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Avanish Kumar Dubey

Researcher at Motilal Nehru National Institute of Technology Allahabad

Publications -  69
Citations -  2038

Avanish Kumar Dubey is an academic researcher from Motilal Nehru National Institute of Technology Allahabad. The author has contributed to research in topics: Machining & Laser cutting. The author has an hindex of 17, co-authored 60 publications receiving 1710 citations.

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Laser beam machining—A review

TL;DR: The experimental and theoretical studies of LBM show that process performance can be improved considerably by proper selection of laser parameters, material parameters and operating parameters, and the trend for future research is outlined.
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Multi-objective optimization of Nd:YAG laser cutting of nickel-based superalloy sheet using orthogonal array with principal component analysis

TL;DR: In this paper, a hybrid approach of Taguchi method (TM) and principal component analysis (PCA) has been applied for multi-objective optimization (MOO) of pulsed Nd:YAG laser beam cutting (LBC) of nickel-based superalloy (SUPERNI 718) sheet to achieve better cut qualities within existing resources.
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Multi-objective optimisation of laser beam cutting process

TL;DR: In this paper, a hybrid Taguchi method and response surface method (TMRSM) was proposed for multi-response optimization of a laser beam cutting process, where the Taguchi quality loss function was used to find the optimum level of input cutting parameters such as assist gas pressure, pulse width, pulse frequency and cutting speed.
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Simultaneous optimization of multiple quality characteristics in laser cutting of titanium alloy sheet

TL;DR: In this article, a genetic algorithm based multi-objective optimization for the laser cutting of a Titanium alloy sheet (grade 5) was proposed to simultaneously optimize the kerf taper and surface roughness.
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Taguchi based fuzzy logic optimization of multiple quality characteristics in laser cutting of Duralumin sheet

TL;DR: In this paper, a hybrid approach, obtained by combining robust parameter design methodology and fuzzy logic theory has been applied to compute the fuzzy multi-response performance index, which is further used for multi-objective optimization.