A GAPSO-Enhanced Extreme Learning Machine Method for Tool Wear Estimation in Milling Processes Based on Vibration Signals

A novel transformer-based neural network model for tool wear estimation

(2020) Hui Liu et al.   MEASUREMENT SCIENCE and TECHNOLOGY

Review of tool condition monitoring in machining and opportunities for deep learning

(2020) G. Serin et al.   INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY

In-process tool condition forecasting based on a deep learning method

(2020) Huibin Sun et al.   ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING

A tool condition monitoring method based on two-layer angle kernel extreme learning machine and binary differential evolution for milling

(2020) Yuqing Zhou et al.   MEASUREMENT

Combining translation-invariant wavelet frames and convolutional neural network for intelligent tool wear state identification

(2019) Xin-Cheng Cao et al.   COMPUTERS IN INDUSTRY

Relevance vector machine for tool wear prediction

(2019) Dongdong Kong et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

Tool wear predicting based on multi-domain feature fusion by deep convolutional neural network in milling operations

(2019) Zhiwen Huang et al.   JOURNAL OF INTELLIGENT MANUFACTURING

Theoretical investigation of the contributions of the excitation forces to the vibration of an axial piston pump

(2019) Shaogan Ye et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

Tool condition monitoring techniques in milling process — a review

(2019) T. Mohanraj et al.   Journal of Materials Research and Technology-JMR&T

An intrinsic timescale decomposition-based kernel extreme learning machine method to detect tool wear conditions in the milling process

(2019) Zhi Lei et al.   INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY

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

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

Online Tool Wear Monitoring Via Hidden Semi-Markov Model With Dependent Durations

(2018) Kunpeng Zhu et al.   IEEE Transactions on Industrial Informatics

Review of tool condition monitoring methods in milling processes

(2018) Yuqing Zhou et al.   INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY

Smart-sensor for tool-breakage detection in milling process under dry and wet conditions based on infrared thermography

(2018) Juan A. Ramirez-Nunez et al.   INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY

A novel energy consumption model for milling process considering tool wear progression

(2018) K.N. Shi et al.   JOURNAL OF CLEANER PRODUCTION

Intelligent fault diagnosis of rolling bearing using deep wavelet auto-encoder with extreme learning machine

(2018) Shao Haidong et al.   KNOWLEDGE-BASED SYSTEMS

Design of a Modular Green Closed Internal Cooling Turning Tool for Applications

(2018) Tao Wu et al.   International Journal of Precision Engineering and Manufacturing-Green Technology

Short-Term Wind Power Forecasting: A New Hybrid Model Combined Extreme-Point Symmetric Mode Decomposition, Extreme Learning Machine and Particle Swarm Optimization

(2018) Jianguo Zhou et al.   Sustainability

Hybrid data-driven physics-based model fusion framework for tool wear prediction

(2018) Houman Hanachi et al.   INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY

Prediction of cutting tool wear during milling process using artificial intelligence techniques

(2018) S. Shankar et al.   INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING

A generic tool wear model and its application to force modeling and wear monitoring in high speed milling

(2018) Kunpeng Zhu et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

A Multisensor Fusion Method for Tool Condition Monitoring in Milling

(2018) Yuqing Zhou et al.   SENSORS

Experimental Investigation of Concave and Convex Micro-Textures for Improving Anti-Adhesion Property of Cutting Tool in Dry Finish Cutting

(2018) Zhengyang Kang et al.   International Journal of Precision Engineering and Manufacturing-Green Technology

Spectrum Prediction in Cognitive Radio with Hybrid Optimized Neural Network

(2017) P. Supraja et al.   MOBILE NETWORKS & APPLICATIONS

Adaptive reactionless control strategy via the PSO-ELM algorithm for free-floating space robots during manipulation of unknown objects

(2017) Zhongyi Chu et al.   NONLINEAR DYNAMICS

Numerical Control Machine Tool Fault Diagnosis Using Hybrid Stationary Subspace Analysis and Least Squares Support Vector Machine with a Single Sensor

(2017) Chen Gao et al.   Applied Sciences-Basel

A virtual sensing based augmented particle filter for tool condition prognosis

(2017) Jinjiang Wang et al.   Journal of Manufacturing Processes

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

Experimental study of tool life transition and wear monitoring in turning operation using a hybrid method based on wavelet multi-resolution analysis and empirical mode decomposition

(2015) Mohamed Khemissi Babouri et al.   INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY

Differential evolution-based feature selection and parameter optimisation for extreme learning machine in tool wear estimation

(2015) Wen-An Yang et al.   INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH

Optimization of gear blank preforms based on a new R-GPLVM model utilizing GA-ELM

(2015) Zhiyong Cao et al.   KNOWLEDGE-BASED SYSTEMS

FPGA-based reconfigurable system for tool condition monitoring in high-speed machining process

(2015) P.Y. Sevilla-Camacho et al.   MEASUREMENT

Influence of tool surface on tribological conditions in conventional and dry sheet metal forming

(2015) Marion Merklein et al.   International Journal of Precision Engineering and Manufacturing-Green Technology

An online damage identification approach for numerical control machine tools based on data fusion using vibration signals

(2014) Zhou Yuqing et al.   JOURNAL OF VIBRATION AND CONTROL

An optimizing BP neural network algorithm based on genetic algorithm

(2011) Shifei Ding et al.   ARTIFICIAL INTELLIGENCE REVIEW