ANN Surface Roughness Optimization of AZ61 Magnesium Alloy Finish Turning: Minimum Machining Times at Prime Machining Costs
出版年份 2018 全文链接
标题
ANN Surface Roughness Optimization of AZ61 Magnesium Alloy Finish Turning: Minimum Machining Times at Prime Machining Costs
作者
关键词
-
出版物
Materials
Volume 11, Issue 5, Pages 808
出版商
MDPI AG
发表日期
2018-05-17
DOI
10.3390/ma11050808
参考文献
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注意:仅列出部分参考文献,下载原文获取全部文献信息。- Surface quality and topographic inspection of variable compliance part after precise turning
- (2018) P. Nieslony et al. APPLIED SURFACE SCIENCE
- An approach to cleaner production for machining hardened steel using different cooling-lubrication conditions
- (2018) Mozammel Mia et al. JOURNAL OF CLEANER PRODUCTION
- Minimization of turning time for high-strength steel with a given surface roughness using the Edgeworth–Pareto optimization method
- (2017) A. T. Abbas et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth
- (2017) D. Yu. Pimenov et al. JOURNAL OF INTELLIGENT MANUFACTURING
- Modeling and optimization in dry face milling of X2CrNi18-9 austenitic stainless steel using RMS and desirability approach
- (2017) Abdel-Ali Selaimia et al. MEASUREMENT
- Finish turning of Ti-6Al-4V with the atomization-based cutting fluid (ACF) spray system
- (2017) Chandra Nath et al. Journal of Manufacturing Processes
- Mono-objective and multi-objective optimization of performance parameters in high pressure coolant assisted turning of Ti-6Al-4V
- (2016) Mozammel Mia et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Design optimization for minimum technological parameters when dry turning of AISI D3 steel using Taguchi method
- (2016) Oussama Zerti et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Performance prediction of high-pressure coolant assisted turning of Ti-6Al-4V
- (2016) Mozammel Mia et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Optimization of surface roughness and cutting temperature in high-pressure coolant-assisted hard turning using Taguchi method
- (2016) Mozammel Mia et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Machining of aluminum alloys: a review
- (2016) Mário C. Santos et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- A comparison of machine learning methods for cutting parameters prediction in high speed turning process
- (2016) Zoran Jurkovic et al. JOURNAL OF INTELLIGENT MANUFACTURING
- Modeling and optimization of tool vibration and surface roughness in boring of steel using RSM, ANN and SVM
- (2016) K. Venkata Rao et al. JOURNAL OF INTELLIGENT MANUFACTURING
- Online non-contact surface finish measurement in machining using graph theory-based image analysis
- (2016) M. Samie Tootooni et al. JOURNAL OF MANUFACTURING SYSTEMS
- Prediction of surface roughness in hard turning under high pressure coolant using Artificial Neural Network
- (2016) Mozammel Mia et al. MEASUREMENT
- Surface roughness prediction for the milling of Ti–6Al–4V ELI alloy with the use of statistical and soft computing techniques
- (2016) N.E. Karkalos et al. MEASUREMENT
- Using artificial neural networks for the prediction of dimensional error on inclined surfaces manufactured by ball-end milling
- (2015) Álvar Arnaiz-González et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Influence of aluminum content on twinning and texture development of cast Mg–Al–Zn alloy during compression
- (2015) N. Tahreen et al. JOURNAL OF ALLOYS AND COMPOUNDS
- Optimisation of turning parameters by integrating genetic algorithm with support vector regression and artificial neural networks
- (2014) Amit Kumar Gupta et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Microstructure evolution and tensile mechanical properties of thixoformed AZ61 magnesium alloy prepared by squeeze casting
- (2014) Tian CHEN et al. TRANSACTIONS OF NONFERROUS METALS SOCIETY OF CHINA
- Optimization of turning process using artificial intelligence technology
- (2013) Rasool Mokhtari Homami et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Application of artificial neural network and optimization algorithms for optimizing surface roughness, tool life and cutting forces in turning operation
- (2013) F. Jafarian et al. Journal of Mechanical Science and Technology
- Flow stress behavior of AZ61 magnesium alloy during hot compression deformation
- (2013) L.C. Tsao et al. MATERIALS & DESIGN
- Hot deformation behavior of hot-rolled AZ31 and AZ61 magnesium alloys
- (2013) M.L. Olguín-González et al. MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING
- On the prediction of surface roughness in the hard turning based on cutting parameters and tool vibrations
- (2013) Zahia Hessainia et al. MEASUREMENT
- Optimization of Radial Basis Function neural network employed for prediction of surface roughness in hard turning process using Taguchi’s orthogonal arrays
- (2012) Fabrício José Pontes et al. EXPERT SYSTEMS WITH APPLICATIONS
- Prediction and analysis of surface roughness characteristics of a non-ferrous material using ANN in CNC turning
- (2011) Chinnasamy Natarajan et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Machined surface quality prediction models based on moving least squares and moving least absolute deviations methods
- (2011) Ilija Svalina et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Prediction and optimisation models for turning operations
- (2008) A. M. A. Al-Ahmari INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
- Optimization of machining parameters of Al/SiC-MMC with ANOVA and ANN analysis
- (2008) N. Muthukrishnan et al. JOURNAL OF MATERIALS PROCESSING TECHNOLOGY
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