An energy survey to optimize the technological parameters during the milling of AISI 304L steel using the RSM, ANN and genetic algorithm

Author:

Bousnina Kamel12ORCID,Hamza Anis12,Ben Yahia Noureddine12

Affiliation:

1. Department of Mechanical Engineering, National School of Engineering of Tunis, University of Tunis, Tunis, Tunisia

2. Mechanical, Production and Energy Laboratory (LMPE), University of Tunis, Tunis, Tunisia

Publisher

Informa UK Limited

Subject

Industrial and Manufacturing Engineering,Mechanics of Materials,General Materials Science

Reference42 articles.

1. U.S. Energy Information Administration (EIA). International Energy Outlook 2019. Office of energy analysis. U.S. Department Of Energy, Washington, DC 20585, 2019 September.

2. World Energy Outlook 2019

3. Energy consumption, capital expenditures, R&D cost and company profitability: evidence from paper and allied industry

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. Modelling and parametric optimization of EDM of Al 8081/SiCp composite through DEAR approach;International Journal on Interactive Design and Manufacturing (IJIDeM);2024-01-10

2. Predictive optimization of surface quality, cost, and energy consumption during milling alloy 2017A: an approach integrating GA-ANN and RSM models;International Journal on Interactive Design and Manufacturing (IJIDeM);2023-11-19

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