The EIS-based Kohonen neural network for high strength steel coating degradation assessment

2017 ◽  
Vol 31 (11) ◽  
pp. e2919 ◽  
Author(s):  
Junshuang Ran ◽  
Yuanming Xu ◽  
Yan Tong
Keyword(s):  
Neural Network ◽  
High Strength Steel ◽  
High Strength ◽  
Coating Degradation ◽  
Kohonen Neural Network ◽  
Steel Coating

Laser lap joining of high-strength steel to aluminum with cold sprayed steel coating

2021 ◽  
Vol 33 (4) ◽  
pp. 042017
Author(s):  
Kyohei Maeda ◽  
Yuji Sato ◽  
Reiichi Suzuki ◽  
Tetsuo Suga ◽  
Masahiro Tsukamoto
Keyword(s):  
High Strength Steel ◽  
High Strength ◽  
Steel Coating

Comparative Efficiency of Artificial Neural Network to Predict Spring-Back Value in U-Bending Process of High Strength Steel

2015 ◽  
Vol 749 ◽  
pp. 319-326
Author(s):  
Arkhom Songkroh ◽  
Bhadpiroon Sresomroeng
Keyword(s):  
Neural Network ◽  
High Strength Steel ◽  
Pearson Correlation ◽  
High Strength ◽  
Final Analysis ◽  
Raw Material ◽  
Work Piece ◽  
Spring Back ◽  
Punch Force ◽  
Bending Process

High strength steel (HSS) was used as raw material in the automotive industry, which required lightweight and high strength, such as chassis and etc. However, the strength and hardness of the steel are relatively high, leading to the low permeability and large spring-back occurring after forming operation. As a result, the work piece is not shaped desire. This research proposes neural network for predicts the spring back values, in U-bending process, that the materials were differences in mechanical properties, such as SPFH590 (JIS) and SPEC980Y (JIS). In the experiment, the input factors for predictable data consists as the punch radius (RP), die radius (Rd), clearance (Cl) and counter punch force (Fc). After that, the input data were analyzed relation with spring-back values by the Pearson Correlation of One-tailed. Next, It was selected by Leave-one-out and k-fold Cross validation (K-fold and LOOCV), to improve efficiency of the prediction process. Moreover, the result was a measurable performance with Root Mean Square Error (RMSE) technique, equal to 0.788 and 2.10 respectively. In the final analysis, the neural network is effective to predict the Spring-back values of SPFH590 (JIS) rather than SPEC 980Y (JIS) in U-bending process.

COMPARATIVE CHARACTERISTICS OF PHYSICAL AND MECHANICAL PROPERTIES OF COMPOSITE MATERIALS AND HIGH-STRENGTH STEEL 30FGSN2A

2020 ◽  
Author(s):  
I.R. Antypes ◽  
◽  
V.V. Zaitsev ◽  
Keyword(s):  
Composite Materials ◽  
High Strength Steel ◽  
Critical Stress ◽  
Physical And Mechanical Properties ◽  
High Strength ◽  
Aviation Industry ◽  
Specific Strength ◽  
Aircraft Landing ◽  
Carbon Plastics ◽  
Aircraft Landing Gear

Currently, the use of composite materials is increasingly used in various areas of the national economy, including the aviation industry. The materials of this article are devoted to the study of the use of composite materials for the manufacture of aircraft landing gear in comparison with the traditionally used brand of steel. As a result of the work carried out, it was found that the slope made of carbon fiber showed a critical stress twice as high as its design made of 30xgsn2a steel. In addition, carbon plastics are superior to high-strength steel in terms of specific strength, stiffness, and tensile strength.

Sign in / Sign up

Export Citation Format

Share Document