4.7 Article

A Physics-Assisted Online Learning Method for Tool Wear Prediction

Related references

Note: Only part of the references are listed.
Article Engineering, Industrial

Physics-informed meta learning for machining tool wear prediction

Yilin Li et al.

Summary: This paper introduces a new physics-informed meta-learning framework for tool wear prediction under varying wear rates, improving prediction accuracy by enhancing modeling strategy and constraining optimization process with a loss term informed by physics.

JOURNAL OF MANUFACTURING SYSTEMS (2022)

Article Computer Science, Interdisciplinary Applications

A Scalable Framework for Process-Aware Thermal Simulation of Additive Manufacturing Processes

Yaqi Zhang et al.

Summary: Thermal simulation plays an important role in additive manufacturing (AM) processes. However, the complexity of the manufacturing process and computational requirements make thermal simulation challenging. This study proposes a new computational framework that supports scalable thermal simulation for any AM process driven by a moving heat source. The framework includes process-aware path-level discretization, a spatial data structure called contact graph, and a localized simulation based on specific physical parameters.

JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING (2022)

Article Engineering, Mechanical

The emerging graph neural networks for intelligent fault diagnostics and prognostics: A guideline and a benchmark study

Tianfu Li et al.

Summary: Deep learning methods have advanced the field of Prognostics and Health Management, but handling irregular data in non-Euclidean space remains a challenge. Research has proposed a practical guideline for utilizing graph neural networks for intelligent fault diagnostics and prognostics, and established a framework based on GNN for this purpose.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2022)

Article Engineering, Electrical & Electronic

Pyramid LSTM Network for Tool Condition Monitoring

Hao Guo et al.

Summary: In this article, a pyramid LSTM auto-encoder is proposed for tool wear monitoring in high-performance CNC machining. The features are compressed layer by layer based on the frequency spectrum, simplifying the monitoring task and reducing model complexity. The efficiency of long-term signal processing is greatly improved by reducing the number of units. The introduction of auto-encoder further enhances the model's accuracy under complex working conditions through unsupervised learning.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2022)

Article Engineering, Multidisciplinary

Tool wear monitoring in micromilling using Support Vector Machine with vibration and sound sensors

Milla Caroline Gomes et al.

Summary: This paper presents a new approach to monitor the wear of cutting tools used in micromilling processes using SVM artificial intelligence model, vibration, and sound signals. Experimental results show that microtools with different cutting lengths have different lifespans.

PRECISION ENGINEERING-JOURNAL OF THE INTERNATIONAL SOCIETIES FOR PRECISION ENGINEERING AND NANOTECHNOLOGY (2021)

Article Automation & Control Systems

Data-Driven Structural Health Monitoring Using Feature Fusion and Hybrid Deep Learning

Hung V. Dang et al.

Summary: This study developed a practical end-to-end framework for smart structural health monitoring, achieving highly accurate damage detection through a hybrid deep learning model and signal processing techniques. Three case studies demonstrated the effectiveness of the proposed approach, suitable for real-time SHM with reduced resource requirements.

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING (2021)

Article Engineering, Multidisciplinary

Deep learning-based tool wear prediction and its application for machining process using multi-scale feature fusion and channel attention mechanism

Xingwei Xu et al.

Summary: Tool wear is a key factor in the cutting process, and accurate prediction using deep learning methods can improve the prediction accuracy.

MEASUREMENT (2021)

Article Automation & Control Systems

Vibration-based tool wear monitoring using artificial neural networks fed by spectral centroid indicator and RMS of CEEMDAN modes

Mourad Nouioua et al.

Summary: This study evaluates the potential of monitoring tool life during turning process of AISI 1045 steel using laser Doppler vibrometer (LDV) and applying CEEMDAN technique for signal processing, along with artificial neural network for real-time wear monitoring. Results indicate that CEEMDAN aids in isolating tool vibration signature and SCI indicator offers accurate wear information for real-time monitoring.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2021)

Article Engineering, Multidisciplinary

Tool wear prediction in high-speed turning of a steel alloy using long short-term memory modelling

Mohsen Marani et al.

Summary: This paper introduces a prediction model for tool flank wear during the machining of a steel alloy using LSTM modeling. The results demonstrate the accuracy and reliability of the model, with a low test RMSE value.

MEASUREMENT (2021)

Article Engineering, Mechanical

Tool wear estimation and life prognostics in milling: Model extension and generalization

Yu Zhang et al.

Summary: A generic wear model with adjustable coefficients is proposed in this study, dividing the tool life into three main wear zones including running-in wear, adhesive wear, and three-body abrasive wear. The wear model is validated and improved based on experimental data to accurately discriminate tool wear ranges.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2021)

Article Chemistry, Analytical

A Novel Machine Learning-Based Methodology for Tool Wear Prediction Using Acoustic Emission Signals

Juan Luis Ferrando Chacon et al.

Summary: There is an increasing trend in the industry towards real-time monitoring of critical aspects like tool wear to reduce costs and scrap in machining processes. Machine learning models based on tool wear data are becoming popular as they simplify the development of physical models. While acoustic emission (AE) technique is widely used for real-time monitoring of industrial assets like cutting tools, the interpretation and processing of AE signals is complex.

SENSORS (2021)

Article Chemistry, Analytical

Estimation of Tool Wear and Surface Roughness Development Using Deep Learning and Sensors Fusion

Pao-Ming Huang et al.

Summary: This paper proposes a method for estimating tool wear and surface roughness using deep learning and sensor fusion, combined with sensor selection analysis to improve estimation accuracy.

SENSORS (2021)

Article Materials Science, Multidisciplinary

Effects of calcium-treatment of a plastic injection mold steel on the tool wear and power consumption in slot milling

Julio C. G. Milan et al.

Summary: The study investigated the machinability of calcium treated mold steel AISI P20 UF and compared it to the non-treated version AISI P20. The results showed that the calcium treated steel had a considerably higher tool life, and while the treatment did not directly affect power consumption, it indirectly reduced it by decreasing tool wear rate.

JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T (2021)

Article Engineering, Electrical & Electronic

A U-Net-Based Approach for Tool Wear Area Detection and Identification

Huihui Miao et al.

Summary: This article presents a direct technique for automated tool wear detection and identification using cutting tool images, utilizing a U-Net-based network for effective extraction of the wear area. The introduction of deep supervision and a Matthews correlation coefficient (MCC)-based surrogate loss function addresses issues such as few-shot and data imbalance, demonstrating the method's effectiveness in experiments with images of cutting tools with wear on the flank face.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2021)

Article Engineering, Electrical & Electronic

Fault Diagnosis of Rolling Bearing Based on WHVG and GCN

Chenyang Li et al.

Summary: Emerging intelligent algorithms have achieved great success in fault diagnosis, but current models struggle with capturing all structure relationships in the data. To address this, a graph convolution network (GCN) incorporating the weighted horizontal visibility graph (WHVG) is proposed, improving performance and benefiting from the internal structure relationships of the data in bearing faults diagnosis.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2021)

Article Computer Science, Artificial Intelligence

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

Zhiwen Huang et al.

JOURNAL OF INTELLIGENT MANUFACTURING (2020)

Article Automation & Control Systems

Time-varying analytical model of ball-end milling tool wear in surface milling

Zemin Zhao et al.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2020)

Article Automation & Control Systems

Tool Wear Prediction via Multidimensional Stacked Sparse Autoencoders With Feature Fusion

Chengming Shi et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (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, Interdisciplinary Applications

A hybrid predictive maintenance approach for CNC machine tool driven by Digital Twin

Weichao Luo et al.

ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING (2020)

Article Computer Science, Interdisciplinary Applications

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

Xin-Cheng Cao et al.

COMPUTERS IN INDUSTRY (2019)

Article Automation & Control Systems

Tool condition monitoring in CNC end milling using wavelet neural network based on machine vision

Pauline Ong et al.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2019)

Article Automation & Control Systems

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

Houman Hanachi et al.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2019)

Article Engineering, Mechanical

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

Kunpeng Zhu et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2019)

Article Automation & Control Systems

An Online Tool Temperature Monitoring Method Based on Physics-Guided Infrared Image Features and Artificial Neural Network for Dry Cutting

Kok-Meng Lee et al.

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING (2018)

Article Computer Science, Hardware & Architecture

A Hybrid Approach to Cutting Tool Remaining Useful Life Prediction Based on the Wiener Process

Huibin Sun et al.

IEEE TRANSACTIONS ON RELIABILITY (2018)

Article Automation & Control Systems

Force-based tool wear estimation for milling process using Gaussian mixture hidden Markov models

Dongdong Kong et al.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2017)

Article Engineering, Electrical & Electronic

Multisensor Feature Fusion for Bearing Fault Diagnosis Using Sparse Autoencoder and Deep Belief Network

Zhuyun Chen et al.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2017)

Article Engineering, Industrial

Enhanced particle filter for tool wear prediction

Jinjiang Wang et al.

JOURNAL OF MANUFACTURING SYSTEMS (2015)

Article Automation & Control Systems

CCIoT-CMfg: Cloud Computing and Internet of Things-Based Cloud Manufacturing Service System

Fei Tao et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2014)

Article Automation & Control Systems

Multiscale Singularity Analysis of Cutting Forces for Micromilling Tool-Wear Monitoring

Zhu Kunpeng et al.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2011)