A zero-shot prediction method based on causal inference under non-stationary manufacturing environments for complex manufacturing systems

A Survey on Causal Inference

(2021) Liuyi Yao et al.   ACM Transactions on Knowledge Discovery from Data

A Meta-Invariant Feature Space Method for Accurate Tool Wear Prediction Under Cross Conditions

(2021) Changqing Liu et al.   IEEE Transactions on Industrial Informatics

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 data-driven approach for tool wear recognition and quantitative prediction based on radar map feature fusion

(2021) Xuebing Li et al.   MEASUREMENT

Transfer learning enabled convolutional neural networks for estimating health state of cutting tools

(2021) Mohamed Marei et al.   ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING

Prediction of pose-dependent modal properties and stability limits in robotic ball-end milling

(2021) Ruoqi Wang et al.   ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING

Milling force prediction model based on transfer learning and neural network

(2020) Juncheng Wang et al.   JOURNAL OF INTELLIGENT MANUFACTURING

Smart manufacturing process and system automation – A critical review of the standards and envisioned scenarios

(2020) Yuqian Lu et al.   JOURNAL OF MANUFACTURING SYSTEMS

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

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

A novel method for accurately monitoring and predicting tool wear under varying cutting conditions based on meta-learning

(2019) Yingguang Li et al.   CIRP ANNALS-MANUFACTURING TECHNOLOGY

Heterogeneous data-driven hybrid machine learning for tool condition prognosis

(2019) Peng Wang et al.   CIRP ANNALS-MANUFACTURING TECHNOLOGY

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

(2019) Jinjiang Wang et al.   COMPUTERS IN INDUSTRY

A General End-to-end Diagnosis Framework for Manufacturing Systems

(2019) Ye Yuan et al.   National Science Review

Toward a new tribological approach to predict cutting tool wear

(2018) J. Rech et al.   CIRP ANNALS-MANUFACTURING TECHNOLOGY

Data-driven smart manufacturing

(2018) Fei Tao et al.   JOURNAL OF MANUFACTURING SYSTEMS

Approximate residual balancing: debiased inference of average treatment effects in high dimensions

(2018) Susan Athey et al.   JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY

Pose-dependent tool tip dynamics prediction using transfer learning

(2018) Gengxiang Chen et al.   INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE

Deep learning

(2015) Yann LeCun et al.   NATURE

Proposal for a new theoretical model of the cutting tool's flank wear

(2013) Z. Pálmai   WEAR

A Survey on Transfer Learning

(2009) Sinno Jialin Pan et al.   IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING

A theory of learning from different domains

(2009) Shai Ben-David et al.   MACHINE LEARNING