Research on tool wear prediction based on temperature signals and deep learning

The effect of vibration and cutting zone temperature on surface roughness and tool wear in eco-friendly MQL turning of AISI D2

(2020) Onur Özbek et al.   Journal of Materials Research and Technology-JMR&T

Tool wear rate prediction in ultrasonic vibration-assisted milling

(2020) Yixuan Feng et al.   MACHINING SCIENCE AND TECHNOLOGY

In situ measurement of cutting edge temperature in turning using a near-infrared fiber-optic two-color pyrometer

(2020) Jinghui Han et al.   MEASUREMENT

A tool wear monitoring and prediction system based on multiscale deep learning models and fog computing

(2020) Huihui Qiao et al.   INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY

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

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

Intelligent recognition of milling cutter wear state with cutting parameter independence based on deep learning of spindle current clutter signal

(2020) Kaiyu Song et al.   INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY

Influence of tool vibrations on tool wear mechanisms in internal turning of hardened steel

(2020) D. I. Suyama et al.   Journal of the Brazilian Society of Mechanical Sciences and Engineering

Multi-Sensor Data Fusion for Remaining Useful Life Prediction of Machining Tools by IABC-BPNN in Dry Milling Operations

(2020) Min Liu et al.   SENSORS

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

Real-time tool wear monitoring using thin-film thermocouple

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

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

(2019) Yinfei Yang et al.   MEASUREMENT

Tool wear evaluation under minimum quantity lubrication by clustering energy of acoustic emission burst signals

(2019) Chengdong Wang et al.   MEASUREMENT

Vibration-based updating of wear prediction for spur gears

(2019) Ke Feng et al.   WEAR

Tool wear state recognition based on GWO–SVM with feature selection of genetic algorithm

(2019) Xiaoping Liao et al.   INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY

Application of surface replication combined with image analysis to investigate wear evolution on gear teeth – A case study

(2019) Haichuan Chang et al.   WEAR

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

Cutting tools embedded with thin film thermocouples vertically to the rake face for temperature measurement

(2019) Jindang Li et al.   SENSORS AND ACTUATORS A-PHYSICAL

A novel method for tool condition monitoring based on long short-term memory and hidden Markov model hybrid framework in high-speed milling Ti-6Al-4V

(2019) Zhengrui Tao et al.   INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY

Tool condition monitoring techniques in milling process — a review

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

Application of spindle power signals in tool condition monitoring based on HHT algorithm

(2019) Bin Shen et al.   INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY

Big Data Oriented Smart Tool Condition Monitoring System

(2019) Kunpeng Zhu et al.   IEEE Transactions on Industrial Informatics

Tool Wear Estimation in End Milling of Titanium Alloy Using NPE and a Novel WOA-SVM Model

(2019) Dongdong Kong et al.   IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT

Bearing remaining useful life prediction based on deep autoencoder and deep neural networks

(2018) Lei Ren et al.   JOURNAL OF MANUFACTURING SYSTEMS

Gaussian process regression for tool wear prediction

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

Tool wear monitoring in rock drilling applications using vibration signals

(2018) Miho Klaic et al.   WEAR

Investigating wear behaviour in sheet metal stamping using acoustic emissions

(2018) Vignesh.V. Shanbhag et al.   WEAR

Toward New-Generation Intelligent Manufacturing

(2018) Ji Zhou et al.   Engineering

Predicting tool wear with multi-sensor data using deep belief networks

(2018) Yuxuan Chen et al.   INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY

Deep Transfer Learning Based on Sparse Autoencoder for Remaining Useful Life Prediction of Tool in Manufacturing

(2018) Chuang Sun et al.   IEEE Transactions on Industrial Informatics

An embedded multi-sensor system on the rotating dynamometer for real-time condition monitoring in milling

(2017) Muhammad Rizal et al.   INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY

A new tool wear monitoring method based on multi-scale PCA

(2016) Guofeng Wang et al.   JOURNAL OF INTELLIGENT MANUFACTURING

A probabilistic neural network applied in monitoring tool wear in the end milling operation via acoustic emission and cutting power signals

(2016) Rodrigo Henriques Lopes da Silva et al.   MACHINING SCIENCE AND TECHNOLOGY

Tool Condition Monitoring and Remaining Useful Life Prognostic Based on a Wireless Sensor in Dry Milling Operations

(2016) Cunji Zhang et al.   SENSORS

Correlation between features of acoustic emission signals and mechanical wear mechanisms

(2012) Alan Hase et al.   WEAR

Advanced monitoring of machining operations

(2010) R. Teti et al.   CIRP ANNALS-MANUFACTURING TECHNOLOGY