Optimization and simulation of laser beam trepanning approach on DSS superalloy

Panigrahi, Debasish; Rout, Sweta; Tripathy, Sourav Sanket; Patel, Saroj Kumar; Dhupal, Debabrata

doi:10.1016/j.matpr.2020.12.094

Cited by 8 publications

(1 citation statement)

References 6 publications

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“…After analyzing the confi rmatory test, the model attained a 0.245 performance grade improvement action. Another multi-response optimization of the ECMM process was done by Panigrahi D. et al 28 using the Particle Swarm Optimization algorithm (PSO). The author utilized response surface methodology (RSM) to design the experiment model.…”

Section: Introductionmentioning

confidence: 99%

Investigation and Prediction of ECMM characteristics of Hardened Die Steel with Nanoparticle Added Electrolytes Using Hybrid Deep Neural Network

Kanniyappan

Sekar²

2022

Polish Journal of Chemical Technology

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In our work, the process efficiency of the ECMM should be improved by using different combinations of nano-particles and added electrolytes. The superior aim of this work is to improve and predict the ECMM machining characteristics of die hardened steel, namely material removal rate (MRR), Tool wear rate (TWR) and Surface Roughness (Ra). The machining conditions are optimized using Response Surface Methodology (RSM) based on Box Behnken Design. The better Nano electrolyte is optimized using Deer Hunting Optimization (DHO) based on the machined outcomes, and the performances are predicted using a hybrid Deep Neural Network (DNN) based DHO. The hybrid DNN-DHO based predicted outcome of MRR is 0.361 mg/min, TWR is 0.272 mg/min and Ra is 2.511 μm. The validation results show that our proposed DNN-DHO model performed well and obtained above 0.99 regression for both training and validation of DNN-DHO, where the root mean square error ranges between 0.018 and 0.024.

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Section: Introductionmentioning

confidence: 99%