A combination of PSO-ANN hybrid algorithm and genetic algorithm to optimize technological parameters during milling 2017A alloy
Author:
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
Funder
no funding
Publisher
Informa UK Limited
Subject
Industrial and Manufacturing Engineering,Control and Systems Engineering
Link
https://www.tandfonline.com/doi/pdf/10.1080/21681015.2023.2243312
Reference38 articles.
1. Predictive Modelling and Optimization of Machining Parameters to Minimize Surface Roughness using Artificial Neural Network Coupled with Genetic Algorithm
2. Multi-objective optimization of cutting parameters for turning AISI 52100 hardened steel
3. ANN–PSO Integrated Optimization Methodology for Intelligent Control of MMC Machining
4. Optimization of cutting conditions using artificial neural networks and the Edgeworth-Pareto method for CNC face-milling operations on high-strength grade-H steel
5. Reducing the energy consumed and increasing energy efficiency in the turning process;Bousnina K;Int J Mod Manuf Technol,2020
Cited by 9 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. Exploring cryo-MQL medium for hard machining of hastelloy C276: a multi-objective optimization approach;International Journal on Interactive Design and Manufacturing (IJIDeM);2024-08-31
2. A Review on Recent Advances in the Energy Efficiency of Machining Processes for Sustainability;Energies;2024-07-25
3. Application of metal cored filler wire for environmental-friendly welding of low alloy steel: experimental investigation and parametric optimization;International Journal on Interactive Design and Manufacturing (IJIDeM);2024-03-26
4. An intelligent hybrid optimization approach to improve the end milling performance of Incoloy 925 based on ANN-NSGA-II-ETOPSIS;International Journal on Interactive Design and Manufacturing (IJIDeM);2024-02-22
5. Modeling surface quality, cost and energy consumption during milling of alloy 2017A: a comparative study integrating GA-ANN and RSM models;International Journal of Modelling and Simulation;2024-02-21
1.学者识别学者识别
2.学术分析学术分析
3.人才评估人才评估
"同舟云学术"是以全球学者为主线，采集、加工和组织学术论文而形成的新型学术文献查询和分析系统，可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容，当前同舟云学术共收录了国内外主流学术期刊6万余种，收集的期刊论文及会议论文总量共计约1.5亿篇，并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询！咨询电话：010-8811{复制后删除}0370
www.globalauthorid.com
TOP
Copyright © 2019-2024 北京同舟云网络信息技术有限公司 京公网安备11010802033243号 京ICP备18003416号-3