Reduction of Defects in Al-6061 Friction Stir Welding and Verified by Radiography

Kumaravel, D.; Raja, V.K. Bupesh; Potnuru, Chakravarthy; Polina, Navakanth

doi:10.1088/1757-899x/197/1/012062

Cited by 3 publications

(6 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These values indicate the monomeric nature of the complexes. [30,39] The data thus obtained are in accordance with the microanalytical and mass spectral data. The Co(II) complexes 2 and 6 showed bands in the region 13 986-14 705 cm −1 , 28 571-28 902 cm −1 and 31 847-32 894 cm −1 , which were assigned to the transitions 4 4 T 1g (F) → 4 A 2g (F) and 4 T 1g (F) → 4 T 2g (P), respectively.…”

Section: Electronic Spectra and Magnetic Properties Of The Complexessupporting

confidence: 76%

“…The materials and methods, DNA-binding, −cleavage and antimicrobial studies were followed as per the procedures previously reported by us. [30] The procedure for cytotoxicity measurements is given below.…”

Section: Methodsmentioning

confidence: 99%

“…The Schiff base ligand L was synthesized according to the method previously reported by us. [30] The metal complexes of the types [M(L)(bpy) 2 ]Cl 2 and [M(L)(phen) 2 ] Cl 2 were synthesized using the following procedure. The synthesized ligand L (5 mmol) was dissolved in a hot methanolic solution.…”

Section: Synthesis Of Schiff Base Ligand (L) and Metal Complexesmentioning

confidence: 99%

See 2 more Smart Citations

DNA fastening and scission actions of Cu(II), Co(II), Ni(II) and Zn(II) complexes: synthesis, spectral characterization and cytotoxic study

Kumaravel¹,

Utthra²,

Raman³

2017

Applied Organom Chemis

View full text Add to dashboard Cite

A heterocyclic compound, 2‐(aminomethyl)benzimidazole dihydrohydrochloride, was treated with nitrobenzaldehyde to form a Schiff base that was made to react with divalent metals. A co‐ligand, either 1,10‐phenanthroline or 2,2′‐bipyridine, was added to this mixture to obtain metal chelators of type [ML(co‐L)2]Cl2. They were in 1:1:2 stoichiometry ratio, which was characterized by various spectroscopic techniques that suggested an octahedral geometry around the central metal ions. These complexes were investigated for their binding affinities with calf thymus (CT) DNA, using various techniques, such as UV–Vis, viscosity, cyclic voltammetry (CV), etc. The binding interaction studies revealed intercalation as the possible binding mode of the complexes with the CT DNA. In addition, these complexes were screened for their antimicrobial potential and DNA denaturing tendencies using gel electrophoretic assay. The antimicrobial screening investigation showed that the complexes behaved as better antimicrobial agents than the ligand, especially, complex 5 shows exceptional activity even in the electrophoretic assay along with the antimicrobial efficacy. Moreover, complex 5 was able to denature the plasmid DNA better than the other compounds. All the compounds were screened for cytotoxic efficacy, and the IC50 values suggest that the compounds possess cytotoxic activity to some extent that is almost the same as the activity of cisplatin.

show abstract

Section: Electronic Spectra and Magnetic Properties Of The Complexessupporting

confidence: 76%

Section: Methodsmentioning

confidence: 99%

Section: Synthesis Of Schiff Base Ligand (L) and Metal Complexesmentioning

confidence: 99%

See 1 more Smart Citation

DNA fastening and scission actions of Cu(II), Co(II), Ni(II) and Zn(II) complexes: synthesis, spectral characterization and cytotoxic study

Kumaravel¹,

Utthra²,

Raman³

2017

Applied Organom Chemis

View full text Add to dashboard Cite

show abstract

“…Efforts have been made to experimentally determine the effects of welding parameters on void formation in commonly used aluminum alloys. [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] Tracers have been used to examine experimentally how the flow of materials affect the void formation. [20][21][22] The time lapse determination of the tracer's position in some cases indicated that the voids occurred near the bottom of the pin where the flow of the plasticized material was interrupted.…”

Section: Introductionmentioning

confidence: 99%

“…Here we examine the effectiveness of supervised machine learning (ML) algorithms to forecast the void formation during FSW. One hundred and eight sets of data for the FSW of three aluminum alloys, AA2024, AA2219 and AA6061 obtained from the peer-reviewed literature [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] have been analyzed using neural network (NN) and decision tree (DT) to examine the effectiveness of ML to mitigate void formation. Vibration and poor fixtures may affect the quality of welds, at least in principle.…”

Section: Introductionmentioning

confidence: 99%

Conditions for void formation in friction stir welding from machine learning

Mukherjee

DebRoy

2019

npj Comput Mater

View full text Add to dashboard Cite

Friction stir welded joints often contain voids that are detrimental to their mechanical properties. Here we investigate the conditions for void formation using a decision tree and a Bayesian neural network. Three types of input data sets including unprocessed welding parameters and computed variables using an analytical and a numerical model of friction stir welding were examined. One hundred and eight sets of independent experimental data on void formation for the friction stir welding of three aluminum alloys, AA2024, AA2219, and AA6061, were analyzed. The neural network-based analysis with welding parameters, specimen and tool geometries, and material properties as input predicted void formation with 83.3% accuracy. When the potential causative variables, i.e., temperature, strain rate, torque, and maximum shear stress on the tool pin were computed from an approximate analytical model of friction stir welding, 90 and 93.3% accuracies of prediction were obtained using the decision tree and the neural network, respectively. When the same causative variables were computed from a rigorous numerical model, both the neural network and the decision tree predicted void formation with 96.6% accuracy. Among these four causative variables, the temperature and maximum shear stress showed the maximum influence on void formation.

show abstract

Optimised machine learning classification model to detect void formations in friction stir welding

Baruah

Borkar

2023

Materials Today: Proceedings

View full text Add to dashboard Cite

Reduction of Defects in Al-6061 Friction Stir Welding and Verified by Radiography

Cited by 3 publications

References 4 publications

DNA fastening and scission actions of Cu(II), Co(II), Ni(II) and Zn(II) complexes: synthesis, spectral characterization and cytotoxic study

DNA fastening and scission actions of Cu(II), Co(II), Ni(II) and Zn(II) complexes: synthesis, spectral characterization and cytotoxic study

Conditions for void formation in friction stir welding from machine learning

Optimised machine learning classification model to detect void formations in friction stir welding

Contact Info

Product

Resources

About