Experimental research and ANN modeling on the impact of the ball burnishing process on the mechanical properties of 5083 Al-Mg material

Başak, Hüdayim; Özkan, Murat Tolga; Toktaş, İhsan

doi:10.4149/km_2019_1_61

Cited by 14 publications

(15 citation statements)

References 26 publications

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“…If this condition (module 4) is met, the system checks to which of the seven stages of the overlap process belongs the entered value of the axial pitch S oi (modules 6,8,10,12,14,16,18). When one of these conditions is met, the overlap area of the grooves is calculated according to the formulas from the article, the numbering of which is given in parentheses (modules 7,9,11,13,15,17,19).…”

Section: Resultsmentioning

confidence: 99%

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Determining Optimal Parameters of Regular Microrelief Formed on the End Surfaces of Rotary Bodies

2021

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The analytical dependences for determining the overlap area of V-shaped grooves of partially regular microrelief shifted by an angular pitch of 0.5° are established. The V-shaped grooves are formed on the end surface of the rotary body by vibration. In addition, the intersection between groove elements can be of different types. The relationship between the geometric parameters of V-shaped grooves and their location is determined. The influence of geometrical parameters of grooves on the overlap area is established depending on their location. Measures are proposed to ensure that the burnishing area is the same at different distances from the center of rotation of the rotary body end surface on which the partially regular microrelief is formed. A graph showing the dependence of the overlap area of two grooves on the axial pitch between them is constructed, and a block diagram of the algorithm for determining the optimal value of the axial pitch is developed.

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Section: Resultsmentioning

confidence: 99%

“…Using plastic deformation of the surface to form a regular microrelief on the conjugated working surfaces of heavily loaded friction pairs allows enhancing their durability [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Determining Optimal Parameters of Regular Microrelief Formed on the End Surfaces of Rotary Bodies

2021

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“…Surface roughness was taken as the output variable, and the burnishing force, the number of passes, feed rate, and burnishing speed were taken as input parameters. Basak et al [63] and Basak and Sonmez [64] examined the effects of burnishing force, feed rate, and the number of passes on surface hardness and surface roughness. A new ANN model with different neuron structures and algorithms was developed.…”

Section: Introductionmentioning

confidence: 99%

“…Singh and Bilga [67] generated an FNN model using different burnishing parameters (number of revolution, feed, number of tool passes, and pressure force) to achieve the required surface roughness and hardness. In previous research in the field of soft computing, FL [57][58][59][60], ANN [49,[61][62][63][64][65], and FNN [66,67] were used to model the ball burnishing process. There are a number of modeling methods.…”

Section: Introductionmentioning

confidence: 99%

The Modelling of Surface Roughness after the Ball Burnishing Process with a High-Stiffness Tool by Using Regression Analysis, Artificial Neural Networks, and Support Vector Regression

Kanović

Vukelić

Šimunović

et al. 2022

Metals

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Surface roughness is an important indicator of the quality of the machined surface. One of the methods that can be applied to improve surface roughness is ball burnishing. Ball burnishing is a finishing process in which a ball is rolled over the workpiece surface. Defining adequate input variables of the ball burnishing process to ensure obtaining the required surface roughness is a typical problem in scientific research. This paper presents the results of experiments to investigate ball burnishing of AISI 4130 alloy steel with a high-stiffness tool and a ceramic ball. The experiments were conducted following a randomized full factorial design for different levels of input variables. The input variables included the initial arithmetic mean roughness (the initial surface roughness), the depth of ball penetration, the burnishing feed, and the burnishing ball diameter, while the output variable was the arithmetic mean roughness after ball burnishing (the final surface roughness). The surface roughness modeling was performed based on the experimental results, using regression analysis (RA), artificial neural network (ANN), and support vector regression (SVR). The regression model displayed large prediction errors at low surface roughness values (below 1 μm), but it proved to be reliable for higher roughness values. The ANN and SVR models have excellently predicted roughness across a range of input variables. Mean percentage error (MPE) during the experimental training research was 29.727%, 0.995%, and 1.592%, and MPE in the confirmation experiments was 34.534%, 1.559%, and 2.164%, for RA, ANN, and SVR, respectively. Based on the obtained MPEs, it can be concluded that the application of ANN and SVR was adequate for modeling the ball burnishing process and prediction of the roughness of the treated surface in terms of the possibility of practical application in real industrial conditions.

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“…In recent decades, more emphasis has been placed on non-chip removal technologies such as the cold plastic surface hardening processes. Surface burnishing technology is used as an effective method for reducing surface roughness, increasing the micro-hardness of the sub-surface layer, and improving the shape correctness, while not requiring large amounts of coolant, so that overall low environmental impact and economical machining can be realized [5][6][7]. This process eliminates the disadvantages (tool marks, scratches) that are associated with the application of conventional chip removal techniques like turning or grinding, thereby preventing dissipation and surface damage [8].…”

Section: Introductionmentioning

confidence: 99%

The Influence of Diamond Burnishing Process Parameters on Surface Roughness of Low-Alloyed Aluminium Workpieces

Ferencsik

Varga

2022

Machines

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This study describes the determination and optimization of burnishing process parameters and their effects on surface roughness of EN AW-2011 aluminium alloy workpieces. The process has a low environmental load and the chip-free burnishing process improves the integrity of the machined surface, but to achieve this, the different burnishing parameters, for example, burnishing force, feed rate, speed and number of passes, must be properly defined according to the material of the workpiece. In our research, a full factorial experimental design method is used to plan and carry out the experiments and to determine the most appropriate parameter range for this material quality.

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Experimental research and ANN modeling on the impact of the ball burnishing process on the mechanical properties of 5083 Al-Mg material

Cited by 14 publications

References 26 publications

Determining Optimal Parameters of Regular Microrelief Formed on the End Surfaces of Rotary Bodies

Determining Optimal Parameters of Regular Microrelief Formed on the End Surfaces of Rotary Bodies

The Modelling of Surface Roughness after the Ball Burnishing Process with a High-Stiffness Tool by Using Regression Analysis, Artificial Neural Networks, and Support Vector Regression

The Influence of Diamond Burnishing Process Parameters on Surface Roughness of Low-Alloyed Aluminium Workpieces

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