4.7 Article

An accurate prediction method of multiple deterioration forms of tool based on multitask learning with low rank tensor constraint

Related references

Note: Only part of the references are listed.
Article Multidisciplinary Sciences

A general end-to-end diagnosis framework for manufacturing systems

Ye Yuan et al.

NATIONAL SCIENCE REVIEW (2020)

Article Engineering, Industrial

Calibration-based tool condition monitoring for repetitive machining operations

Rui Liu et al.

JOURNAL OF MANUFACTURING SYSTEMS (2020)

Article Engineering, Industrial

Big data analytics for smart factories of the future

Robert X. Gao et al.

CIRP ANNALS-MANUFACTURING TECHNOLOGY (2020)

Article Engineering, Industrial

Physics guided neural network for machining tool wear prediction

Jinjiang Wang et al.

JOURNAL OF MANUFACTURING SYSTEMS (2020)

Article Computer Science, Information Systems

Tool Wear Condition Monitoring in Milling Process Based on Current Sensors

Yuqing Zhou et al.

IEEE ACCESS (2020)

Article Automation & Control Systems

Using Multiple-Feature-Spaces-Based Deep Learning for Tool Condition Monitoring in Ultraprec's on Manufacturing

Chengming Shi et al.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2019)

Article Engineering, Industrial

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

Yingguang Li et al.

CIRP ANNALS-MANUFACTURING TECHNOLOGY (2019)

Editorial Material Engineering, Multidisciplinary

From Intelligence Science to Intelligent Manufacturing

Lihui Wang

ENGINEERING (2019)

Article Engineering, Mechanical

Deep learning and its applications to machine health monitoring

Rui Zhao et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2019)

Article Automation & Control Systems

A real-time tool failure monitoring system based on cutting force analysis

Xuechun Shi et al.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2018)

Article Automation & Control Systems

CNC internal data based incremental cost-sensitive support vector machine method for tool breakage monitoring in end milling

Guangda Xu et al.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2018)

Article Automation & Control Systems

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

Kunpeng Zhu et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2018)

Article Automation & Control Systems

Tool wear monitoring based on kernel principal component analysis and v-support vector regression

Dongdong Kong et al.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2017)

Article Automation & Control Systems

Residual Life Prediction of Multistage Manufacturing Processes With Interaction Between Tool Wear and Product Quality Degradation

Li Hao et al.

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING (2017)

Article Engineering, Industrial

Study of spindle power data with neural network for predicting real-time tool wear/breakage during inconel drilling

Raphael Corne et al.

JOURNAL OF MANUFACTURING SYSTEMS (2017)

Article Computer Science, Artificial Intelligence

A survey of deep neural network architectures and their applications

Weibo Liu et al.

NEUROCOMPUTING (2017)

Article Automation & Control Systems

Milling Force Modeling of Worn Tool and Tool Flank Wear Recognition in End Milling

Yongfeng Hou et al.

IEEE-ASME TRANSACTIONS ON MECHATRONICS (2015)

Article Engineering, Industrial

Adaptive resampling-based particle filtering for tool life prediction

Peng Wang et al.

JOURNAL OF MANUFACTURING SYSTEMS (2015)

Article Engineering, Industrial

Enhanced particle filter for tool wear prediction

Jinjiang Wang et al.

JOURNAL OF MANUFACTURING SYSTEMS (2015)

Article Automation & Control Systems

Application of backpropagation neural network for spindle vibration-based tool wear monitoring in micro-milling

Wan-Hao Hsieh et al.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2012)

Review Computer Science, Artificial Intelligence

A review of optimization methodologies in support vector machines

John Shawe-Taylor et al.

NEUROCOMPUTING (2011)

Article Automation & Control Systems

Design of multisensor fusion-based tool condition monitoring system in end milling

Sohyung Cho et al.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2010)

Article Engineering, Manufacturing

Tool breakage detection using support vector machine learning in a milling process

S Cho et al.

INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE (2005)

Article Computer Science, Artificial Intelligence

Multilevel classification of milling tool wear with confidence estimation

RK Fish et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2003)

Article Engineering, Manufacturing

Hidden Markov model-based tool wear monitoring in turning

LT Wang et al.

JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME (2002)

Article Engineering, Manufacturing

On-line tool wear estimation in CNC turning operations using fuzzy neural network model

C Chungchoo et al.

INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE (2002)