Localized investigation of magnetic bulk property deterioration of electrical steel: Analysing magnetic property drop thorough mechanical and laser cutting of electrical steel laminations using neutron grating interferometry

2013 ◽  
Author(s):  
Rene Siebert ◽  
Andreas Wetzig ◽  
Eckhard Beyer ◽  
Benedikt Betz ◽  
Christian Grunzweig ◽  
...  
Keyword(s):  
Magnetic Property ◽  
Laser Cutting ◽  
Electrical Steel ◽  
Bulk Property

Comparison between Magnetic Anisotropy Energy and a Parameter to the Assessment of Magnetic Property of Electrical Steel

2018 ◽  
Vol 930 ◽  
pp. 449-453
Author(s):  
R.A.C. Felix ◽  
R.L.O. da Rosa ◽  
Luiz P. Brandão
Keyword(s):  
Magnetic Properties ◽  
Magnetic Property ◽  
Magnetic Anisotropy ◽  
Electrical Steel ◽  
Anisotropy Energy ◽  
Alternative Methods ◽  
Magnetic Polarization ◽  
Magnetic Anisotropy Energy ◽  
Electrical Steels ◽  
Global Parameters

Alternative methods of quantitative texture analysis are applied to characterize the non-oriented grain electrical steels (NOG) in relation to their magnetic properties. Magnetic anisotropy energy (Ea) and A parameter are two models based on crystallographic texture that generates global parameters that can be used to predict the magnetic properties of NOG steels. In this work, these two models were used to evaluate the magnetic polarization and compared between themselves to realize which one best correlates to this property.

Applicability of various beam sources for high power laser cutting of non-oriented electrical steel

2012 ◽  
Author(s):  
René Siebert ◽  
Harry Thonig ◽  
Andreas Wetzig ◽  
Eckhard Beyer
Keyword(s):  
High Power ◽  
Laser Cutting ◽  
Electrical Steel ◽  
High Power Laser ◽  
Power Laser

An improved real-time temperature control for pulsed laser cutting of non-oriented electrical steel

2021 ◽  
Vol 136 ◽  
pp. 106783
Author(s):  
Dinh-Tu Nguyen ◽  
Jeng-Rong Ho ◽  
Pi-Cheng Tung ◽  
Chih-Kuang Lin
Keyword(s):  
Real Time ◽  
Temperature Control ◽  
Laser Cutting ◽  
Electrical Steel ◽  
Pulsed Laser

scholarly journals Prediction of Kerf Width in Laser Cutting of Thin Non-Oriented Electrical Steel Sheets Using Convolutional Neural Network

Mathematics ◽  
2021 ◽  
Vol 9 (18) ◽  
pp. 2261
Author(s):  
Dinh-Tu Nguyen ◽  
Jeng-Rong Ho ◽  
Pi-Cheng Tung ◽  
Chih-Kuang Lin
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Laser Cutting ◽  
Electrical Steel ◽  
Cutting Speed ◽  
Pulse Frequency ◽  
Kerf Width ◽  
Percentage Error ◽  
Steel Sheets ◽  
Electrical Steel Sheets

Kerf width is one of the most important quality items in cutting of thin metallic sheets. The aim of this study was to develop a convolutional neural network (CNN) model for analysis and prediction of kerf width in laser cutting of thin non-oriented electrical steel sheets. Three input process parameters were considered, namely, laser power, cutting speed, and pulse frequency, while one output parameter, kerf width, was evaluated. In total, 40 sets of experimental data were obtained for development of the CNN model, including 36 sets for training with k-fold cross-validation and four sets for testing. Compared with a deep neural network (DNN) model and an extreme learning machine (ELM) model, the developed CNN model had the lowest mean absolute percentage error (MAPE) of 4.76% for the final test dataset in predicting kerf width. This indicates that the proposed CNN model is an appropriate model for kerf width prediction in laser cutting of thin non-oriented electrical steel sheets.

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