Prediction and evaluation of surface roughness with hybrid kernel extreme learning machine and monitored tool wear

Accurate estimation of tool wear levels during milling, drilling and turning operations by designing novel hyperparameter tuned models based on LightGBM and stacking

(2022) Jawad Mahmood et al.   MEASUREMENT

Prediction of surface roughness using fuzzy broad learning system based on feature selection

(2022) Wenwen Tian et al.   JOURNAL OF MANUFACTURING SYSTEMS

A novel ensemble deep learning model for cutting tool wear monitoring using audio sensors

(2022) Zhixiong Li et al.   Journal of Manufacturing Processes

Roughness prediction model of milling noise-vibration-surface texture multi-dimensional feature fusion for N6 nickel metal

(2022) Songyuan Li et al.   Journal of Manufacturing Processes

Prediction of surface roughness in turning using vibration features selected by largest Lyapunov exponent based ICEEMDAN decomposition

(2022) Vikrant Guleria et al.   MEASUREMENT

Modeling and prediction of surface roughness at the drilling of SLM-Ti6Al4V parts manufactured with pre-hole with optimized ANN and ANFIS

(2022) Hakan Dedeakayoğulları et al.   MEASUREMENT

AMS-Net: Attention mechanism based multi-size dual light source network for surface roughness prediction

(2022) Taohong Zhang et al.   Journal of Manufacturing Processes

Prediction of surface roughness based on a hybrid feature selection method and long short-term memory network in grinding

(2021) Weicheng Guo et al.   The International Journal of Advanced Manufacturing Technology

Prediction of cutting power and surface quality, and optimization of cutting parameters using new inference system in high-speed milling process

(2021) Long-Hua Xu et al.   Advances in Manufacturing

The analysis of tool vibration signals by spectral kurtosis and ICEEMDAN modes energy for insert wear monitoring in turning operation

(2021) Mohamed Lamine Bouhalais et al.   INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY

Intelligent tool wear monitoring based on parallel residual and stacked bidirectional long short-term memory network

(2021) Xianli Liu et al.   JOURNAL OF MANUFACTURING SYSTEMS

Position-varying surface roughness prediction method considering compensated acceleration in milling of thin-walled workpiece

(2021) Zequan Yao et al.   Frontiers of Mechanical Engineering

Tool wear monitoring of TC4 titanium alloy milling process based on multi-channel signal and time-dependent properties by using deep learning

(2021) Boling Yan et al.   JOURNAL OF MANUFACTURING SYSTEMS

Physics-informed meta learning for machining tool wear prediction

(2021) Yilin Li et al.   JOURNAL OF MANUFACTURING SYSTEMS

Intelligent tool wear monitoring and multi-step prediction based on deep learning model

(2021) Minghui Cheng et al.   JOURNAL OF MANUFACTURING SYSTEMS

A novel adaptive weighted kernel extreme learning machine algorithm and its application in wind turbine blade icing fault detection

(2021) Ruining Tong et al.   MEASUREMENT

Application of improved fireworks algorithm in grinding surface roughness online monitoring

(2021) Yang Li et al.   Journal of Manufacturing Processes

Effect of milling surface topography and texture direction on fatigue behavior of ZK61M magnesium alloy

(2021) Shiqi Chen et al.   INTERNATIONAL JOURNAL OF FATIGUE

Multi-kernel optimized relevance vector machine for probabilistic prediction of concrete dam displacement

(2020) Siyu Chen et al.   ENGINEERING WITH COMPUTERS

A hybrid information model based on long short-term memory network for tool condition monitoring

(2020) Weili Cai et al.   JOURNAL OF INTELLIGENT MANUFACTURING

An improved case based reasoning method and its application in estimation of surface quality toward intelligent machining

(2020) Longhua Xu et al.   JOURNAL OF INTELLIGENT MANUFACTURING

Marine Predators Algorithm: A nature-inspired metaheuristic

(2020) Afshin Faramarzi et al.   EXPERT SYSTEMS WITH APPLICATIONS

Effect of machining parameters on surface roughness for compacted graphite cast iron by analyzing covariance function of Gaussian process regression

(2020) Juan Lu et al.   MEASUREMENT

Deep kernel learning in extreme learning machines

(2020) A. L. Afzal et al.   PATTERN ANALYSIS AND APPLICATIONS

Effect of machined surface texture on fretting crack nucleation under radial loading in conformal contact

(2020) Huiqing Gu et al.   TRIBOLOGY INTERNATIONAL

Prediction models for specific energy consumption of machine tools and surface roughness based on cutting parameters and tool wear

(2020) Yu Su et al.   PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE

Prediction and control of surface roughness for the milling of Al/SiC metal matrix composites based on neural networks

(2020) Guo Zhou et al.   Advances in Manufacturing

Prediction of surface roughness in extrusion-based additive manufacturing with machine learning

(2019) Zhixiong Li et al.   ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING

Investigation of surface topography and its deterioration resulting from tool wear evolution when dry turning of titanium alloy Ti-6Al-4V

(2019) Xiaoliang Liang et al.   TRIBOLOGY INTERNATIONAL

Efficiency of vibration signal feature extraction for surface finish monitoring in CNC machining

(2019) E. García Plaza et al.   Journal of Manufacturing Processes

Surface generation modeling of micro milling process with stochastic tool wear

(2019) Xuewei Zhang et al.   PRECISION ENGINEERING-JOURNAL OF THE INTERNATIONAL SOCIETIES FOR PRECISION ENGINEERING AND NANOTECHNOLOGY

Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks

(2018) Rui Zhao et al.   IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

Semi-supervised roughness prediction with partly unlabeled vibration data streams

(2018) Maciej Grzenda et al.   JOURNAL OF INTELLIGENT MANUFACTURING

Gaussian process regression for tool wear prediction

(2018) Dongdong Kong et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

A new method of online extreme learning machine based on hybrid kernel function

(2018) Senyue Zhang et al.   NEURAL COMPUTING & APPLICATIONS

Mixed kernel based extreme learning machine for electric load forecasting

(2018) Yanhua Chen et al.   NEUROCOMPUTING

Kernel-Based Multilayer Extreme Learning Machines for Representation Learning

(2018) Chi Man Wong et al.   IEEE Transactions on Neural Networks and Learning Systems

Prediction of Cutting Conditions in Turning AZ61 and Parameters Optimization Using Regression Analysis and Artificial Neural Network

(2018) Nabeel H. Alharthi et al.   Advances in Materials Science and Engineering

Development of a Grey online modeling surface roughness monitoring system in end milling operations

(2017) PoTsang B. Huang et al.   JOURNAL OF INTELLIGENT MANUFACTURING

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

A comprehensive study on the effects of tool wear on surface roughness, dimensional integrity and residual stress in turning IN718 hard-to-machine alloy

(2017) Farbod Akhavan Niaki et al.   Journal of Manufacturing Processes

The Whale Optimization Algorithm

(2016) Seyedali Mirjalili et al.   ADVANCES IN ENGINEERING SOFTWARE

Tool wear monitoring and prognostics challenges: a comparison of connectionist methods toward an adaptive ensemble model

(2016) Kamran Javed et al.   JOURNAL OF INTELLIGENT MANUFACTURING

Effect of Deformation-induced Residual Stress on Peel Strength of Polymer Laminated Sheet Metal

(2015) Hadi Noori et al.   JOURNAL OF ADHESION

Extreme Learning Machine for Regression and Multiclass Classification

(2011) Guang-Bin Huang et al.   IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS

Recent advances in differential evolution: a survey and experimental analysis

(2009) Ferrante Neri et al.   ARTIFICIAL INTELLIGENCE REVIEW