Predictive model development in dry turning of Nimonic C263 by artificial neural networks-Reference-Cited by-同舟云学术

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Predictive model development in dry turning of Nimonic C263 by artificial neural networks

Published:2021-12 Issue: Volume: Page:
ISSN:2214-7853
Container-title:Materials Today: Proceedings
language:en
Short-container-title:Materials Today: Proceedings

Author:

Ayyaswamy John Presin Kumar,Kulandaivel Arul^ORCID,Ezilarasan Chakaravarthy,Arunagiri Adinarayanan,Charles Martin,Kumar S. Raj

Publisher

Elsevier BV

Subject

General Medicine

Reference21 articles.

1. Artificial neural networks based integrated predictive modelling of quality characteristics in CNC turning of cantilever bars;Davakan;World J. Mech.,2017

2. Development of artificial intelligence model for the prediction of MRR in turning;Chaurasia;Int. J. Hybrid Inf. Technol.,2016

3. Ikpeseni Evaluation of ANN and ANFIS modeling ability in the prediction of AISI 1050 steel machining performance;Sada;Heliyon,2021

4. Experimental investigation for the multi-objective optimization of machining parameters on AISI D2 steel using particle swarm optimization coupled with artificial neural network;Gopan;J. Adv. Manuf. Syst.,2020

5. Hybrid intelligence systems and artificial neural network (ANN) approach for modeling of surface roughness in drilling;Ch. Sanjay;Cogent Eng.,2014

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Understanding the influence of tool coating and cutting environment on the machinability of Nimonic C-263;International Journal on Interactive Design and Manufacturing (IJIDeM);2023-10-30

2. Machine Learning Approach: Prediction of Surface Roughness in Dry Turning Inconel 625;Advances in Materials Science and Engineering;2022-10-05

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