Optimization of Machining Conditions for Surface Quality in Milling AA7039-Based Metal Matrix Composites

Karabulut, Şener; Gökmen, Uğur; Çinici, Henifi

doi:10.1007/s13369-017-2691-z

Cited by 30 publications

(8 citation statements)

References 23 publications

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“…This machine was used to turn a maximum of 320 mm diameter and maximum of 500 mm length as shown in Figure 2. Diamond-Like Carbon-(DLC-) coated tungsten carbide tool is used for turning hybrid composite materials [32][33][34].…”

Section: Methodsmentioning

confidence: 99%

Synthesis of AA8050/B₄C/TiB₂ Hybrid Nanocomposites and Evaluation of Computer‐Aided Machining Parameters

Sathish

Mohanavel

Raja

et al. 2022

Journal of Nanomaterials

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This research focusses on synthesizing the hybrid nanocomposite samples with AA8050 and the reinforcement of B4C and TiB2 nanoparticles at 3 different quality grades. To investigate their machinabilities on the prepared composites in the computer-aided machining centre, the objectives are maximizing the material removal rate (MRR) and minimizing the surface roughness for a specific application. Stir casting process was employed in synthesizing the hybrid nanocomposite samples. Utilizing CNC turning centre was employed to investigate machinability performance on hybrid nanocomposite samples. The PVD-coated HSS tool and dry cutting environment were considered. The quality of machining was investigated by observing the surface roughness on the machined surfaces of samples of hybrid nanocomposite. The machining rate was investigated through the response of material removal rate at as per Taguchi design of experiments L27 orthogonal array. The hybrid nanocomposite synthesizing parameter of contribution of nanoparticle reinforcement (8%, 10%, and 12%) and the Turing parameters include spindle speed (800 rpm, 1000 rpm, and 1200 rpm), machining feed (0.05 mm/rev, 0.10 mm/rev, and 0.15 mm/rev) and depth of cut (0.5 mm, 0.75 mm, and 1 mm). The best performing input levels were identified through Taguchi analysis and the involved input variables were analysed and prediction model developed through ANOVA. The maximum material removal rate and the minimum surface roughness were reordered as 1380 mm3/min.

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Section: Methodsmentioning

confidence: 99%

Synthesis of AA8050/B₄C/TiB₂ Hybrid Nanocomposites and Evaluation of Computer‐Aided Machining Parameters

Sathish

Mohanavel

Raja

et al. 2022

Journal of Nanomaterials

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“…Some experimental studies have investigated the potential of the minimum quantity lubrication technique in the high-speed drilling process [5]. According to Karabulut et al [6], minimum quantity lubrication has been proven as an effective method to replace the application of cooling fluid in the metal cutting process. Minimum quantity lubrication eliminates cutting fluid purchase and management from cost components and thus reduces overall machining costs [7].…”

Section: Introductionmentioning

confidence: 99%

Comparison of metal removal rate and surface roughness optimization for AISI 316L using sunflower oil minimum quantity lubrication and dry turning processes

MARTOWIBOWO

Ariza²,

Damanik³

2022

JMES

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The turning process is one of the significant machining processes widely applied in manufacturing industries. The study compared the minimum quantity lubrication turning process using sunflower oil lubrication and the dry turning process for AISI 316L material. In this study, a genetic algorithm was used to optimize material removal rate and surface roughness. Tool nose radius, cutting speeds, feed rates, and depth of cut was chosen as process parameters. The result of the process was a fitness function, which reflects the correlation between process parameters and material removal rate or surface roughness. The genetic algorithm uses the fitness function to yield optimum process parameters with the highest material removal rate and lowest surface roughness in a separate optimization process. The optimization method developed in the study can be applied to predict optimum material removal rate and surface roughness values for minimum quantity lubrication or dry turning process. The study concluded that the minimum quantity lubrication technique could yield favorable machining results with a higher material removal rate and lower surface roughness.

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“…In addition to cutting parameter influence, the optimization of machining conditions in the machining of MMCs is general in today's studies [16,17]. The optimization of machining conditions in MMCs milling was studied by S. Karabulut et al [18] to achieve a better surface finish. In this study, the machining parameter was optimized by Taguchi's L18 (21 × 32), and an artificial neural network (ANN) model was used to estimate Ra's arithmetical mean roughness value.…”

Section: Introductionmentioning

confidence: 99%

Experimental Investigation of Surface Roughness in Milling of DuralcanTM Composite

et al. 2021

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In today’s developing aircraft and automotive industry, extremely durable and wear-resistant materials, especially in high temperatures, are applied. Due to this practical approach, conventional materials have been superseded by composite materials. In recent years, the application of metal matrix composites has become evident in industry 4.0. A study has been performed to analyze the surface roughness of aluminum matrix composites named Duralcan® during end milling. Two roughness surface parameters have been selected: arithmetical mean roughness value Ra and mean roughness depth Rz regarding the variable cutting speed. Due to the classification of aluminum matrix composites as hard-to-cut materials concerning excessive tool wear, this paper describes the possibility of surface roughness prediction using machine learning algorithms. In order to find the best algorithm, Classification and Regression Tree (CART) and pattern recognition models based on artificial neural networks (ANN) have been compared. By following the obtained models, the experiment shows the effectiveness of roughness prediction based on verification models. Based on experimental research, the authors obtained the coefficient R2 for the CART model 0.91 and the mean square error for the model ANN 0.11.

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Optimization of Machining Conditions for Surface Quality in Milling AA7039-Based Metal Matrix Composites

Cited by 30 publications

References 23 publications

Synthesis of AA8050/B₄C/TiB₂ Hybrid Nanocomposites and Evaluation of Computer‐Aided Machining Parameters

Synthesis of AA8050/B₄C/TiB₂ Hybrid Nanocomposites and Evaluation of Computer‐Aided Machining Parameters

Comparison of metal removal rate and surface roughness optimization for AISI 316L using sunflower oil minimum quantity lubrication and dry turning processes

Experimental Investigation of Surface Roughness in Milling of DuralcanTM Composite

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Optimization of Machining Conditions for Surface Quality in Milling AA7039-Based Metal Matrix Composites

Cited by 30 publications

References 23 publications

Synthesis of AA8050/B4C/TiB2 Hybrid Nanocomposites and Evaluation of Computer‐Aided Machining Parameters

Synthesis of AA8050/B4C/TiB2 Hybrid Nanocomposites and Evaluation of Computer‐Aided Machining Parameters

Comparison of metal removal rate and surface roughness optimization for AISI 316L using sunflower oil minimum quantity lubrication and dry turning processes

Experimental Investigation of Surface Roughness in Milling of DuralcanTM Composite

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Synthesis of AA8050/B₄C/TiB₂ Hybrid Nanocomposites and Evaluation of Computer‐Aided Machining Parameters

Synthesis of AA8050/B₄C/TiB₂ Hybrid Nanocomposites and Evaluation of Computer‐Aided Machining Parameters