Abstract
No public abstract is available from the verified source records.
Hadi Bakhshan, Sima Farshbaf, Fernando Rastellini, Josep Maria Carbonell
Abstract
Authors
Institutions
Provenance
No local reference links have been materialized yet.
No local citing links have been materialized yet.
A review on theories/methods to obtain surface topography and analysis of corresponding affecting factors in the milling process
10.1007/s00170-023-11723-4 · 2023
Milled die steel surface roughness correlation with steel sheet friction
10.1016/j.cirp.2010.03.140 · 2010
Effect of tool wear on surface roughness in machining of AA7075/10áwt.% SiC composite
2022
A comprehensive research on wear resistance of GH4169 superalloy in longitudinal-torsional ultrasonic vibration side milling with tool wear and surface quality
10.1016/j.cja.2023.07.009 · 2024
Feasibility analysis of the replacement of the actual machining surface by a 3D numerical simulation rough surface
10.1016/j.ijmecsci.2018.10.023 · 2019
Surface roughness effects on the fatigue strength of additively manufactured Ti-6Al-4V
10.1016/j.ijfatigue.2018.07.013 · 2018
Influencing factors and theoretical modeling methods of surface roughness in turning process: State-of-the-art
10.1016/j.ijmachtools.2018.02.001 · 2018
Surface plastic deformation and surface topography prediction in peripheral milling with variable pitch end mill
10.1016/j.ijmachtools.2014.11.009 · 2015
Unresolved referenced work
crossref
Confidence 100%
ror
Confidence 99%
openalex
Confidence 95%
datacite
Confidence 0%
2008
3D surface topography simulation and experiments for ball-end nc milling considering dynamic feedrate
10.1016/j.cirpj.2020.05.011 · 2020
Modified iterative approach for predicting machined surface topography in ball-end milling operation
10.1007/s00170-021-07245-6 · 2021
A review of artificial intelligent methods for machined surface roughness prediction
10.1016/j.triboint.2024.109935 · 2024
Surface roughness and surface crack length prediction using supervised machine learning–based approach of electrical discharge machining of deep cryogenically treated NiTi, NiCu, and BeCu alloys
10.1007/s00170-023-12269-1 · 2023
Surface roughness prediction as a classification problem using support vector machine
10.1007/s00170-017-0165-9 · 2017
The use of support vector machine, neural network, and regression analysis to predict and optimize surface roughness and cutting forces in milling
10.1007/s00170-019-04227-7 · 2019
Explainable artificial intelligence (XAI) and supervised machine learning-based algorithms for prediction of surface roughness of additively manufactured polylactic acid (PLA) specimens
10.3390/applmech4020034 · 2023
Prediction of surface roughness in the end milling machining using artificial neural network
10.1016/j.eswa.2009.07.033 · 2010
Proper estimation of surface roughness using hybrid intelligence based on artificial neural network and genetic algorithm
10.1016/j.jmapro.2021.08.062 · 2021
Knowledge-based neural network for surface roughness prediction of ball-end milling
10.1016/j.ymssp.2023.110282 · 2023
Predicting surface roughness in machining: a review
10.1016/s0890-6955(03)00059-2 · 2003
Unresolved referenced work
1998
Particle swarm optimization
1995
Ensemble learning with a genetic algorithm for surface roughness prediction in multi-jet polishing
10.1016/j.eswa.2022.118024 · 2022
Predicting the evolution of sheet metal surface scratching by the technique of artificial intelligence
10.1007/s00170-020-06394-4 · 2021
An effective PSO-LSSVM-based approach for surface roughness prediction in high-speed precision milling
10.1109/access.2021.3084617 · 2021
Prediction of surface roughness based on fused features and ISSA-DBN in milling of die steel P20
10.1038/s41598-023-42968-4 · 2023
Predicting surface roughness in turning complex-structured workpieces using vibration-signal-based gaussian process regression
10.3390/s24072117 · 2024
Application of improved fireworks algorithm in grinding surface roughness online monitoring
10.1016/j.jmapro.2021.12.046 · 2022
Roughness prediction model of milling noise-vibration-surface texture multi-dimensional feature fusion for N6 nickel metal
10.1016/j.jmapro.2022.04.055 · 2022
A novel data augmentation method based on coralgan for prediction of part surface roughness
10.1109/tnnls.2021.3137172 · 2022
Milling surface roughness prediction based on physics-informed machine learning
10.3390/s23104969 · 2023
Evaluation of turned and milled surfaces roughness using convolutional neural network
10.1016/j.measurement.2020.107860 · 2020
Image-based measurement of material roughness using machine learning techniques
10.1016/j.procir.2020.02.292 · 2020
Unresolved referenced work
2019
Taking the human out of the loop: A review of Bayesian optimization
10.1109/jproc.2015.2494218 · 2015
A comprehensive study on modern optimization techniques for engineering applications
10.1007/s10462-024-10829-9 · 2024
Sustainable multi-diamond wire sawing of 4H-SiC: Cooling strategies and hybrid VMD-ML for CO2 reduction and prediction
2025
Performance evaluation of machine learning techniques in surface roughness prediction for 3D printed micro-lattice structures
10.1016/j.jmapro.2025.01.082 · 2025
Unresolved referenced work
2026
Surface topography and roughness in hole-making by helical milling
10.1007/s00170-012-4419-2 · 2013
Empirical power-consumption model for material removal in three-axis milling
10.1016/j.jclepro.2014.03.061 · doi-reference
Physics-informed machine learning
10.1038/s42254-021-00314-5 · doi-reference
Multiobjective evolutionary algorithms: A survey of the state of the art
10.1016/j.swevo.2011.03.001 · doi-reference
Survey of modeling and optimization strategies to solve high-dimensional design problems with computationally-expensive black-box functions
10.1007/s00158-009-0420-2 · doi-reference
Evolutionary techniques in optimizing machining parameters: Review and recent applications (2007–2011)
10.1016/j.eswa.2012.02.109 · doi-reference
Random forests
10.1023/a:1010933404324 · doi-reference
10.1145/3292500.3330701
10.1145/3292500.3330701 · doi-reference
DBSCAN revisited, revisited: why and how you should (still) use DBSCAN
10.1145/3068335 · doi-reference
A comparison of three methods for selecting values of input variables in the analysis of output from a computer code
10.1080/00401706.2000.10485979 · doi-reference
Manufacturing automation: metal cutting mechanics, machine tool vibrations, and CNC design
10.1115/1.1399383 · doi-reference
Cutting edge geometries
10.1016/j.cirp.2014.05.009 · doi-reference
Model for surface topography prediction in peripheral milling considering tool vibration
10.1016/j.cirp.2009.03.084 · doi-reference
Investigation of the impact of face milling parameters on the roughness of the machined surface for 1.4301 steel
10.12913/22998624/170422 · doi-reference
A high efficiency 3D surface topography model for face milling processes
10.1016/j.jmapro.2023.10.026 · doi-reference
Surface topography prediction model in milling of thin-walled parts considering machining deformation
10.3390/ma14247679 · doi-reference
Surface topography modeling and roughness extraction in helical milling operation
10.1007/s00170-017-1516-2 · doi-reference
Performance evaluation of machine learning techniques in surface roughness prediction for 3D printed micro-lattice structures
10.1016/j.jmapro.2025.01.082 · doi-reference
A comprehensive study on modern optimization techniques for engineering applications
10.1007/s10462-024-10829-9 · doi-reference
Taking the human out of the loop: A review of Bayesian optimization
10.1109/jproc.2015.2494218 · doi-reference
Image-based measurement of material roughness using machine learning techniques
10.1016/j.procir.2020.02.292 · doi-reference
Evaluation of turned and milled surfaces roughness using convolutional neural network
10.1016/j.measurement.2020.107860 · doi-reference
Milling surface roughness prediction based on physics-informed machine learning
10.3390/s23104969 · doi-reference
A novel data augmentation method based on coralgan for prediction of part surface roughness
10.1109/tnnls.2021.3137172 · doi-reference
Roughness prediction model of milling noise-vibration-surface texture multi-dimensional feature fusion for N6 nickel metal
10.1016/j.jmapro.2022.04.055 · doi-reference
Application of improved fireworks algorithm in grinding surface roughness online monitoring
10.1016/j.jmapro.2021.12.046 · doi-reference
Predicting surface roughness in turning complex-structured workpieces using vibration-signal-based gaussian process regression
10.3390/s24072117 · doi-reference
Prediction of surface roughness based on fused features and ISSA-DBN in milling of die steel P20
10.1038/s41598-023-42968-4 · doi-reference
An effective PSO-LSSVM-based approach for surface roughness prediction in high-speed precision milling
10.1109/access.2021.3084617 · doi-reference
Predicting the evolution of sheet metal surface scratching by the technique of artificial intelligence
10.1007/s00170-020-06394-4 · doi-reference
Ensemble learning with a genetic algorithm for surface roughness prediction in multi-jet polishing
10.1016/j.eswa.2022.118024 · doi-reference
Predicting surface roughness in machining: a review
10.1016/s0890-6955(03)00059-2 · doi-reference
Knowledge-based neural network for surface roughness prediction of ball-end milling
10.1016/j.ymssp.2023.110282 · doi-reference
Proper estimation of surface roughness using hybrid intelligence based on artificial neural network and genetic algorithm
10.1016/j.jmapro.2021.08.062 · doi-reference
Prediction of surface roughness in the end milling machining using artificial neural network
10.1016/j.eswa.2009.07.033 · doi-reference
Explainable artificial intelligence (XAI) and supervised machine learning-based algorithms for prediction of surface roughness of additively manufactured polylactic acid (PLA) specimens
10.3390/applmech4020034 · doi-reference
The use of support vector machine, neural network, and regression analysis to predict and optimize surface roughness and cutting forces in milling
10.1007/s00170-019-04227-7 · doi-reference
Surface roughness prediction as a classification problem using support vector machine
10.1007/s00170-017-0165-9 · doi-reference
Surface roughness and surface crack length prediction using supervised machine learning–based approach of electrical discharge machining of deep cryogenically treated NiTi, NiCu, and BeCu alloys
10.1007/s00170-023-12269-1 · doi-reference
A review of artificial intelligent methods for machined surface roughness prediction
10.1016/j.triboint.2024.109935 · doi-reference
Modified iterative approach for predicting machined surface topography in ball-end milling operation
10.1007/s00170-021-07245-6 · doi-reference