Cutting Force Embedded Manifold Learning for Condition Monitoring of Vertical Machining Center

Wang, Jing; Cheng, Xiaobin; Gao, Yan; Wang, Xun; Jiang, Yang

doi:10.1109/tim.2022.3180420

Cited by 10 publications

(2 citation statements)

References 47 publications

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“…In [17], the signal features were extracted from force, vibration and acoustic emission (AE) signals, and the tool wear was subsequently monitored and predicted by feeding these extracted features into a deep learning regression model known as the sequence-to-sequence model with attention and monotonicity loss (SMAML). Wang et al [18]. utilized a t-distributed stochastic neighbor embedding (t-SNE) method to classify various tool wear levels, utilizing features extracted from both vibration and force signals.…”

Section: Introductionmentioning

confidence: 99%

Vibration Signal-Based Tool Condition Monitoring Using Regularized Sensor Data Modeling and Model Frequency Analysis

Liu,

Lang,

Gui

et al. 2024

IEEE Trans. Instrum. Meas.

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Section: Introductionmentioning

confidence: 99%

Vibration Signal-Based Tool Condition Monitoring Using Regularized Sensor Data Modeling and Model Frequency Analysis

Liu,

Lang,

Gui

et al. 2024

IEEE Trans. Instrum. Meas.

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“…The map manifold has characteristics of high initial position sensitivity, great iterations variation, chaotic attractor internal stability, and uncertainty. These characteristics make it have a wide range of applications in data encryption, 1,2 software design, 3 image processing, 4 mechanical structure design, 5 and other fields. In view of the different types of manifolds, scholars put forward different calculation methods 6‐9 …”

Section: Introduction To Derivative Transfer Theorymentioning

confidence: 99%

Computer data processing method of map manifold based on derivative transfer approach

Jia

Sun

2023

Concurrency and Computation

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SummaryWith the rapid development of Internet technology and the rapid upgrade of hardware devices such as computers and smartphones, the data processed by computers often presents a high dimension. How to reduce data dimension and improve data processing speed is a hot research topic in the computer field. One shortcoming of one‐dimensional manifold calculation methods via sequence points is that the position of new points on the manifold is determined by searching the existing manifold by dichotomy, which is tedious and time‐consuming. This paper improved this method based on derivative transfer. According to the calculation results of one‐dimensional stable and unstable manifolds, they exhibited a special property, that is, for any point on the manifold orbit, its image and preimage must also be on the manifold orbit. Based on this, the derivative transfer method was used to improve the calculation of one‐dimensional manifolds for the map, and a three‐step calculation method of “prediction‐correction‐accuracy verification” was proposed. Then this method was applied to the simulation of high‐dimensional manifolds. The simulation results revealed that the proposed method refined the manifold calculation and greatly enhanced the computation speed.

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Differentiating Additive and Traditional Manufacturing Processes Through Unsupervised Learning and Image Processing

Ördek

Borgianni

2023

Lecture Notes in Networks and Systems

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Cutting Force Embedded Manifold Learning for Condition Monitoring of Vertical Machining Center

Cited by 10 publications

References 47 publications

Vibration Signal-Based Tool Condition Monitoring Using Regularized Sensor Data Modeling and Model Frequency Analysis

Vibration Signal-Based Tool Condition Monitoring Using Regularized Sensor Data Modeling and Model Frequency Analysis

Computer data processing method of map manifold based on derivative transfer approach

Differentiating Additive and Traditional Manufacturing Processes Through Unsupervised Learning and Image Processing

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