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Optimization of internal burnishing operation for energy efficiency, machined quality, and noise emission

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Boosting energy efficiency and machining quality are prominent solutions to achieve sustainable production for burnishing operations. In this work, an effective optimization has been performed to enhance the energy efficiency (EFb) and decrease the machining noise (MN) as well as surface roughness (SR) of the internal burnishing operation. The burnishing factors are the spindle speed (S), burnishing feed (f), burnishing depth (D), and the number of rollers (N). The burnishing trails of the hardened material labeled SCr440 have been conducted on a CNC milling machine. The adaptive neuro-based-fuzzy inference system (ANFIS) was used to construct the correlations between the process inputs and burnishing responses. The entropy approach is employed to calculate the weight of each technical objective. The non-dominated sorting particle swarm optimization (NSPSO) is utilized to determine the optimal parameters. A comprehensive model of the production cost is developed to check the effectiveness of the proposed approach. The scientific outcomes revealed that the optimal values of the S, f, D, and N are 1645 RPM, 260 mm/min, 0.08 mm, and 4, respectively. The improvements in the EFb, SR, and MN are 6.98%, 25.00%, and 2.23%, as compared to the initial values. The machining cost is saved by 6.2% at the optimal solution. Moreover, the scientific finding is a potent technical solution to enhance machining performances for the burnishing process of various components having internal holes.

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Data availability

All data and materials have been included on the manuscript.


AL (μm):

Depth of the affected layers


Adaptive neuro-fuzzy inference system

SR (μm):

Surface roughness

BE (kJ):

Burnishing energy

BS (mm):

Bore size

D (mm):

Burnishing depth

EC (kJ):

Energy consumed

EFb (%):

Energy efficiency

f (mm/min):

Feed rate

F (N):

Burnishing force



MH (VH):


MN (dB):

Machining noise


Minimum quantity lubrication


Number of rollers


Non-dominated sorting particle swarm optimization


Number of passes




Power factor

PI (μm):

Profile irregularities

RS (MPa):

Residual stress


Response surface method

RW (mm):

Roller width

Ry (μm):

Maximum height roughness

S (RPM):

Spindle speed


Surface hardness

SO (mm):


V (m/min):

Burnishing speed

VH (VH):

Vicker hardness


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This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 107.04-2020.02.

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Authors and Affiliations



Conceptualization, T.-T.N. and M.T.L.; methodology, T.-T.N. and M.T.L.; software, T.-T.N. and M.T.L.; validation, T.-T.N. and M.T.L.; data curation, T.-T.N.; writing—original draft preparation, T.-T.N. and M.T.L.; writing—review and editing, T.-T.N. and M.T.L.; All authors have read and agreed to the published version of the manuscript.

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Correspondence to Minh-Thai Le.

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Nguyen, TT., Le, MT. Optimization of internal burnishing operation for energy efficiency, machined quality, and noise emission. Int J Adv Manuf Technol 114, 2115–2139 (2021).

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