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

Nguyen, Trung Thanh; Thai, Le Minh

doi:10.1007/s00170-021-06920-y

Cited by 13 publications

(13 citation statements)

References 25 publications

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“…Similar impacts of the tangential speed (cutting speed) on the energy efficiency can be found in the milling, 27 turning, 28 and burnishing operations. 29 Supplementary Figure 6b indicated that a higher linear speed increases energy efficiency. When the linear speed relatively changes from 4 mm/min to 8 mm/min, the energy efficiency is relatively decreased by 22.2%.…”

Section: Parametric Influencesmentioning

confidence: 97%

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An energy efficiency-based optimization of the rough honing process of hardened 5150 alloy

Van

Nguyen

2022

Proceedings of the Institution of Mechanical Engineers, Part E:

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The honing operation is maned as an effective and efficient finishing solution to achieve better surface properties and machining efficiency. However, quality and productivity indicators are primary considerations of published investigations. In this study, the rough honing operation of the hardened 5150 alloy has been addressed and optimized to decrease the energy efficiency ( EF), surface roughness ( SR), and material removal rate ( MRR). The optimizing factors include tangential speed ( T), linear speed ( L), honing pressure ( P), and honing depth ( D). The artificial neural network (ANN) method is utilized to present the non-linear relations between the process parameters and performance measures. The Taguchi method is applied to select the best architecture of ANN models. The Entropy and the vibration and communication particle swarm optimization (VCPSO) algorithm is applied to compute the weight factors of response and find optimal data. The total honing cost ( THC) model is proposed to compute the honing expenses. The results indicated that the optimal values of the T, L, P, and D are 32.0 m/min, 8.0 m/min, 3.3 MPa, and 0.01 mm, respectively, while the improvements in the EF, SR, MRR, and THC were 15.17%, 44.7%, 134.19%, and 25.0%, as compared to the initial values. The optimizing technique comprising the ANN, Entropy, and VCPSO could be named as an effective and efficient method to solve complicated optimization issues. The optimal data could help the machine operator to improve the honing performances with regard to saving trial costs and essential efforts.

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Section: Parametric Influencesmentioning

confidence: 97%

“…Higher energy efficiency with increased honing depth (machining depth) can be found for the milling, 27 turning, 28 and burnishing operations. 29 The main influences of process parameters on the surface roughness are shown in Supplementary Figure 7.…”

Section: Parametric Influencesmentioning

confidence: 99%

An energy efficiency-based optimization of the rough honing process of hardened 5150 alloy

Van

Nguyen

2022

Proceedings of the Institution of Mechanical Engineers, Part E:

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“…The authors stated that improvements in the power factor, EB, and Ra were 22.0%, 49.5%, and 13.8%, respectively. The ANFIS approach was applied to present the correlations between process parameters (S, f, BD, and N) and the burnishing outputs (energy efficiency, machining noise, and Ra) [13]. The optimal values presented that the reductions in the energy efficiency, machining noise, and Ra were 6.98%, 25.0%, and 2.23%, 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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“…The AR, cylindricity (CL), and circularity (CC) were saved by 55.0%, 80.0%, and 2.0% at the optimal S, f, and D for the burnished hardened steel [7]. The ANFIS models of the energy efficiency and machining noise were developed regarding the S, f, D, and the number of rollers for internal burnished SCr440 steel [8]. The authors stated that the energy efficiency and machining noise were saved by 7.0% and 2.2% at the optimality.…”

Section: Introductionmentioning

confidence: 99%

Performance optimization of multi-roller flat burnishing process in terms of surface properties

Thai¹,

Van²,

Nguyen³

2023

Journal of Machine Engineering

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In the current investigation, two primary indicators, including the average roughness (AR) and Brinell hardness (BH) of the roller burnishing operation are enhanced using the optimal inputs (the spindle speed-S, feed rate-f, and depth of penetration-D). The performance measures are developed using the Kriging approach and optimal outcomes are generated by the Crow Search Algorithm (CSA). The optimal outcomes generated by the CSA of the S, f, and D were 832 rpm, 112 mm/min, and 0.12 mm, while the AR was reduced by 37.0% and the BH was increased by 29.9%, respectively. The optimal findings could be utilized in the practice for enhancing the burnished quality and to develop a professional system related to the roller burnishing operation. The Kriging-based AR and BH correlations could be used to present nonlinear experimental data. The optimizing technique could be utilized to deal with optimizing problems for different machining operations.

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

Cited by 13 publications

References 25 publications

An energy efficiency-based optimization of the rough honing process of hardened 5150 alloy

An energy efficiency-based optimization of the rough honing process of hardened 5150 alloy

Multi-Response Optimization of Burnishing Variables for Minimizing Environmental Impacts

Performance optimization of multi-roller flat burnishing process in terms of surface properties

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