Analysis of ball-burnishing impact on barrier properties of wood workpieces

Vukelić, Djordje; Tadić, Branko; Džunić, Dragan; Kocovic, Vladimir; Brzakovic, Ljiljana; Živković, Milutin; Šimunović, Goran

doi:10.1007/s00170-017-0134-3

Cited by 7 publications

(3 citation statements)

References 47 publications

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“…The authors revealed that the enhancements in the SR and SH were 24.0% and 86.0%. A new ball-burnishing tool was developed to investigate the barrier properties of wood specimens [4]. The authors stated that the water absorption was significantly decreased, while the density and hardness of the surface layer were effectively increased.…”

Section: Introductionmentioning

confidence: 99%

Multi-Response Optimization of the Flat Burnishing Process with a High-Stiffness Tool in terms of Surface Characteristics

2023

Teh. vjesn.

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In this work, the surface roughness (SR), surface hardness (SH), and the thickness of the affected layer (TL) of the multi-roller flat burnishing process are optimized.The parameter inputs are the tool rotational speed (S), burnishing depth (D), and feed rate (f). The flat burnishing tool having three rollers was utilized to facilitate burnishing trials. The Kriging models of performances are proposed regarding inputs.The CRITIC method and Crow Search Algorithm (CSA) were employed to select weights and optimality. The optimizing outcomes indicated that the optimal values of the S, f, and D were 912 rpm, 150 mm/min, and 0.12 mm, respectively. The improvements in the SR, SH, and TL were 33.3%, 26.9%, and 48.6%, respectively. The SR was primarily influenced by the f, followed by the D and S, respectively. The SH and TL were primarily influenced by the D, followed by the S and f, respectively. The optimal data could be applied to the practical multi-roller burnishing process to improve surface properties for flat surfaces. The Kriging models and CSA could be efficiently utilized to solve complex issues for burnishing operations and other machining processes.

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Section: Introductionmentioning

confidence: 99%

Multi-Response Optimization of the Flat Burnishing Process with a High-Stiffness Tool in terms of Surface Characteristics

2023

Teh. vjesn.

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“…This process can manage various materials such as steel, copper, aluminum, brass, polymers, titanium, nickel-chromium-molybdenum, wood, etc. [5].…”

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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“…Ball burnishing has a positive effect on surface roughness and can increase the hardness of surface layers of treated materials up to three times [48]. The treated samples have better water absorption resistance, of even more than 60%, depending on the type of wood [49]. The roller burnishing process requires less time than other finishing techniques.…”

Section: Introductionmentioning

confidence: 99%

Investigation into the Effects of Roller Pressing on Hardness, Roughness and Elastic Modulus of Wooden Workpieces

et al. 2022

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The paper investigates the effects of roller pressing on the hardness, roughness, and elastic modulus of wooden workpieces. For research purposes, a roller pressing device based on purely mechanical processing was designed and developed. Four different levels of pressing force have been applied to eight different types of wood: cherry, fir, alder, linden, beech, walnut, oak, and ash. The obtained results indicate that the proposed processing method can significantly improve the hardness, elastic modulus, and surface quality of wooden workpieces. The ash sample exhibited the largest relative increase in hardness (175.9%), while the most significant relative increase in the elastic modulus (66.73%) was measured on the linden sample. The largest relative decrease in surface roughness (54.75%) was achieved on the alder sample. For all types of wood except for fir, in which case an increase in pressing force did not produce the desired reduction of roughness, correlation coefficients indicate a strong relationship between the pressing force as an input variable and the elastic modulus, hardness, and roughness as output variables.

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Analysis of ball-burnishing impact on barrier properties of wood workpieces

Cited by 7 publications

References 47 publications

Multi-Response Optimization of the Flat Burnishing Process with a High-Stiffness Tool in terms of Surface Characteristics

Multi-Response Optimization of the Flat Burnishing Process with a High-Stiffness Tool in terms of Surface Characteristics

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

Investigation into the Effects of Roller Pressing on Hardness, Roughness and Elastic Modulus of Wooden Workpieces

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