<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</abstract>

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.

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

Polish Journal of Chemical Technology

This work is licensed under the Creative Commons Attribution 4.0 International License.

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...

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

Data publikacji: 26 gru 2022

Zakres stron: 7 - 22

DOI: https://doi.org/10.2478/pjct-2022-0024

Słowa kluczowe
ECMM, Die hardened steel, machining parameters, RSM, hybrid, neural network, prediction

© 2022 Vijayakumar Kanniyappan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

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

Data publikacji: 26 gru 2022

Zakres stron: 7 - 22

DOI: https://doi.org/10.2478/pjct-2022-0024

Słowa kluczoweECMM, Die hardened steel, machining parameters, RSM, hybrid, neural network, prediction

© 2022 Vijayakumar Kanniyappan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
ECMM, Die hardened steel, machining parameters, RSM, hybrid, neural network, prediction