Research graph
References from Surface roughness-driven inverse design of milling processes using machine learning. Local targets link to admitted publications; unresolved targets remain external evidence.
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 · External reference
Milled die steel surface roughness correlation with steel sheet friction
10.1016/j.cirp.2010.03.140 · 2010 · External reference
Effect of tool wear on surface roughness in machining of AA7075/10áwt.% SiC composite
2022 · External reference
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 · External reference
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 · External reference
Surface roughness effects on the fatigue strength of additively manufactured Ti-6Al-4V
10.1016/j.ijfatigue.2018.07.013 · 2018 · External reference
Influencing factors and theoretical modeling methods of surface roughness in turning process: State-of-the-art
10.1016/j.ijmachtools.2018.02.001 · 2018 · External reference
Surface plastic deformation and surface topography prediction in peripheral milling with variable pitch end mill
10.1016/j.ijmachtools.2014.11.009 · 2015 · External reference
Unresolved reference
2008 · External reference
3D surface topography simulation and experiments for ball-end nc milling considering dynamic feedrate
10.1016/j.cirpj.2020.05.011 · 2020 · External reference
Modified iterative approach for predicting machined surface topography in ball-end milling operation
10.1007/s00170-021-07245-6 · 2021 · External reference
A review of artificial intelligent methods for machined surface roughness prediction
10.1016/j.triboint.2024.109935 · 2024 · External 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 · 2023 · External reference
Surface roughness prediction as a classification problem using support vector machine
10.1007/s00170-017-0165-9 · 2017 · External 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 · 2019 · External 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 · 2023 · External reference
Prediction of surface roughness in the end milling machining using artificial neural network
10.1016/j.eswa.2009.07.033 · 2010 · External reference
Proper estimation of surface roughness using hybrid intelligence based on artificial neural network and genetic algorithm
10.1016/j.jmapro.2021.08.062 · 2021 · External reference
Knowledge-based neural network for surface roughness prediction of ball-end milling
10.1016/j.ymssp.2023.110282 · 2023 · External reference
Predicting surface roughness in machining: a review
10.1016/s0890-6955(03)00059-2 · 2003 · External reference
Unresolved reference
1998 · External reference
Particle swarm optimization
1995 · External reference
Ensemble learning with a genetic algorithm for surface roughness prediction in multi-jet polishing
10.1016/j.eswa.2022.118024 · 2022 · External reference
Predicting the evolution of sheet metal surface scratching by the technique of artificial intelligence
10.1007/s00170-020-06394-4 · 2021 · External reference
An effective PSO-LSSVM-based approach for surface roughness prediction in high-speed precision milling
10.1109/access.2021.3084617 · 2021 · External reference
Prediction of surface roughness based on fused features and ISSA-DBN in milling of die steel P20
10.1038/s41598-023-42968-4 · 2023 · External reference
Predicting surface roughness in turning complex-structured workpieces using vibration-signal-based gaussian process regression
10.3390/s24072117 · 2024 · External reference
Application of improved fireworks algorithm in grinding surface roughness online monitoring
10.1016/j.jmapro.2021.12.046 · 2022 · External 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 · 2022 · External reference
A novel data augmentation method based on coralgan for prediction of part surface roughness
10.1109/tnnls.2021.3137172 · 2022 · External reference
Milling surface roughness prediction based on physics-informed machine learning
10.3390/s23104969 · 2023 · External reference
Evaluation of turned and milled surfaces roughness using convolutional neural network
10.1016/j.measurement.2020.107860 · 2020 · External reference
Image-based measurement of material roughness using machine learning techniques
10.1016/j.procir.2020.02.292 · 2020 · External reference
Unresolved reference
2019 · External reference
Taking the human out of the loop: A review of Bayesian optimization
10.1109/jproc.2015.2494218 · 2015 · External reference
A comprehensive study on modern optimization techniques for engineering applications
10.1007/s10462-024-10829-9 · 2024 · External reference
Sustainable multi-diamond wire sawing of 4H-SiC: Cooling strategies and hybrid VMD-ML for CO2 reduction and prediction
2025 · External reference
Performance evaluation of machine learning techniques in surface roughness prediction for 3D printed micro-lattice structures
10.1016/j.jmapro.2025.01.082 · 2025 · External reference
Unresolved reference
2026 · External reference
Surface topography and roughness in hole-making by helical milling
10.1007/s00170-012-4419-2 · 2013 · External reference
Surface topography modeling and roughness extraction in helical milling operation
10.1007/s00170-017-1516-2 · 2018 · External reference
Surface topography prediction model in milling of thin-walled parts considering machining deformation
10.3390/ma14247679 · 2021 · External reference
A high efficiency 3D surface topography model for face milling processes
10.1016/j.jmapro.2023.10.026 · 2023 · External reference
Investigation of the impact of face milling parameters on the roughness of the machined surface for 1.4301 steel
10.12913/22998624/170422 · 2023 · External reference
Model for surface topography prediction in peripheral milling considering tool vibration
10.1016/j.cirp.2009.03.084 · 2009 · External reference
Cutting edge geometries
10.1016/j.cirp.2014.05.009 · 2014 · External reference
Manufacturing automation: metal cutting mechanics, machine tool vibrations, and CNC design
10.1115/1.1399383 · 2001 · External reference
Unresolved reference
2006 · External 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 · 2000 · External reference
Unresolved reference
2009 · External reference
Unresolved reference
2015 · External reference
DBSCAN revisited, revisited: why and how you should (still) use DBSCAN
10.1145/3068335 · 2017 · External reference
Unresolved reference
2017 · External reference
10.1145/3292500.3330701
10.1145/3292500.3330701 · External reference
Random forests
10.1023/a:1010933404324 · 2001 · External reference
All models are wrong, but many are useful: Learning a variable’s importance by studying an entire class of prediction models simultaneously
2019 · External reference
Evolutionary techniques in optimizing machining parameters: Review and recent applications (2007–2011)
10.1016/j.eswa.2012.02.109 · 2012 · External 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 · 2010 · External reference
Multiobjective evolutionary algorithms: A survey of the state of the art
10.1016/j.swevo.2011.03.001 · 2011 · External reference
Practical bayesian optimization of machine learning algorithms
2012 · External reference
Unresolved reference
2023 · External reference
Physics-informed machine learning
10.1038/s42254-021-00314-5 · 2021 · External reference
Differentiable expected hypervolume improvement for parallel multi-objective Bayesian optimization
2020 · External reference
Unresolved reference
2012 · External reference
Empirical power-consumption model for material removal in three-axis milling
10.1016/j.jclepro.2014.03.061 · 2014 · External 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 · ExternalCitation · doi-reference
Surface topography and roughness in hole-making by helical milling
10.1007/s00170-012-4419-2 · ExternalCitation · doi-reference
Surface roughness prediction as a classification problem using support vector machine
10.1007/s00170-017-0165-9 · ExternalCitation · doi-reference
Surface topography modeling and roughness extraction in helical milling operation
10.1007/s00170-017-1516-2 · ExternalCitation · 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 · ExternalCitation · doi-reference
Predicting the evolution of sheet metal surface scratching by the technique of artificial intelligence
10.1007/s00170-020-06394-4 · ExternalCitation · doi-reference
Modified iterative approach for predicting machined surface topography in ball-end milling operation
10.1007/s00170-021-07245-6 · ExternalCitation · doi-reference
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 · ExternalCitation · 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 · ExternalCitation · doi-reference
A comprehensive study on modern optimization techniques for engineering applications
10.1007/s10462-024-10829-9 · ExternalCitation · doi-reference
Model for surface topography prediction in peripheral milling considering tool vibration
10.1016/j.cirp.2009.03.084 · ExternalCitation · doi-reference
Milled die steel surface roughness correlation with steel sheet friction
10.1016/j.cirp.2010.03.140 · ExternalCitation · doi-reference
Cutting edge geometries
10.1016/j.cirp.2014.05.009 · ExternalCitation · doi-reference
3D surface topography simulation and experiments for ball-end nc milling considering dynamic feedrate
10.1016/j.cirpj.2020.05.011 · ExternalCitation · doi-reference
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 · ExternalCitation · doi-reference
Prediction of surface roughness in the end milling machining using artificial neural network
10.1016/j.eswa.2009.07.033 · ExternalCitation · doi-reference
Evolutionary techniques in optimizing machining parameters: Review and recent applications (2007–2011)
10.1016/j.eswa.2012.02.109 · ExternalCitation · doi-reference
Ensemble learning with a genetic algorithm for surface roughness prediction in multi-jet polishing
10.1016/j.eswa.2022.118024 · ExternalCitation · doi-reference
Surface roughness effects on the fatigue strength of additively manufactured Ti-6Al-4V
10.1016/j.ijfatigue.2018.07.013 · ExternalCitation · doi-reference
Surface plastic deformation and surface topography prediction in peripheral milling with variable pitch end mill
10.1016/j.ijmachtools.2014.11.009 · ExternalCitation · doi-reference
Influencing factors and theoretical modeling methods of surface roughness in turning process: State-of-the-art
10.1016/j.ijmachtools.2018.02.001 · ExternalCitation · doi-reference
Feasibility analysis of the replacement of the actual machining surface by a 3D numerical simulation rough surface
10.1016/j.ijmecsci.2018.10.023 · ExternalCitation · doi-reference
Empirical power-consumption model for material removal in three-axis milling
10.1016/j.jclepro.2014.03.061 · ExternalCitation · 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 · ExternalCitation · doi-reference
Application of improved fireworks algorithm in grinding surface roughness online monitoring
10.1016/j.jmapro.2021.12.046 · ExternalCitation · 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 · ExternalCitation · doi-reference
A high efficiency 3D surface topography model for face milling processes
10.1016/j.jmapro.2023.10.026 · ExternalCitation · 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 · ExternalCitation · doi-reference
Evaluation of turned and milled surfaces roughness using convolutional neural network
10.1016/j.measurement.2020.107860 · ExternalCitation · doi-reference
Image-based measurement of material roughness using machine learning techniques
10.1016/j.procir.2020.02.292 · ExternalCitation · doi-reference
Multiobjective evolutionary algorithms: A survey of the state of the art
10.1016/j.swevo.2011.03.001 · ExternalCitation · doi-reference
A review of artificial intelligent methods for machined surface roughness prediction
10.1016/j.triboint.2024.109935 · ExternalCitation · doi-reference
Knowledge-based neural network for surface roughness prediction of ball-end milling
10.1016/j.ymssp.2023.110282 · ExternalCitation · doi-reference
Predicting surface roughness in machining: a review
10.1016/s0890-6955(03)00059-2 · ExternalCitation · doi-reference
Random forests
10.1023/a:1010933404324 · ExternalCitation · 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 · ExternalCitation · doi-reference
Physics-informed machine learning
10.1038/s42254-021-00314-5 · ExternalCitation · 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 · ExternalCitation · doi-reference
An effective PSO-LSSVM-based approach for surface roughness prediction in high-speed precision milling
10.1109/access.2021.3084617 · ExternalCitation · doi-reference
Taking the human out of the loop: A review of Bayesian optimization
10.1109/jproc.2015.2494218 · ExternalCitation · doi-reference
A novel data augmentation method based on coralgan for prediction of part surface roughness
10.1109/tnnls.2021.3137172 · ExternalCitation · doi-reference
Manufacturing automation: metal cutting mechanics, machine tool vibrations, and CNC design
10.1115/1.1399383 · ExternalCitation · doi-reference
DBSCAN revisited, revisited: why and how you should (still) use DBSCAN
10.1145/3068335 · ExternalCitation · doi-reference
10.1145/3292500.3330701
10.1145/3292500.3330701 · ExternalCitation · 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 · ExternalCitation · 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 · ExternalCitation · doi-reference
Surface topography prediction model in milling of thin-walled parts considering machining deformation
10.3390/ma14247679 · ExternalCitation · doi-reference
Milling surface roughness prediction based on physics-informed machine learning
10.3390/s23104969 · ExternalCitation · doi-reference
Predicting surface roughness in turning complex-structured workpieces using vibration-signal-based gaussian process regression
10.3390/s24072117 · ExternalCitation · doi-reference
