Development of a Material Selection Model for Rolls Rolling Mills Using Neural Networks

2020 ◽  
Author(s):  
M.F. Gafarov ◽  
K.P. Pavlova ◽  
E.A. Gafarova
Keyword(s):  
Neural Networks ◽  
Material Selection ◽  
Selection Model ◽  
Rolling Mills

scholarly journals Development of Material Combination Model Considering Economics and Construction Efficiency for G-SEED Certification

2021 ◽  
Vol 13 (6) ◽  
pp. 3535
Author(s):  
Byung-Ju Jeon ◽  
Byung-Soo Kim
Keyword(s):  
Structural Design ◽  
Material Selection ◽  
Selection Model ◽  
Construction Costs ◽  
Seed Certification ◽  
Evaluation Scores ◽  
The Cost ◽  
Material Information ◽  
Business As Usual ◽  
Construction Efficiency

The Korean government proposed a goal to reduce its greenhouse gas emissions by 37% compared to business-as-usual levels by 2030 and launched the Green Standard for Energy and Environmental Design (G-SEED) certification system. The certification requires meeting the required score and material selection with a secured economy and construction efficiency. However, most buildings only focus on obtaining the certification scores instead of choosing economical materials with high construction efficiency. This research focused on developing a material selection model that considers both the construction efficiency and economy of the materials and the acquisition of material and resource evaluation scores from the G-SEED certification. This research, therefore, analyzed actual data to automate the material selection and compare alternatives to using a genetic algorithm to obtain optimized alternatives. This model proposes an alternative to constructability and economy when the required score and material information is entered. When the model was applied to actual cases, the result revealed a reduction in construction costs of about 37% compared to the cost with the traditional methods. The material selection model from this research can benefit construction project owners in terms of cost reduction, designers in terms of structural design time, and constructors in terms of construction efficiency

scholarly journals Comparative estimation of models with different structures for estimation of mechanical properties of light steel profiles

2019 ◽  
Vol 5 (4) ◽  
pp. 16-27
Author(s):  
Zambrano Ortíz Denis Joaquín ◽  
Litardo Velásquez Rosa Mariuxi ◽  
Arzola Ruiz José
Keyword(s):  
Mechanical Properties ◽  
Neural Networks ◽  
Case Studies ◽  
Regularization Method ◽  
Linear Models ◽  
Linear Quadratic ◽  
Steel Company ◽  
Iterative Regularization ◽  
Rolling Mills ◽  
Network Training

The paper presents linear, quadratic, signomial and radial-based neural networks for the estimation of the mechanical properties of steel profiles for construction obtained from the chemical composition of the batches, the cross-section of the profile to be laminated, for the lamination workshops taken as case studies. As primary information, a database with the batches produced in the Antillana de Acero rolling mills is used for more than ten years. The results obtained show that the radial base neural networks applying Landweber's iterative regularization method to network training provide the highest precision. The signomial, quadratic and linear models reach similar values ​​of precision taking as a criterion of comparison the standard deviation of the estimate with respect to the results of the passive experiments obtained from the quality control of the production. The modeling work is done for the case studies of the laminating workshops 250 and 300 of the steel company Antillana de Acero.

A Material Selection Model of Mechanical Products Based on Total Life Cycle Design

2011 ◽  
Vol 84-85 ◽  
pp. 310-316
Author(s):  
Liang He ◽  
Wan Lin Guo
Keyword(s):  
Life Cycle ◽  
Material Selection ◽  
Transformation Method ◽  
Selection Model ◽  
Design Stage ◽  
Assessment Index ◽  
Early Design Stage ◽  
Life Cycle Design ◽  
Mechanical Products ◽  
Total Life

Material selection in mechanical products based on total life cycle design is a complicated work, which should be studied systematically. A material selection model of mechanical products based on total life cycle design was proposed. A set of candidate materials were screened out, and then assessed according to the technical, economic and environmental assessment index. The candidate materials were ranked by using by using Z-transformation method in each of the assessment index. Different weights were assigned to each of the three assessment indexes, and global assessment was carried out according to different strategies or requirements which pay more attention to technical, economic or environmental performance of the material product used. A case in selecting aircraft structure element material was studied. The analysis results showed that the method could rank the candidate materials and selected out the “optimized material”, and the influence of the subjectivity of designer was reduced. The method provides some practical values for preliminary material selection in the early design stage of the mechanical products based on life cycle design.

A Method about Load Distribution of Rolling Mills Based on RBF Neural Network

2011 ◽  
Vol 279 ◽  
pp. 418-422
Author(s):  
Dong Dong Liu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Load Distribution ◽  
Rolling Force ◽  
Rbf Neural Network ◽  
Rbf Neural Networks ◽  
Rolling Mills ◽  
The Neural Network ◽  
Network Output

Rolling mills process is too complicated to be described by formulas. RBF neural networks can establish finishing thickness and rolling force models. Traditional models are still useful to the neural network output. Compared with those finishing models which have or do not have traditional models as input, the importance of traditional models in application of neural networks is obvious. For improving the predictive precision, BP and RBF neural networks are established, and the result indicates that the model of load distribution based on RBF neural network is more accurate.

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