The neural network modeling of titanium alloy phase transformation and mechanical properties

JOM ◽  
2005 ◽  
Vol 57 (11) ◽  
pp. 54-57 ◽  
Author(s):  
S. Malinov ◽  
W. Sha
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Titanium Alloy ◽  
Phase Transformation ◽  
Network Modeling ◽  
Neural Network Modeling ◽  
The Neural Network

scholarly journals SUPERIORITY OF ARTIFICIAL NEURAL NETWORKS OVER STATISTICAL METHODS IN PREDICTION OF THE OPTIMAL LENGTH OF ROCK BOLTS

2012 ◽  
Vol 18 (5) ◽  
pp. 655-661 ◽  
Author(s):  
Hadi Hasanzadehshooiili ◽  
Ali Lakirouhani ◽  
Jurgis Medzvieckas
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Statistical Models ◽  
Network Modeling ◽  
Neural Network Modeling ◽  
Rock Bolts ◽  
Optimal Length ◽  
The Neural Network ◽  
Rock Bolting ◽  
Geological Conditions

Rock bolting is one of the most important support systems used for rock structures. Rock bolts are widely used in underground excavations as they are suitable for a wide range of geological conditions and allow using progressive design methods; besides, they help economising in the use of materials and manpower. Thus, to provide the most effective support at minimum cost by means of rock bolting, it is essential to optimise the elements contributing to bolt design, including their length, as well as bolt density and tension during installation. This paper considers the length of bolts for optimisation of the design phase, which is one of the most important parameters impacting the entire design procedure. Presenting and comparing results of some statistical models, neural network modeling is introduced as powerful means in prediction of the optimal length of rock bolts. Subsequent to training and testing of a large number of 1-layer and 2-layer backpropagation neural networks, it was reported that the optimal model was the network with the architecture of 6-18-3-1 as it demonstrated the minimum RMSE and MAE as well as the maximum R2. In comparison to statistical models (0.7182 for the value of R2 in the multiple linear regression model, 0.68 in the polynomial model and 0.7 in the dimensionless model), the results obtained by the neural network modeling – i.e. the coefficient of determination R2 of 0.9259, the value of mean absolute error MAE of 0.068, and the root mean squared error RMSE of 0.078 – not only proved their superiority but also introduced the neural network modelling as a highly capable prediction tool in forecasting the optimal length of rock bolts. Furthermore, sensitivity analysis was used to obtain parameters that have the greatest and the least impact on the optimal bolt length: the effect of the overburden thickness, tensile strength, cohesion and Poisson's ratio on the optimal bolt length was almost the same while the friction angle had the least influence.

scholarly journals FORECASTING OF THE SURFACE ROUGHNESS IN FINISHING MILLING USING NEURAL NETWORKS

2019 ◽  
Vol 4 (10) ◽  
pp. 135-141
Author(s):  
Е. Ерыгин ◽  
E. Erygin ◽  
Т. Дуюн ◽  
T. Duyun
Keyword(s):  
Neural Network ◽  
Cutting Tool ◽  
Learning Algorithm ◽  
Network Modeling ◽  
Training Sample ◽  
Neural Network Modeling ◽  
Network Learning ◽  
The Neural Network ◽  
And Training ◽  
Positive Results

This article describes the task of predicting roughness when finishing milling using neural network modeling. As a basis for the creation and training of an artificial neural network, a progressive formu-la for determining the roughness during finishing milling is chosen. The thermoEMF of the processing and processed materials is used as one of the parameters for calculating the roughness. The use of thermoEMF allows to take into account the material of the workpiece and the cutting tool, which af-fects the accuracy of the results. A training sample is created with data for five inputs and one output. The architecture, features and network learning algorithm are described. A neural network that de-termines the roughness for finishing milling has been created and configured. The process of learning and debugging of the neural network by means of graphs is clearly displayed. The network operability is checked on the test data, which allows obtaining positive results.

The Development of a Digital Twin of a Cutting Tool for Mechanical Production

2019 ◽  
pp. 11-17
Author(s):  
Y.G. Kabaldin ◽  
D.A. Shatagin ◽  
A.M. Kuzmishina
Keyword(s):  
Neural Network ◽  
Cutting Tool ◽  
Product Information ◽  
Network Modeling ◽  
Neural Network Modeling ◽  
Virtual Model ◽  
Security Issues ◽  
The Neural Network ◽  
Composition And Structure ◽  
Processing Modes

A digital model (twin) of a cutting tool based on neural network modeling is proposed in this work. It is shown that the developed virtual model makes it possible to optimize the composition and structure of wear-resistant coating and to determine the processing modes that ensure the maximum wear resistance of the cutting tool. The optimization can be performed before the actual manufacturing of the cutting tool by varying the input data of the neural network has taken place. A digital passport of the cutting tool allows the consumer to avoid buying a counterfeit product. Information security issues are considered.

scholarly journals Efficient Neural Network Modeling for Flight and Space Dynamics Simulation

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
Ayman Hamdy Kassem
Keyword(s):  
Neural Network ◽  
Dynamic Systems ◽  
Dynamic Models ◽  
Network Modeling ◽  
Linear Equations ◽  
Dynamics Simulation ◽  
Neural Network Modeling ◽  
The Neural Network ◽  
Speed Up ◽  
Space Dynamics

This paper represents an efficient technique for neural network modeling of flight and space dynamics simulation. The technique will free the neural network designer from guessing the size and structure for the required neural network model and will help to minimize the number of neurons. For linear flight/space dynamics systems, the technique can find the network weights and biases directly by solving a system of linear equations without the need for training. Nonlinear flight dynamic systems can be easily modeled by training its linearized models keeping the same network structure. The training is fast, as it uses the linear system knowledge to speed up the training process. The technique is tested on different flight/space dynamic models and showed promising results.

Predicting of mechanical properties of Fe–Mn–(Al, Si) TRIP/TWIP steels using neural network modeling

2009 ◽  
Vol 45 (4) ◽  
pp. 959-965 ◽  
Author(s):  
G. Dini ◽  
A. Najafizadeh ◽  
S.M. Monir-Vaghefi ◽  
A. Ebnonnasir
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Network Modeling ◽  
Neural Network Modeling ◽  
Twip Steels

scholarly journals Modeling the Kinetic Characteristics of the Curing Process of Polymer Composites Based on a Neural Network

2021 ◽  
Vol 27 (1) ◽  
pp. 031-041
Author(s):  
O. S. Dmitriev ◽  
◽  
A. A. Barsukov ◽  
Keyword(s):  
Neural Network ◽  
Polymer Composites ◽  
Network Modeling ◽  
Neural Network Modeling ◽  
Curing Process ◽  
Kinetic Characteristics ◽  
Degree Of Cure ◽  
Fiber Reinforced Plastic ◽  
The Neural Network ◽  
Reinforced Plastic

The article considers the possibility of modeling the kinetic characteristics of the curing process of polymer composites (for example, carbon fiber reinforced plastic) based on the use of artificial neural networks. Using neural network modeling, the dependence of the change in the kinetic function of the polymer composite on the degree of cure has been obtained. The performance of the neural network is compared with experimental data using classical approximation methods.

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