar K, Çakir FH (2023) An experimental study of the friction stir welding of Al 5083 H321 plates by using different process parameters. Can Metall Q 1–13. https://doi.org/10.1080/00084433.2023.2212932
Kumar Ghadai R, Chakraborty S, Kalita K (2023) On solving parametric optimization problem of an end milling process for machining of Al 1070 using MCDM techniques: a comparative analysis. Adv Mater Process 1–23. https://doi.org/10.1080/2374068X.2023.2216398
Dillibabu SP, Vasudevan B, Megaraj M et al (2023) Aluminum and its alloys in automotive and aerospace applications review. p 020027
Kechagias JD, Ziogas CK, Pappas MK, Ntziatzias IE (2011) Parameter optimization during finish end milling of Al alloy 5083 using robust design. In: Proc. World Cong. Engineering 2011, WCE 2011
Fountas N, Koutsomichalis A, Kechagias JD, Vaxevanidis NM (2019) Multi-response optimization of CuZn39Pb3 brass alloy turning by implementing Grey Wolf algorithm. Frat Integrita Strutt 13. https://doi.org/10.3221/IGF-ESIS.50.49
Chakradhar B, Singaravel B, Ugrasen G, Kiran Kumar A (2023) Prediction of cutting forces using MRA, GMDH and ANN techniques in micro end milling of titanium alloy. Mater Today: Proc 72:1943–1949. https://doi.org/10.1016/j.matpr.2022.10.209
Kechagias J, Tsiolikas A, Asteris P, Vaxevanidis N (2018) Optimizing ANN performance using DOE: application on turning of a titanium alloy. MATEC Web Conf 178:01017. https://doi.org/10.1051/matecconf/201817801017
Ahmad A, Akram S, Jaffery SHI, Khan MA (2023) Evaluation of specific cutting energy, tool wear, and surface roughness in dry turning of titanium grade 3 alloy. Int J Adv Manuf Technol 127:1263–1274. https://doi.org/10.1007/s00170-023-11580-1
Kaushik N, Meena A, Mali HS (2023) Flat-end mill machining analysis of processed CrMnFeCoNi high-entropy alloys. Mater Manuf Process 38:755–769. https://doi.org/10.1080/10426914.2022.2149785
Wang C, Ding P, Huang X, Li H (2023) A method for predicting ball-end cutter milling force and its probabilistic characteristics. Mech Based Des Struct Mach 51:3416–3433. https://doi.org/10.1080/15397734.2021.1927752
Soori M, Arezoo B (2023) Dimensional, geometrical, thermal and tool deflection errors compensation in 5-Axis CNC milling operations. Aust J Mech Eng 1–15. https://doi.org/10.1080/14484846.2023.2195149
Kannan C, Ramanujam R, Balan ASS (2018) Machinability studies on Al 7075/BN/Al2O3 squeeze cast hybrid nanocomposite under different machining environments. Mater Manuf Process 33:587–595. https://doi.org/10.1080/10426914.2017.1401718
Sivaprakasam P, Udaya Prakash J, Hariharan P (2022) Enhancement of material removal rate in magnetic field-assisted micro electric discharge machining of Aluminium Matrix composites. Int J Ambient Energy 43:584–589. https://doi.org/10.1080/01430750.2019.1653979
Pinar AM, Filiz S, Ünlü BS (2016) A comparison of cooling methods in the pocket milling of AA5083-H36 alloy via Taguchi method. Int J Adv Manuf Technol 83:1431–1440. https://doi.org/10.1007/s00170-015-7666-1
Iyappan SK, Ghosh A (2020) Small quantity lubrication assisted end milling of aluminium using sunflower oil. Int J Precis Eng Manuf - Green Technol 7:337–345. https://doi.org/10.1007/s40684-019-00081-w
Shijin M, Nazar KPA (2020) Multi Objective Parameter Optimization of End Milling Operation on AA5083. In: Int. Conf. Power Electronics and Renewable Energy Appl (PEREA). IEEE, pp 1–6
Skelton RC (1969) Surface finish produced by a vibrating tool during turning. Int J Mach Tool Des Res 9:375–389. https://doi.org/10.1016/0020-7357(69)90021-3
Merchant ME (1945) Mechanics of the metal cutting process. II. Plasticity conditions in Orthogonal cutting. J Appl Phys 16:318–324. https://doi.org/10.1063/1.1707596
Kalita K, Madhu S, Ramachandran M et al (2023) Experimental investigation and parametric optimization of a milling process using multi-criteria decision making methods: a comparative analysis. Int J Interact Des Manuf 17:453–467. https://doi.org/10.1007/s12008-022-00973-3
Karagiannis S, Stavropoulos P, Ziogas C, Kechagias J (2014) Prediction of surface roughness magnitude in computer numerical controlled end milling processes using neural networks, by considering a set of influence parameters: an aluminium alloy 5083 case study. Proc Inst Mech Eng B J Eng Manuf 228. https://doi.org/10.1177/0954405413498582
Chryssolouris G (2013) Manufacturing systems: theory and practice. Springer Science & Business Media
Adin MŞ (2023) Machining aerospace aluminium alloy with cryo-treated and untreated HSS cutting tools. Adv Mater Process Technol 1–26. https://doi.org/10.1080/2374068X.2023.2273035
Ates E, Evis Z, Ozturk F (2023) Machining parameters and distortion analyses in end milling of AA2050 and AA7050. Int J Adv Manuf Technol 128:2915–2929. https://doi.org/10.1007/s00170-023-12062-0
Pimenov DY, Kiran M, Khanna N et al (2023) Review of improvement of machinability and surface integrity in machining on aluminum alloys. Int J Adv Manuf Technol 129:4743–4779. https://doi.org/10.1007/s00170-023-12630-4
Dev A, Tandon S, Kumar P, Dutt A (2020) Effect of coating and polishing of cutting tool on machined surface quality in dry machining of aluminium alloy. Def Sci J 70:299–305. https://doi.org/10.14429/dsj.70.14831
López de Lacalle LN, Lamikiz A, Sánchez JA, Cabanes I (2001) Cutting conditions and tool optimization in the high-speed milling of aluminium alloys. Proc Inst Mech Eng B J Eng Manuf 215:1257–1269. https://doi.org/10.1243/0954405011519312
Kechagias JD (2023) 3D printing parametric optimization using the power of Taguchi design: an expository paradigm. Mater Manuf Process 1–7. https://doi.org/10.1080/10426914.2023.2290258
Montgomery DC, Lynch C (2023) Optimal experimental designs for hypothesis testing with multiple factors: maximizing power for the biological sciences. Int J Exp Des Proc Opt 1. https://doi.org/10.1504/IJEDPO.2023.10061657
Montgomery DC (2012) Design and analysis of experiments, eighth ed. Wiley, Hoboken, NJ, USA
Zhang JZ, Chen JC, Kirby ED (2007) Surface roughness optimization in an end-milling operation using the Taguchi design method. J Mater Process Technol 184:233–239. https://doi.org/10.1016/j.jmatprotec.2006.11.029
Ulutan D, Ozel T (2011) Machining induced surface integrity in titanium and nickel alloys: a review. Int J Mach Tools Manuf 51:250–280. https://doi.org/10.1016/j.ijmachtools.2010.11.003
Phadke MS (1989) Quality engineering using robust design, vol 07632. Prentice Hall PTR, Englewood Cliffs, New Jersey
Buj-Corral I, Vivancos-Calvet J, Domínguez-Fernández A (2012) Surface topography in ball-end milling processes as a function of feed per tooth and radial depth of cut. Int J Mach Tools Manuf 53:151–159. https://doi.org/10.1016/j.ijmachtools.2011.10.006
Kilic ZM, Altintas Y (2016) Generalized mechanics and dynamics of metal cutting operations for unified simulations. Int J Mach Tools Manuf 104:1–13. https://doi.org/10.1016/j.ijmachtools.2016.01.006
Huda Z (2020) Machining Processes and Machines. CRC Press, First edition. | Boca Raton : CRC Press
Wu J, Yu G, Gao Y, Wang L (2018) Mechatronics modeling and vibration analysis of a 2-DOF parallel manipulator in a 5-DOF hybrid machine tool. Mech Mach Theory 121:430–445. https://doi.org/10.1016/j.mechmachtheory.2017.10.023
Wu J, Wang J, Wang L, Li T (2009) Dynamic formulation of redundant and nonredundant parallel manipulators for dynamic parameter identification. Mechatronics 19:586–590. https://doi.org/10.1016/j.mechatronics.2009.01.003
Wu J, Wang X, Zhang B, Huang T (2021) Multi-objective optimal design of a novel 6-DOF spray-painting robot. Robotica 39:2268–2282. https://doi.org/10.1017/S026357472100031X
Funding
Open access funding provided by HEAL-Link Greece. Open access funding provided by HEAL-Link Greece.
Author information
Authors and Affiliations
Contributions
JK: methodology, conceptualization, resources, data curation, data analysis, software, visualization writing (draft and editing), writing (review and editing), supervision.
Corresponding author
Ethics declarations
Ethics approval
Compliance with ethical standards (no human participants or animals involved).
Consent to participate
Not applicable.
Consent for publication
The Author has read and agreed to the published version of the paper.
Competing interests
The author declares no competing interests.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Kechagias, J. Multiparameter signal-to-noise ratio optimization for end milling cutting conditions of aluminium alloy 5083. Int J Adv Manuf Technol 132, 4979–4988 (2024). https://doi.org/10.1007/s00170-024-13667-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-024-13667-9