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

Calibration-based tool condition monitoring for repetitive machining operations

(2020) Rui Liu et al.   JOURNAL OF MANUFACTURING SYSTEMS

A pragmatic dataset augmentation approach for transformation temperature prediction in steels

(2020) C. Hüter et al.   COMPUTATIONAL MATERIALS SCIENCE

Tool wear progression of PCD and PCBN cutting tools in high speed machining of NiTi shape memory alloy under various cutting speeds

(2020) Eren Kaya et al.   DIAMOND AND RELATED MATERIALS

Applications of machine learning to machine fault diagnosis: A review and roadmap

(2020) Yaguo Lei et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

Enhanced deep residual network with multilevel correlation information for fault diagnosis of rotating machinery

(2020) Shoucong Xiong et al.   JOURNAL OF VIBRATION AND CONTROL

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

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

Physics guided neural network for machining tool wear prediction

(2020) Jinjiang Wang et al.   JOURNAL OF MANUFACTURING SYSTEMS

Recent progress of chatter prediction, detection and suppression in milling

(2020) Lida Zhu et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

A systematic review of deep transfer learning for machinery fault diagnosis

(2020) Chuan Li et al.   NEUROCOMPUTING

Machine learning and reasoning for predictive maintenance in Industry 4.0: Current status and challenges

(2020) Jovani Dalzochio et al.   COMPUTERS IN INDUSTRY

Real-time tool wear monitoring using thin-film thermocouple

(2020) Tianxiang Li et al.   JOURNAL OF MATERIALS PROCESSING TECHNOLOGY

An automatic and accurate method for tool wear inspection using grayscale image probability algorithm based on bayesian inference

(2020) Yingguang Li et al.   ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING

Research on the Milling Tool Wear and Life Prediction by Establishing an Integrated Predictive Model

(2019) Yinfei Yang et al.   MEASUREMENT

Relevance vector machine for tool wear prediction

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

Time varying and condition adaptive hidden Markov model for tool wear state estimation and remaining useful life prediction in micro-milling

(2019) Weijian Li et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

Deep heterogeneous GRU model for predictive analytics in smart manufacturing: Application to tool wear prediction

(2019) Jinjiang Wang et al.   COMPUTERS IN INDUSTRY

Data-driven smart manufacturing: Tool wear monitoring with audio signals and machine learning

(2019) Zhixiong Li et al.   Journal of Manufacturing Processes

Deep learning for big data applications in CAD and PLM – Research review, opportunities and case study

(2018) Jonathan Dekhtiar et al.   COMPUTERS IN INDUSTRY

Gaussian process regression for tool wear prediction

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

Novel texture-based descriptors for tool wear condition monitoring

(2018) Aco Antić et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

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

Using deep neural network with small dataset to predict material defects

(2018) Shuo Feng et al.   MATERIALS & DESIGN

EigenSample: A non-iterative technique for adding samples to small datasets

(2017) Jayadeva et al.   APPLIED SOFT COMPUTING

Cyclostationarity approach for monitoring chatter and tool wear in high speed milling

(2013) M. Lamraoui et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

Detection process approach of tool wear in high speed milling

(2010) M. Kious et al.   MEASUREMENT