Multi-response optimization of CuZn39Pb3 brass alloy turning by implementing Grey Wolf algorithm

Fountas, Nikolaos A.; Koutsomichalis, Angelos; Kechagias, John; Vaxevanidis, Nikolaos M.

doi:10.3221/igf-esis.50.49

Cited by 11 publications

(5 citation statements)

References 23 publications

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“…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].…”

Section: Introductionmentioning

confidence: 99%

Multiparameter signal-to-noise ratio optimization for end milling cutting conditions of aluminium alloy 5083

Kechagias

2024

Int J Adv Manuf Technol

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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.

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Section: Introductionmentioning

confidence: 99%

Multiparameter signal-to-noise ratio optimization for end milling cutting conditions of aluminium alloy 5083

Kechagias

2024

Int J Adv Manuf Technol

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“…Therefore, an in-depth analysis of the technology itself, together with the cutting mechanism, is crucial in order to obtain utilizable results useful for the machining industry. As a result of this need, the focus of the mainstream studies is the comprehensive analysis of the process parameters, either via statistical methods or neural networks [5,6]. In this study, a detailed design of the experiment is the basis of the statistical-based analysis of the surface topography data obtained.…”

Section: Introductionmentioning

confidence: 99%

Influence of Cutting Parameters and Tool Geometry on Topography of Hard Turned Surfaces

Molnár

2023

Machines

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Three-dimensional topography measurement is a focus area of precision machining because it provides detailed information on the functional behavior of contacting surfaces. The purpose of this study is to analyze how the process parameters and the edge geometry of the cutting insert influence the topographical characteristics of hardened 16MnCr5 steel surfaces and to determine the favorable combinations of technology parameter values based on the analyzed topography parameters (Ssk, Sku, Sz, Sa, Sk, Spk, Svk). A research question is whether the analyzed parameters can be substituted by each other or not. A hard turning experiment was carried out based on a full factorial design of experiments, and the areas of the machined surfaces were scanned and analyzed by analysis of variance, regression analysis (response function), and correlation analysis. It was found that there are strong correlations between the topography parameters that allow the substitution of one with another; the main influencing cutting parameter is the feed rate for both insert types. The contributions of the study are the numerical information with regard to the correlations among the topography parameters, as well as the extent of the influence of cutting parameters.

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“…Conventional optimization methods based on the gradient may not obtain the optimal parameters with the accuracy and precision of system models [24]. Numerous metaheuristic optimization algorithms have become increasingly prevalent to obtain optimal control parameters over the past two decades [25]. The metaheuristic algorithms are simple.…”

Section: Introductionmentioning

confidence: 99%

Parallel Multi-Layer Monte Carlo Optimization Algorithm for Doubly Fed Induction Generator Controller Parameters Optimization

Tao,

Mo,

Qin

et al. 2023

Energies

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This work proposes a parallel multi-layer Monte Carlo optimization algorithm (PMMCOA) that optimizes proportional–integral parameters for a doubly fed induction generator-based wind turbine controller. The PMMCOA, an improved form of the Monte Carlo algorithm, realizes the optimization process via a parallel multi-layer structure. The PMMCOA includes rough search layers, precise search layers, and re-precise search layers. Each layer of the PMMCOA adopts a multi-region and multi-granularity approach to increase the diversity and randomness of the search samples. The PMMCOA is employed to tune the controller parameters for achieving maximum power point tracking and improving generation efficiency. The controller fitness function reflects the sum of the rotor angular velocity error and the reactive power error. Compared with the five metaheuristic algorithms, the PMMCOA has a higher global convergence and more accurate power tracking ability.

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Multi-response optimization of CuZn39Pb3 brass alloy turning by implementing Grey Wolf algorithm

Cited by 11 publications

References 23 publications

Multiparameter signal-to-noise ratio optimization for end milling cutting conditions of aluminium alloy 5083

Multiparameter signal-to-noise ratio optimization for end milling cutting conditions of aluminium alloy 5083

Influence of Cutting Parameters and Tool Geometry on Topography of Hard Turned Surfaces

Parallel Multi-Layer Monte Carlo Optimization Algorithm for Doubly Fed Induction Generator Controller Parameters Optimization

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