Neural Network Modeling and Prediction of Surface Roughness in Machining Aluminum Alloys

Journals Menu

Follow SCIRP

	customer@scirp.org
	+86 18163351462(WhatsApp)
	1655362766

	Paper Publishing WeChat

Journal of Computer and Communications

ISSN Print: 2327-5219
ISSN Online: 2327-5227
www.scirp.org/journal/jcc
E-mail: jcc@scirp.org

Google-based Impact Factor: 1.98
Citations

Journals Menu

"Neural Network Modeling and Prediction of Surface Roughness in Machining Aluminum Alloys"
written by N. Fang, N. Fang, P. Srinivasa Pai, N. Edwards,
published by Journal of Computer and Communications, Vol.4 No.5, 2016

has been cited by the following article(s):

Google Scholar
CrossRef

[1]	Development of Surface Roughness Prediction and Monitoring System in Milling Process
	Engineering, Technology & Applied …, 2024

[2]	Roughness prediction of end milling surface for behavior mapping of digital twined machine tools
	Digital Twin, 2024

[3]	Machine Learning and Artificial Intelligence in CNC Machine Tools, A
	Sustainable Manufacturing and Service Economics, 2023

[4]	Machine Learning and Artificial Intelligence in CNC Machine Tools, A Review
	Sustainable Manufacturing and Service …, 2023

[5]	Machine Learning-Based Modeling and Optimization in Hard Turning of Aisi d6 Steel with Advanced AlTiSiN-COATED Carbide Inserts to Predict Surface Roughness …
	Surface Review and …, 2022

[6]	Soft computing techniques for modelling and multi-objective optimization of magnetic field assisted powder mixed EDM process
	Neural Computing and Applications, 2022

[7]	Machine learning based modelling and optimization in hard turning of AISI D6 steel with newly developed AlTiSiN coated carbide tool
	arXiv preprint arXiv …, 2022

[8]	Machine Learning-based modeling and optimization in hard turning of AISI D6 steel with advanced AlTiSiN-coated carbide inserts to predict surface roughness and …
	Surface Review and …, 2022

[9]	Neural-Network-Based Approaches for Optimization of Machining Parameters Using Small Dataset
	Materials, 2022

[10]	Artificial intelligence-based surface roughness estimation modelling for milling of AA6061 alloy
	2021

[11]	Optimal cutting state predictions in internal turning operation with nano-SiC/GFRE composite layered boring tools
	2021

[12]	An improved algorithm to predict the mechanical properties of nuclear grade 316 stainless steel under elevated-temperature liquid sodium
	2021

[13]	Artificial Intelligence‐Based Surface Roughness Estimation Modelling for Milling of AA6061 Alloy
	Advances in Materials …, 2021

[14]	Research Article Artificial Intelligence-Based Surface Roughness Estimation Modelling for Milling of AA6061 Alloy
	2021

[15]	Data‐driven framework for the prediction of cutting force in turning
	IET Collaborative Intelligent …, 2020

[16]	Data-driven framework for the prediction of cutting force in turning
	2020

[17]	Application of Fuzzy Logic in the Analysis of Surface Roughness of Thin-Walled Aluminum Parts
	2019

[18]	A Review of Artificial Intelligence Technologies to Achieve Machining Objectives
	Cognitive Social Mining Applications in Data Analytics and Forensics, 2019

[19]	A Comparative Study between Regression and Neural Networks for Modeling Al6082-T6 Alloy Drilling
	2019

[20]	Research on Establishing Prediction Model for Aerospace Aluminum Alloy Milling Force with the Help of RBF Neural Network
	2018

[21]	Optimization of Cutting Parameters in Cs in CNC Turning of AISI 304 & AISI 316 Stainless Steel
	International journal of Trend in Scientific Research and Development, 2018

[22]	MONITORING OF BIOSURFACTANT PRODUCTION BY Bacillus subtilis USING BEET PEEL AS CULTURE MEDIUM VIA THE DEVELOPMENT OF A …
	2018

[23]	Dynamic Bayesian Network-based Approach by Integrating Sensor Deployment for Machining Process Monitoring
	2018

[24]	Modeling for Prediction of Surface Roughness and Experimental Research in Ultra-Precision Flycutting Machining
	2018

[25]	Study on Parameter Optimization of Cutting Surface Roughness of Aluminum Alloy
	2018

[26]	Monitoring of biosurfactant production by Bacillus subtilis using beet peel as culture medium via the development of a neural soft-sensor in an electronic …
	2018

[27]	Monitoring of biosurfactant production by Bacillus subtilis using beet peel as culture medium via the development of a neural soft-sensor in an electronic spreadsheet
	2018

[28]	Küreselleştirme isil İşlemi uygulanmiş AISI 1050 Çeliğinin yüzey pürüzlülük değerlerinin yapay sinir ağlari ile modellenmesi
	2017

[1]	Ann-based predictive model of geometrical deviations in dry turning of AA7075 (Al-Zn) alloy Measurement, 2025 DOI:10.1016/j.measurement.2024.116355

[2]	Roughness prediction of end milling surface for behavior mapping of digital twined machine tools Digital Twin, 2024 DOI:10.12688/digitaltwin.17819.2

[3]	Development of Surface Roughness Prediction and Monitoring System in Milling Process Engineering, Technology & Applied Science Research, 2024 DOI:10.48084/etasr.6664

[4]	Machine learning and artificial intelligence in CNC machine tools, A review Sustainable Manufacturing and Service Economics, 2023 DOI:10.1016/j.smse.2023.100009

[5]	Roughness prediction of end milling surface for behavior mapping of digital twined machine tools Digital Twin, 2023 DOI:10.12688/digitaltwin.17819.1

[6]	Roughness prediction of end milling surface for behavior mapping of digital twined machine tools Digital Twin, 2023 DOI:10.12688/digitaltwin.17819.1

[7]	Machine learning and artificial intelligence in CNC machine tools, A review Sustainable Manufacturing and Service Economics, 2023 DOI:10.1016/j.smse.2023.100009

[8]	Roughness prediction of end milling surface for behavior mapping of digital twined machine tools Digital Twin, 2023 DOI:10.12688/digitaltwin.17819.1

[9]	Soft computing techniques for modelling and multi-objective optimization of magnetic field assisted powder mixed EDM process Neural Computing and Applications, 2022 DOI:10.1007/s00521-022-07498-6

[10]	MACHINE LEARNING-BASED MODELING AND OPTIMIZATION IN HARD TURNING OF AISI D6 STEEL WITH ADVANCED AlTiSiN-COATED CARBIDE INSERTS TO PREDICT SURFACE ROUGHNESS AND OTHER MACHINING CHARACTERISTICS Surface Review and Letters, 2022 DOI:10.1142/S0218625X22501372

[11]	Neural-Network-Based Approaches for Optimization of Machining Parameters Using Small Dataset Materials, 2022 DOI:10.3390/ma15030700

[12]	Neural-Network-Based Approaches for Optimization of Machining Parameters Using Small Dataset Materials, 2022 DOI:10.3390/ma15030700

[13]	Artificial Intelligence‐Based Surface Roughness Estimation Modelling for Milling of AA6061 Alloy Advances in Materials Science and Engineering, 2021 DOI:10.1155/2021/5576600

[14]	An improved algorithm to predict the mechanical properties of nuclear grade 316 stainless steel under elevated-temperature liquid sodium Journal of Nuclear Science and Technology, 2021 DOI:10.1080/00223131.2021.1918591

[15]	Artificial Intelligence-Based Surface Roughness Estimation Modelling for Milling of AA6061 Alloy Advances in Materials Science and Engineering, 2021 DOI:10.1155/2021/5576600

[16]	An improved algorithm to predict the mechanical properties of nuclear grade 316 stainless steel under elevated-temperature liquid sodium Journal of Nuclear Science and Technology, 2021 DOI:10.1080/00223131.2021.1918591

[17]	Experimental study and analysis of surface roughness of the flow formed H30 alloy tubes Materials Today: Proceedings, 2021 DOI:10.1016/j.matpr.2020.09.647

[18]	Data-driven framework for the prediction of cutting force in turning IET Collaborative Intelligent Manufacturing, 2020 DOI:10.1049/iet-cim.2019.0055

[19]	Data‐driven framework for the prediction of cutting force in turning IET Collaborative Intelligent Manufacturing, 2020 DOI:10.1049/iet-cim.2019.0055

[20]	A Comparative Study between Regression and Neural Networks for Modeling Al6082-T6 Alloy Drilling Machines, 2019 DOI:10.3390/machines7010013

[21]	Cognitive Social Mining Applications in Data Analytics and Forensics Advances in Social Networking and Online Communities, 2019 DOI:10.4018/978-1-5225-7522-1.ch008

[22]	A Comparative Study between Regression and Neural Networks for Modeling Al6082-T6 Alloy Drilling Machines, 2019 DOI:10.3390/machines7010013

[23]	Application of Fuzzy Logic in the Analysis of Surface Roughness of Thin-Walled Aluminum Parts International Journal of Precision Engineering and Manufacturing, 2019 DOI:10.1007/s12541-019-00229-3

[24]	Research on Establishing Prediction Model for Aerospace Aluminum Alloy Milling Force with the Help of RBF Neural Network Proceedings of the International Conference on Information Technology and Electrical Engineering 2018 - ICITEE '18, 2018 DOI:10.1145/3148453.3306259

[25]	MONITORING OF BIOSURFACTANT PRODUCTION BY Bacillus subtilis USING BEET PEEL AS CULTURE MEDIUM VIA THE DEVELOPMENT OF A NEURAL SOFT-SENSOR IN AN ELECTRONIC SPREADSHEET Brazilian Journal of Chemical Engineering, 2018 DOI:10.1590/0104-6632.20180354s20160664

[26]	Dynamic Bayesian Network-Based Approach by Integrating Sensor Deployment for Machining Process Monitoring IEEE Access, 2018 DOI:10.1109/ACCESS.2018.2846251

Follow SCIRP

	customer@scirp.org
	+86 18163351462(WhatsApp)
	1655362766

	Paper Publishing WeChat

Journal of Computer and Communications

Journal of Computer and Communications

Journals Menu

Home

About SCIRP

Service

Policies