Optimization of Milling Parameters for Energy Savings and Surface Quality

Nguyen, Trung-Thanh; Nguyen, Truong-An; Trinh, Quang Hung

doi:10.1007/s13369-020-04679-0

Cited by 23 publications

(8 citation statements)

References 24 publications

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“…In this work, ten maximum values of the active turning-burnishing power and machine power consumed are taken into account to calculate the average outcomes. The similar definitions are computed in the works of References [22,23].…”

Section: Optimization Issuesmentioning

confidence: 99%

Modeling and Evaluation of Energy Efficiency of New Hybrid Turning-Burnishing Process in Terms of Surface Properties

Nguyen

Mia

2020

Energies

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The combination of the turning and burnishing process is an efficient approach to improve machined quality and productivity. This paper aims to optimize energy efficiency (EF), improved hardness ratio (IHR), and decreased roughness ratio (DRR) of a new hybrid turning-burnishing process. The machining parameters are the feed rate (f), turning speed (v), depth of cut (a), burnishing pressure (p), and the diameter of the compressing ball (d). A new turning-burnishing tool using compressed air has been designed and fabricated. A set of experiments for Aluminum Alloy 5083 were performed using the Taguchi method. The weightage principal component analysis (WPCA) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) were applied to obtain the weight values and optimal outcomes. The results indicated that optimum values of the depth of cut, pressure, diameter, feed rate, and speed are 1.00 mm, 0.4 MPa, 16.00 mm, 0.084 mm/rev, and 120 m/min, respectively. The improvements in the EF and IHR are by 20.75% and 8.23% respectively, while the DDR is decreased by 19.05%, as compared to common values.

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Section: Optimization Issuesmentioning

confidence: 99%

Modeling and Evaluation of Energy Efficiency of New Hybrid Turning-Burnishing Process in Terms of Surface Properties

Nguyen

Mia

2020

Energies

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“…Further, GA was adopted to optimize the conflicting multi-objectives. Nguyen et al [15] analyzed the influence of tool radius, feed, depth of cut, and cutting speed on product quality and energy efficiency. All the process variables were found to influence the two performance measures.…”

Section: Introductionmentioning

confidence: 99%

Sustainable thin-wall machining: holistic analysis considering the energy efficiency, productivity, and product quality

Bolar

Joshi

Das

2022

Int J Interact Des Manuf

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Enhanced energy efficiency, product quality, and productivity have become crucial requirements in thin-wall machining. Therefore, the work examined the impact of axial depth of cut, radial depth of cut, feed per tooth, and tool diameter on three performance measures. Full factorial was used to design experiments, and Analysis of Variance (ANOVA), a statistical method, was employed to analyze and interpret the influence of process variables on the machining performance. Additionally, Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) was adopted to arrive at the Pareto-optimal solutions to evaluate the trade-off between the three performance measures. The optimized process parameters for roughing operation helped maximize the process productivity at the expense of product quality. In contrast, the Pareto solutions for finishing operation effectively improved energy efficiency and produced quality open straight and curved thin-wall parts. Improved surface finish with minimal deflection can be achieved by milling with a cutter of diameter 8 mm and maintaining the feed, axial, and radial depth at 0.02 mm/z, 8 mm, and 0.3125 mm, respectively. The proposed findings can provide effective solutions for machining open straight and curved thin-wall parts with improved productivity, product quality, and energy efficiency.

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“…The range of four milling parameters is vc∈ [80,120], fz∈ [0.08,0.14], ae∈ [0.5,1.5] and ap∈ [8,12]. The random number m [-10,10].…”

Section: Source Of Datamentioning

confidence: 99%

“…Diyaley et al [7] studied six metaheuristic algorithms, in the form of artificial bee colony optimization, ant colony optimization, particle swarm optimization, differential evolution, firefly algorithm and teaching-learning-based optimization techniques applied for parametric optimization of a multi-pass face milling process. Nguyen et al [8] used a type of neutral network entitled the radius basic function (RBF) to render the relationships between milling inputs and performances measured. The adaptive simulated annealing (ASA) algorithm was applied to obtain the optimal values.…”

Section: Introductionmentioning

confidence: 99%

Research on Parallel Distributed Clustering Algorithm Applied to Milling Parameter Optimization

Wei

Sun

Liu

et al. 2021

Preprint

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In the big data era, traditional data mining technology cannot meet the requirements of massive data processing with the background of intelligent manufacturing. Aiming at insufficient computing power and low efficiency in mining process, this paper proposes a improved K-means clustering algorithm based on the concept of distributed clustering in cloud computing environment. The improved algorithm (T.K-means) is combined with MapReduce computing framework of Hadoop platform to realize parallel computing, so as to perform processing tasks of massive data. In order to verify the practical performance of T.K-means algorithm, taking machining data of milling Ti-6Al-4V alloy as the mining object. The mapping relationship among milling parameters, surface roughness and material removal rate is mined, and the optimized value for milling parameters are obtained. The results show that T.K-means algorithm can be used to mine the optimal milling parameters, so that the best surface roughness can be obtained in milling Ti-6Al-4V titanium alloy.

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Optimization of Milling Parameters for Energy Savings and Surface Quality

Cited by 23 publications

References 24 publications

Modeling and Evaluation of Energy Efficiency of New Hybrid Turning-Burnishing Process in Terms of Surface Properties

Modeling and Evaluation of Energy Efficiency of New Hybrid Turning-Burnishing Process in Terms of Surface Properties

Sustainable thin-wall machining: holistic analysis considering the energy efficiency, productivity, and product quality

Research on Parallel Distributed Clustering Algorithm Applied to Milling Parameter Optimization

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