Application of ANN in milling process: a review

In recent years the trends were towards modeling of machining using artificial intelligence. ANN is considered one of the important methods of artificial intelligence in the modeling of nonlinear problems like machining processes. Artificial neural networks show good capability in prediction and optimization of machining processes compared with traditional methods. In view of the importance of artificial neural networks in machining, this paper is an attempt to review the previous studies and investigations on the application of artificial neural networks in the milling process for the last decade.

• Publication year

  2011

• Venue

  Modelling and Simulation in Engineering

• Publication date

  Unknown publication date

• Fields of study

  Computer Science, Engineering

• Identifiers
  DOI 10.1155/2011/696275
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Use of NC kernel data for surface roughness monitoring in milling operations

  2011cited by this paper
• Optimum surface roughness prediction in face milling by using neural network and harmony search algorithm

  2011cited by this paper
• Review: Neural control strategy of constant cutting force system in end milling

  2011cited by this paper
• Surface roughness monitoring application based on artificial neural networks for ball-end milling operations

  2011cited by this paper
• Prediction of surface roughness in CNC end milling by machine vision system using artificial neural network based on 2D Fourier transform

  2011cited by this paper
• Prediction of surface roughness in the end milling machining using Artificial Neural Network

  2010cited by this paper
• Flank wear regulation using artificial neural networks

  2010cited by this paper
• An in-process adaptive control of surface roughness in end milling operations

  2010cited by this paper
• Surface Roughness Prediction for CNC Milling Process using Artificial Neural Network

  2010cited by this paper
• Artificial Neural Networks for Surface Roughness Prediction when Face Milling Al 7075-T7351

  2010cited by this paper
• A new artificial neural network approach to modeling ball-end milling

  2010cited by this paper
• Study on the cutting force prediction of supercritical material milling

  2010cited by this paper
• An integrated study of surface roughness for modelling and optimization of cutting parameters during end milling operation

  2009cited by this paper
• Optimization Using Neural Network Modeling and Swarm Intelligence in the Machining of Titanium (Ti 6Al 4V) Alloy

  2009cited by this paper
• Artificial Neural Network for Predicting Machining Performance of Uncoated Carbide (WC-Co) in Milling Machining Operation

  2009cited by this paper
• Application of Regression and ANN Techniques for Modeling of the Surface Roughness in End Milling Machining Process

  2009cited by this paper
• Milling Wear Monitoring Study Based on Cutting Power

  2009cited by this paper
• Cutting Force Prediction of High-Speed Milling Hardened Steel Based on BP Neural Networks

  2009cited by this paper
• Cutting force signal pattern recognition using hybrid neural network in end milling

  2009cited by this paper
• The role of stepover ratio in prediction of surface roughness in flat end milling

  2009cited by this paper
• Prediction of tool wear using regression and ANN models in end-milling operation

  2008cited by this paper
• Artificial-neural-networks-based surface roughness Pokayoke system for end-milling operations

  2008cited by this paper
• Prediction of surface roughness profiles for milled surfaces using an artificial neural network and fractal geometry approach

  2008cited by this paper
• Surface roughness modelling in finish face milling under MQL and dry cutting conditions

  2008cited by this paper
• Development of an artificial neural network algorithm for predicting the surface roughness in end milling of Inconel 718 alloy

  2008cited by this paper
• Study on Tool Wear Monitoring Based on Multi-source Information Fusion

  2008cited by this paper
• Mill-cut: a neural network system for the prediction of thermo-mechanical loads induced in end-milling operations

  2008cited by this paper
• Comparison of response surface model with neural network in determining the surface quality of moulded parts

  2007cited by this paper
• Modeling of cutting forces as function of cutting parameters for face milling of satellite 6 using an artificial neural network

  2007cited by this paper
• Estimation of tool wear during CNC milling using neural network-based sensor fusion

  2007cited by this paper
• Selection of optimal cutting conditions by using GONNS

  2006cited by this paper
• Surface roughness modeling of high speed machining TC4 based on artificial neural network method

  2006cited by this paper
• Adaptive Force Control in High-Speed Machining by Using a System of Neural Networks

  2006cited by this paper
• The efficacy of back propagation neural network with delta bar delta learning in predicting the wear of carbide inserts in face milling

  2006influential reference
• A generalized neural network model of ball-end milling force system

  2006cited by this paper
• Prediction of minimum surface roughness in end milling mold parts using neural network and genetic algorithm

  2006cited by this paper
• Optimization of CNC ball end milling : a neural network-based model

  2005cited by this paper
• Optimum surface roughness in end milling Inconel 718 by coupling neural network model and genetic algorithm

  2005cited by this paper
• An artificial-neural-networks-based in-process tool wear prediction system in milling operations

  2005cited by this paper
• Tool cutting force modeling in ball-end milling using multilevel perceptron

  2004cited by this paper
• A Hybrid Radial Basis Function Neural Network for Dimensional Error Prediction in End Milling

  2004cited by this paper
• Intelligent process supervision for predicting tool wear in machining processes

  2003cited by this paper
• Optimization in ball-end milling by using adaptive neural controller

  2003cited by this paper
• Selecting an artificial neural network for efficient modeling and accurate simulation of the milling process

  2002cited by this paper
• Prediction of surface roughness in CNC face milling using neural networks and Taguchi's design of experiments

  2002cited by this paper
• NC end milling optimiza-tion using evolutionary computation

  2002cited by this paper
• A Novel Artificial Neural Networks Force Model for End Milling

  2001cited by this paper
• Applicability of the modified back-propagation algorithm in tool condition monitoring for faster convergence

  2000cited by this paper
• An in-process surface recognition system based on neural networks in end milling cutting operations

  1999cited by this paper
• Modelling and simulation in engineering

  1988cited by this paper
• Surface Roughness Prediction for CNC Milling Process using Artificial Neural Network

  year unknowncited by this paper

• Nanofinishing of Aluminum Sheet Using CNC Fixed Abrasive Lapping

  2025cites this paper
• Predictive modeling and analysis of machinability in EN 31 steel

  2025cites this paper
• Analysis and Prediction of Temperature Using an Artificial Neural Network Model for Milling Glass Fiber Reinforced Polymer Composites

  2024cites this paper
• A review of automated cutting tool selection methods

  2024cites this paper
• Applications of artificial neural networks in machining processes: a comprehensive review

  2024cites this paper
• Improving milling force predictions: A hybrid approach integrating physics-based simulation and machine learning for remarkable accuracy across diverse unseen materials and tool types

  2024cites this paper
• A review of recent advances and applications of machine learning in tribology.

  2023cites this paper
• Deep learning-based cutting force prediction for machining process using monitoring data

  2023cites this paper
• Prediction of Surface Roughness of Monel k 500 Super Alloy by Using Artificial Neural Network

  2023cites this paper
• Modeling of Dry EDM process parameters during machining of Inconel 718 using artificial neural network

  2023cites this paper
• Artificial Neural Network System for Predicting Cutting Forces in Helical-End Milling of Laser-Deposited Metal Materials

  2023cites this paper
• Acoustic Sensing and Supervised Machine Learning for In Situ Classification of Semi-Autogenous (SAG) Mill Feed Size Fractions Using Different Feature Extraction Techniques

  2023cites this paper
• Evaluating the surface characteristics of Stellite -6 super alloy during end milling using titanium nitride coated binary inserts

  2023cites this paper
• A novel ensemble method based on the SBLMD-ANN-MOPSO approach for predicting milling stability regimes

  2022cites this paper
• Modelling and Analysis of Surface Roughness Using the Cascade Forward Neural Network (CFNN) in Turning of Inconel 625

  2022cites this paper
• Towards advanced prediction and control of machining distortion: a comprehensive review

  2022cites this paper
• Application of Artificial Neural Networks and Genetic Algorithm for optimizing Process parameters in Pocket Milling of AA7075

  2022cites this paper
• Extenuating Chatter Vibration in Milling Process Using a New Ensemble Approach

  2022cites this paper
• Predictive Information Modeling: Machine Learning Strategies for Material Uncertainty

  2021cites this paper
• ANN prediction of particle flow characteristics in a drum based on synthetic acoustic signals from DEM simulations

  2021cites this paper
• Artificial intelligence techniques for implementation of intelligent machining

  2021cites this paper
• Evolutionary Optimization of Machining Parameters Based on Surface Roughness in End Milling of Hot Rolled Steel

  2021cites this paper
• Nanocomposite-Based Humidity Sensor

  2020cites this paper
• Multi-response optimization of carbon fiber reinforced polymer (CFRP) drilling using back propagation neural network-particle swarm optimization (BPNN-PSO)

  2020cites this paper
• Neuro-mechanistic model for cutting force prediction in helical end milling of metal materials layered in multiple directions

  2020cites this paper
• Implementation Criteria for Intelligent Systems in Motor Production Line Process Management

  2020cites this paper
• Learning-Based Prediction of Pose-Dependent Dynamics

  2020cites this paper
• Modeling and optimizing of cutting force and surface roughness in milling process of Inconel 738 using hybrid ANN and GA

  2019cites this paper
• Re/Learning the Wheel: Methods to Utilize Neural Networks as Design Tools for Doubly Curved Metal Surfaces

  2019cites this paper
• Machine learning-based instantaneous cutting force model for end milling operation

  2019cites this paper
• Machine Learning in Electric Motor Production - Potentials, Challenges and Exemplary Applications

  2019cites this paper
• Machine Learning in Production – Potentials, Challenges and Exemplary Applications

  2019cites this paper
• Electric Motor Production 4.0 – Application Potentials of Industry 4.0 Technologies in the Manufacturing of Electric Motors

  2018cites this paper
• Tool Monitoring – A Scalable Learning Approach to Estimate Cutting Tool Conditions with Machine-Internal Data in Job Shop Production of a Milling Process

  2018cites this paper
• Efficient Optimization of Neural Network using Taguchi-grey Relational Analysis with Signal-to-Noise Ratio Approach for 2.5D End Milling Process

  2018cites this paper
• Adaptive Robotic Carving: Training Methods for the Integration of Material Performances in Timber Manufacturing

  2018cites this paper
• Modelling, prediction and analysis of surface roughness in turning process with carbide tool when cutting steel C38 using artificial neural network

  2017cites this paper
• Adaptive Robotic Training Methods for Subtractive Manufacturing

  2017cites this paper
• Optimization design of RBF‐ARX model and application research on flatness control system

  2017cites this paper
• Milling tool wear diagnosis by feed motor current signal using an artificial neural network

  2016cites this paper
• Prediction of multi-response parameters in material removal processes using soft computing — A review

  2016cites this paper
• Soft computing techniques during drilling of bi-directional carbon fiber reinforced composite

  2016cites this paper
• Monitoring of tool wear using measured machining forces and neuro-fuzzy modelling approaches during machining of GFRP composites

  2015cites this paper
• Optimization of neural network parameters using Grey–Taguchi methodology for manufacturing process applications

  2015cites this paper
• Prediction of tool life in end milling of Ti-6Al-4V alloy using artificial neural network and multiple regression models

  2013cites this paper
• Application of Artificial Neural Network and Response Surface Methodology for Achieving Desired Surface Roughness in End Milling Process of Ductile Iron Grade 80-55-06

  2013cites this paper
• Optimized Prediction and Modeling Under End Milling Machining By ANOVA And Artificial Neural Network

  2013cites this paper
• Use of Neural Networks in Prediction and Simulation of Steel Surface Roughness

  2013cites this paper
• Modelling of tool life in end milling of Ti6Al4V alloy using artificial neural networks

  2012cites this paper
• Application of artificial neural networks in prediction tool life of PVD coated carbide when end milling of TI6aL4v alloy

  2012cites this paper
• Application of artificial intelligence techniques in incremental forming: a state-of-the-art review

  year unknowncites this paper