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Product quality prediction in pulsed laser cutting of silicon steel sheet using vibration signals and deep neural network
Andhi Indira Kusuma,
Yi Mei Huang
機械工程學系
研究成果
:
雜誌貢獻
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期刊論文
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同行評審
21
引文 斯高帕斯（Scopus）
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指紋
研究計畫
(1)
指紋
深入研究「Product quality prediction in pulsed laser cutting of silicon steel sheet using vibration signals and deep neural network」主題。共同形成了獨特的指紋。
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Keyphrases
Vibration Signal
100%
Deep Neural Network
100%
Product Quality Prediction
100%
Pulsed Laser Cutting
100%
Silicon Steel Sheet
100%
Kerf Width
66%
Wavelet Decomposition
33%
Statistical Features
33%
Selected Features
33%
Straight Slot
33%
Product Quality
16%
Machining Parameters
16%
K-best
16%
Predictive Models
16%
In(III)
16%
Performance Comparison
16%
Laser Machining
16%
Feature Extracting
16%
3-axis
16%
Pearson Correlation Coefficient
16%
Five-level
16%
Laser Cutting
16%
Slot Cutting
16%
Vibration Decomposition
16%
Laser Cutting Machine
16%
Raw Vibration Signal
16%
Non-oriented Silicon Steel Sheet
16%
Engineering
Product Quality
100%
Electrical Steel
100%
Pulsed Laser
100%
Deep Neural Network
100%
Network Model
50%
Input Feature
50%
Wavelet Decomposition
33%
Statistical Feature
33%
Time Domain
16%
Machining Parameter
16%
Input Data
16%
Measured Signal
16%
Extracted Feature
16%
Pearsons Linear Correlation Coefficient
16%
Axis Direction
16%
Material Science
Laser Cutting
100%
Silicon Steel
100%
Steel Sheet
100%
Laser Beam Machining
50%