Optimization of wear parameters using Taguchi grey relational analysis and ANN-TLBO algorithm for silicon nitride filled AA6063 matrix composites

Stalin, B.; Kumar, P. Ramesh; Ravichandran, M.; Kumar, M. Siva; Meignanamoorthy, M.

doi:10.1088/2053-1591/ab3d90

Cited by 74 publications

(17 citation statements)

References 44 publications

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“…The ANN model was built with a feed forward back-propagation type of network, where trainlm was the training function, learngdm was the learning function, performance was measured by the mean squared error, the optimum number of layers was five, the numbers of neurons was seven and transig was used for the transfer function (Stalin et al, 2019). The coefficient of correlation for predicted versus actual values was 0.9136.…”

Section: Resultsmentioning

confidence: 99%

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Comparative ANN-TLBO and RSM Optimisation Approach for Bioactive Potential of Microwave Convective Dried Mango (Mangifera indica)

Sarkar

Salauddin

Hazra

et al. 2020

The 1st International Electronic Conference on Food Science and Functional Foods

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The mango (Mangifera indica) is a rich source of antioxidants like phenolic acids and flavonoids. Polyphenol oxidase (PPO) and polyphenol peroxidase (POD) enzymes oxidize mono and diphenols, and thus the antioxidant potential diminishes. For cost-effective and sustainable preservation of this sub-tropical climacteric fruit, microwave convective dehydration is a prospective choice. Drying operations deactivate PPO and POD. Microwave convective drying was done at 100–300 W of power in a temperature range of 40–80 °C, with a total soluble solid content of 20–30 °B and a puree load of 0.4–0.6 g/cm2 to investigate the effect of the process parameters on the total phenolic content (TPC) of the final product. Response surface methodology (RSM) was employed to optimize the TPC value. Furthermore, an artificial neural network (ANN) with the back propagation-feed forward modeling approach was adopted for the experimental results obtained, teaching–learning-based optimization (TLBO) was then employed to acquire the optimized drying condition for samples with the maximum TPC value. The optimized process conditions obtained from both methods were virtually compatible. From RSM analysis, the maximum TPC value of 13.36 mg GAE/100 g was observed at a 170.27 W power level, 57.84 °C oven temperature, 0.60 g/cm2 of puree load and 29.05 °B total soluble solid content, whereas from the ANN-TLBO technique, 14.29 mg GAE/100 g of TPC was attained for the following combination of drying parameters: 175.08 W, 57.15 °C, 0.60 g/cm2 and 29.25 °B. The ANN-TLBO approach predicted a more optimized result (TPC value) in comparison with the RSM method.

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Section: Resultsmentioning

confidence: 99%

“…Sample extract preparation and estimation of the total phenolic content (TPC) was done following our previous work [3]. The response surface methodology (RSM) and ANN-TLBO [4] was conducted using Design Expert 7.0.0 and MATLAB 2018a (Figure 1).…”

Section: Methodsmentioning

confidence: 99%

Comparative ANN-TLBO and RSM Optimisation Approach for Bioactive Potential of Microwave Convective Dried Mango (Mangifera indica)

Sarkar

Salauddin

Hazra

et al. 2020

The 1st International Electronic Conference on Food Science and Functional Foods

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“…On the other hand for OC and TWR, the smaller-thebetter performance characteristic was considered, and the same has been expressed in Eq. 2 [32].…”

Section: Normalizing the Datamentioning

confidence: 99%

Multi Response Optimization of ECDM Process Parameters for Machining of Microchannel in Silica Glass Using Taguchi–GRA Technique

Bellubbi

Sathisha

Mallick³

2021

Silicon

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In this work, machining of microchannel in silica glass was successfully carried out using electro chemical discharge machining (ECDM) process. The experiments were planned according to L27 orthogonal array with applied voltage, stand-off distance (SOD), electrolyte concentration, pulse frequency and pulse-on-time (TON) as control factors. The material removal rate (MRR), overcut (OC) and tool wear rate (TWR) were considered as response characteristics. In this study the effects of control parameters on MRR, OC and TWR have been investigated. The multi-objective optimization of ECDM was carried out through grey relational analysis (GRA) method. Optimal combination of process parameters achieved from GRA was 45V applied voltage, 25wt.% electrolyte concentration, 1.5mm SOD, 400Hz pulse frequency and 45μs TON. ANOVA for GRG study revealed that the applied voltage (70.33%) was most significant factor affecting output responses followed by electrolyte concentration (11.69%), pulse frequency (4.98%) and SOD (4.13%). Furthermore, the regression equations were formulated for the optimum combination to predict the collaboration and higher-order effects of the control parameters. In addition confirmation test was conducted for the optimal setting of process parameters and the comparison of experimental results exhibited a good agreement with predicted values. The microstructural observation of machined surface for the optimum combination was carried out.

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“…The properties of Aluminium alloy can be altered by adopting some techniques, including heat treatment, grain refiners, by incorporating hard ceramic particles inside either micron or nano in size. Reinforcement can be done by ceramic particles such as B 4 C (Liu et al 2019), ZrO 2 (Hemanth 2009), Al 2 O 3 (Xu et al 2019), Sic (Lan et al 2004), CNT (Reddy and Anand 2019), WC (Banerjee et al 2019), Si 3 N 4 (Stalin et al 2019) etc to develop metal matrix composites. Among all-aluminum alloy families, 7 series contain zinc as a central alloying element and high strength alloy; they are prevalent in aerospace and automotive applications.…”

Section: Introductionmentioning

confidence: 99%

Microstructure evolution, mechanical properties, and fractography of AA7068/ Si₃N₄ nanocomposite fabricated thorough ultrasonic-assisted stir casting advanced with bottom pouring technique

Kumar¹,

Rana²,

Purohit³

2022

Mater. Res. Express

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Ceramic particulate embedded aluminum metal matrix nanocomposites (AMNCs) possess superior mechanical and surface properties and lightweight features. AMNCs are a suitable replacement of traditional material, i.e., steel, to make automotive parts. The current work deals with developing Si3N4 strengthened high strength AA7068 nanocomposites via novel ultrasonic-assisted stir casting method advanced with bottom pouring setup in the proportion of 0.5, 1.0, 1.5, and 2 wt.%. Planetary ball milling was performed on a mixture of AA7068 powder and Si3N4 (in the proportion of 3:1) before incorporation in aluminum alloy melt to avoid rejection of fine particles. Finite element scanning electron microscope (FESEM), Energy dispersive spectroscopy (EDS), X-Ray diffraction (XRD), and Elemental mapping techniques were used in the microstructural investigation. Significant grain refinement was observed with increasing reinforcing content, whereas agglomeration was found at higher weight %. Hardness, Tensile strength, ductility, porosity content, compressive strength, and impact energy were also examined of pure alloy and each composite. Improvement of 72.71%, 50.07%, and 27.41 % was noticed in hardness value, tensile strength, and compressive strength, respectively, at 1.5 weight % compared to base alloy because of various strengthening mechanisms. These properties are decreased at 2wt.% due to severe agglomeration. In contrast, nanocomposite's ductility and impact strength continuously decrease compared to monolithic AA7068. Fracture analysis shows the ductile and mixed failure mode in alloy and nanocomposites.

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Optimization of wear parameters using Taguchi grey relational analysis and ANN-TLBO algorithm for silicon nitride filled AA6063 matrix composites

Cited by 74 publications

References 44 publications

Comparative ANN-TLBO and RSM Optimisation Approach for Bioactive Potential of Microwave Convective Dried Mango (Mangifera indica)

Comparative ANN-TLBO and RSM Optimisation Approach for Bioactive Potential of Microwave Convective Dried Mango (Mangifera indica)

Multi Response Optimization of ECDM Process Parameters for Machining of Microchannel in Silica Glass Using Taguchi–GRA Technique

Microstructure evolution, mechanical properties, and fractography of AA7068/ Si₃N₄ nanocomposite fabricated thorough ultrasonic-assisted stir casting advanced with bottom pouring technique

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Optimization of wear parameters using Taguchi grey relational analysis and ANN-TLBO algorithm for silicon nitride filled AA6063 matrix composites

Cited by 74 publications

References 44 publications

Comparative ANN-TLBO and RSM Optimisation Approach for Bioactive Potential of Microwave Convective Dried Mango (Mangifera indica)

Comparative ANN-TLBO and RSM Optimisation Approach for Bioactive Potential of Microwave Convective Dried Mango (Mangifera indica)

Multi Response Optimization of ECDM Process Parameters for Machining of Microchannel in Silica Glass Using Taguchi–GRA Technique

Microstructure evolution, mechanical properties, and fractography of AA7068/ Si3N4 nanocomposite fabricated thorough ultrasonic-assisted stir casting advanced with bottom pouring technique

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Product

Resources

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Microstructure evolution, mechanical properties, and fractography of AA7068/ Si₃N₄ nanocomposite fabricated thorough ultrasonic-assisted stir casting advanced with bottom pouring technique