4.7 Article

Branched Neural Network based model for cutter wear prediction in machine tools

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Engineering, Manufacturing

Monitoring of in-process force coefficients and tool wear

Yen-Po Liu et al.

Summary: This paper proposes a method for monitoring the in-process force coefficients for toolpaths with varying radial immersions or feed rates, and validates the effectiveness of this method.

CIRP JOURNAL OF MANUFACTURING SCIENCE AND TECHNOLOGY (2022)

Article Engineering, Multidisciplinary

A particle swarm optimization-support vector machine hybrid system with acoustic emission on damage degree judgment of carbon fiber reinforced polymer cables

Jie Xu et al.

Summary: The study utilized a hybrid system with support vector machine classification and particle swarm optimization algorithms to predict the damage degree of carbon fiber reinforced polymer cables, analyzing characteristic parameters of acoustic emission signals to demonstrate their close relation to the cable's damage degree.

STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL (2021)

Article Engineering, Multidisciplinary

Detecting structural damage under unknown seismic excitation by deep convolutional neural network with wavelet-based transmissibility data

Ying Lei et al.

Summary: This article proposes a deep learning-based approach for structural damage detection under unknown seismic excitations, utilizing wavelet transform to eliminate the influence of different seismic excitations, and validating the performance through numerical simulation and experimental tests.

STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL (2021)

Article Engineering, Multidisciplinary

Deep residual network framework for structural health monitoring

Ruhua Wang et al.

Summary: Convolutional neural networks have been widely used in structural health monitoring and damage identification. The deep residual network framework proposed in this article outperforms state-of-the-art models in predicting damage indexes from vibration characteristics in both numerical and experimental studies.

STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL (2021)

Article Engineering, Multidisciplinary

Group sparsity-aware convolutional neural network for continuous missing data recovery of structural health monitoring

Zhiyi Tang et al.

Summary: In structural health monitoring, data quality is crucial for tasks such as structural damage identification. A convolutional neural network-based data recovery method is proposed to simultaneously recover multi-channel data and maximize the use of interrupted information. The method achieves good recovery results on synthetic data, field-test data, and seismic response monitoring data.

STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL (2021)

Article Engineering, Multidisciplinary

Unsupervised deep learning approach using a deep auto-encoder with an one-class support vector machine to detect structural damage

Zilong Wang et al.

Summary: This article proposes an unsupervised deep learning-based approach for structural damage detection, which utilizes a carefully designed deep auto-encoder and one-class support vector machine for extracting damage-sensitive features and detecting future damage. Experimental and numerical studies confirm the high accuracy and stability of the method in detecting structural damage.

STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL (2021)

Article Computer Science, Interdisciplinary Applications

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

Mohamed Marei et al.

Summary: By utilizing a transfer learning enabled CNN approach, this study effectively predicts and evaluates the wear condition of cutting tools, providing viable strategies for health management in CNC machining applications.

ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING (2021)

Proceedings Paper Computer Science, Artificial Intelligence

Predictive Maintenance of Relative Humidity Using Random Forest Method

Aji Teguh Prihatno et al.

Summary: This paper discusses the implementation of predicting Relative Humidity in a smart factory environment using the Random Forest method, achieving an accuracy of 82.49%. The research goal may contribute to lowering costs and increasing maintenance efficiency in the manufacturing industry.

3RD INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE IN INFORMATION AND COMMUNICATION (IEEE ICAIIC 2021) (2021)

Proceedings Paper Engineering, Electrical & Electronic

Comparing Normalization Methods for Limited Batch Size Segmentation Neural Networks

Martin Kolarik et al.

2020 43RD INTERNATIONAL CONFERENCE ON TELECOMMUNICATIONS AND SIGNAL PROCESSING (TSP) (2020)

Article Automation & Control Systems

Micro-milling tool wear monitoring under variable cutting parameters and runout using fast cutting force coefficient identification method

Tongshun Liu et al.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2020)

Article Engineering, Manufacturing

On-line chatter detection in milling using drive motor current commands extracted from CNC

Deniz Aslan et al.

INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE (2018)

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, Manufacturing

Real-time tool wear monitoring in milling using a cutting condition independent method

Mehdi Nouni et al.

INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE (2015)

Article Engineering, Electrical & Electronic

Parameterized AdaBoost: Introducing a Parameter to Speed Up the Training of Real AdaBoost

Shuqiong Wu et al.

IEEE SIGNAL PROCESSING LETTERS (2014)

Article Engineering, Manufacturing

Correlating surface roughness, tool wear and tool vibration in the milling process of hardened steel using long slender tools

Marcelo Mendes de Aguiar et al.

INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE (2013)

Review Automation & Control Systems

A review of machining monitoring systems based on artificial intelligence process models

Jose Vicente Abellan-Nebot et al.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2010)

Article Engineering, Manufacturing

Chatter suppression in fast tool servo-assisted turning by spindle speed variation

Dan Wu et al.

INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE (2010)

Article Computer Science, Artificial Intelligence

Leave-One-Out-Training and Leave-One-Out-Testing Hidden Markov Models for a Handwritten Numeral Recognizer: The Implications of a Single Classifier and Multiple Classifications

Albert Hung-Ren Ko et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2009)

Article Engineering, Industrial

Tool wear monitoring of micro-milling operations

Mohammad Malekian et al.

JOURNAL OF MATERIALS PROCESSING TECHNOLOGY (2009)

Article Engineering, Manufacturing

An approach to use an array of three acoustic emission sensors to locate uneven events in machining - Part 1: method and validation

DA Axinte et al.

INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE (2005)

Article Engineering, Manufacturing

A cutting power model for tool wear monitoring in milling

H Shao et al.

INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE (2004)

Article Engineering, Manufacturing

Effect of grinding forces on the vibration of grinding machine spindle system

M Alfares et al.

INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE (2000)