Optimization of burnishing parameters and determination of select surface characteristics in engineering materials

Babu, P. D.; Ankamma, Kandula; Prasad, T. S. P. L. N.; Raju, A. V. Sita Rama; Prasad, N. Eswara

doi:10.1007/s12046-012-0092-2

Cited by 18 publications

(10 citation statements)

References 6 publications

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“…In earlier studies of the ball burnishing process, the Taguchi method [43][44][45][46][47][48][49][50] and response surface methodology (RSM) [51][52][53][54][55][56] were widely used. The Taguchi method reduces the number of experiments required for analysis but considers only the main effects on the process and does not allow consideration of the interaction between the input variables.…”

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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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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“…A roughness of 0.055 μm and hardness of 46.69 HRC for the burnished T215Cr12 material were obtained with the aid of the response surface method [2]. Babu et al [3] stated that the burnishing depth significantly increased the micro-hardness, while the average roughness was primarily influenced by the burnishing force for the burnishing processes of the EN steels, aluminium alloy, and alpha-beta brass. Tadic et al [4] emphasized that a small burnishing force significantly contributed to a lower roughness.…”

Section: Introductionmentioning

confidence: 99%

Optimization of the Internal Roller Burnishing Process for Energy Reduction and Surface Properties

Nguyen

Thai

2021

SV-JME

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Improving the surface quality after burnishing operation has been the subject of various published investigations. Unfortunately, the trade-off analysis between the energy consumption and surface characteristics of the internal burnishing has been not addressed due to the expensive cost and huge efforts required. The objective of the present work is to optimize burnishing factors, including the spindle speed, burnishing feed, depth of penetration, and the number of rollers for minimizing the energy consumed in the burnishing time, as well as surface roughness and maximizing Rockwell hardness. An adaptive neuro-based-fuzzy inference system (ANFIS) was used to develop burnishing objectives in terms of machining parameters. The optimization outcomes were selected using an evolution algorithm, specifically the non-dominated sorting particle swarm optimization (NSPSO). The results of the proposed ANFIS models are significant and can be employed to predict response values in industrial applications. The optimization technique comprising the ANFIS and NOPSO is a powerful approach to model burnishing performances and select optimal parameters as compared to the trial and error method as well as operator experience. Finally, the optimal solution can help to achieve the improvements in the energy consumed by 16.3 %, surface roughness by 24.3 %, and Rockwell hardness by 4.0 %, as compared to the common values.

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“…Babu et al. 17 reported the effects of burnishing parameters on the surface characteristics, microstructure and microhardness. Based on optimal burnishing parameters, they presented burnishing maps.…”

Section: Introductionmentioning

confidence: 99%

“…The results indicated that burnishing with an especially designed tool improved ductility but did not improve hardness. Babu et al 17 reported the effects of burnishing parameters on the surface characteristics, microstructure and microhardness. Based on optimal burnishing parameters, they presented burnishing maps.…”

Section: Introductionmentioning

confidence: 99%

Investigation of ball burnishing processing on mechanical characteristics of wooden elements

Babić

Kocovic

Vukelić

et al. 2016

Proceedings of the Institution of Mechanical Engineers, Part C:

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Numerous research results indicate that the finishing processing of metal materials using ball burnishing has positive effects from the aspect of surface roughness decrease to the hardness increase in the surface layers of the processed materials. Little research has been devoted to this type of processing for nonmetal materials. This paper presents research results related to the influence of ball burnishing processing on the hardness increase of a wooden element. It was determined that the hardness can be increased up to three times for processing of wood using this technology, which is not the case for processing of metal materials.

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Optimization of burnishing parameters and determination of select surface characteristics in engineering materials

Cited by 18 publications

References 6 publications

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 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

Optimization of the Internal Roller Burnishing Process for Energy Reduction and Surface Properties

Investigation of ball burnishing processing on mechanical characteristics of wooden elements

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