Optimization of roller burnishing process on EN-9 grade alloy steel using response surface methodology

John, Merbin; Banerjee, Nilanjan; Shrivastava, Karuna; Vinayagam, B. K.

doi:10.1007/s40430-016-0674-8

Cited by 34 publications

(16 citation statements)

References 18 publications

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“…The new experiment is conducted for optimized input parameters, i.e., confirmatory test. The output parameters for optimized input parameters are bore size is 31.752 mm, surface roughness is 0.388 lm, and ovality is 0.012 mm [17][18][19]. Figs.…”

Section: Mathematical Modelsmentioning

confidence: 99%

Optimisation of internal roller burnishing process in CNC machining center using response surface methodology

John

Balaji

Vinayagam

2017

J Braz. Soc. Mech. Sci. Eng.

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In this study, optimization of the burnishing process was carried out to find better performance of burnishing process in CNC machining center using response surface methodology (RSM). The study was conducted on S.G. cast iron component in DOOSAN vertical machining center using internal roller burnishing tools. The internal roller burnishing tool was used in CNC vertical machining center to maintain precise bore size and surface finish. The input parameters were speed, feed, and penetration allowances for burnishing (stock). In this study, the parameter combinations are made using orthogonal L27 array. The optimization was carried out between input and output parameters for the given constraints using RSM. The response surface mathematical model was developed. The interactions between variables were analyzed using response surface plots. The optimized values for burnishing were burnishing speed of 1100 rpm, burnishing feed of 0.113461 mm/rev, and stock of 0.031503 mm. The optimized bore size (31.74 mm), surface roughness (0.3757 lm), and ovality (0.012 mm) were obtained.

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Section: Mathematical Modelsmentioning

confidence: 99%

Optimisation of internal roller burnishing process in CNC machining center using response surface methodology

John

Balaji

Vinayagam

2017

J Braz. Soc. Mech. Sci. Eng.

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“…Conventionally, the surface characteristics (roughness indicators, hardness criteria, the depth of the hardened layer, geometrical deviations, wear rate, and coefficient of friction) of the burnished surface are primarily considered in former publications. The empirical models of the average roughness (Ra) and surface hardness (SH) of the burnished EN-9 steel were developed in terms of the burnishing force (BF), feed rate (f), roller contact width, and the number of passes (N) [1]. The authors stated that the Ra and SH could be decreased by 94.5% and 41.7%, respectively.…”

Section: Introductionmentioning

confidence: 99%

Multi-Response Optimization of Burnishing Variables for Minimizing Environmental Impacts

2023

Teh. vjesn.

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The purpose of this investigation is to optimize minimum quantity lubrication (MQL) variables, including the nozzle diameter (D), inclined angle (A), air pressure (P), oil quantity (F), and spraying distance (S) for decreasing the energy consumption in the burnishing time (EB) and particulate matter index (PI) of the interior burnishing process. The optimal adaptive neuro-based-fuzzy inference system (ANFIS) models of the performance measures were proposed in terms of the MQL variables with the aid of the Taguchi method. The non-dominated sorting genetic algorithm based on the grid partitioning (NSGA-G) and TOPSI were employed to produce feasible solutions and determine the best optimal point. The obtained results indicated that the optimal values of the D, A, P, F, and S are 1.0 mm, 35 deg., 3 Bar, 70 ml/h, and 10 mm, respectively, while the EB and PI are decreased by 8.0% and 15.7% at the optimal solution. The optimal ANFIS models were trustworthy and ensure accurate predictions. The optimization technique comprising the ANFIS, NSGA-G, and TOPSIS could be extensively utilized to determine the optimal outcomes instead of the trial-error and/or human experience. The outcomes could help to decrease environmental impacts in the practical burnishing process.

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“…Amdouni et al [8] indicated that the burnishing crossed path could be effectively used to decrease the roughness, while the successive one was an alternative solution to increase the hardness for the burnished aluminium alloy. John et al [9] emphasized that the average roughness could be decreased by 94.5 %, and the micro-hardness could be increased by around 41.7 % for the burnishing process of EN-9 alloy. Similarly, the impacts of the processing conditions on the average roughness bore size, and the ovality of the internal roller burnishing were presented by John et al [10].…”

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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Optimization of roller burnishing process on EN-9 grade alloy steel using response surface methodology

Cited by 34 publications

References 18 publications

Optimisation of internal roller burnishing process in CNC machining center using response surface methodology

Optimisation of internal roller burnishing process in CNC machining center using response surface methodology

Multi-Response Optimization of Burnishing Variables for Minimizing Environmental Impacts

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

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