scholarly journals Online Monitoring Based on Temperature Field Features and Prediction Model for Selective Laser Sintering Process

2018 ◽  
Vol 8 (12) ◽  
pp. 2383 ◽  
Author(s):  
Zhehan Chen ◽  
Xianhui Zong ◽  
Jing Shi ◽  
Xiaohua Zhang
Keyword(s):  
Temperature Field ◽  
Prediction Model ◽  
Selective Laser Sintering ◽  
Principal Component ◽  
Laser Sintering ◽  
Support Vector ◽  
Sintering Process ◽  
Metal Materials ◽  
Key Features ◽  
The Mathematical Model

Selective laser sintering (SLS) is an additive manufacturing technology that can work with a variety of metal materials, and has been widely employed in many applications. The establishment of a data correlation model through the analysis of temperature field images is a recognized research method to realize the monitoring and quality control of the SLS process. In this paper, the key features of the temperature field in the process are extracted from three levels, and the mathematical model and data structure of the key features are constructed. Feature extraction, dimensional reduction, and parameter optimization are realized based on principal component analysis (PCA) and support vector machine (SVM), and the prediction model is built and optimized. Finally, the feasibility of the proposed algorithms and model is verified by experiments.

Physical Modeling for Selective Laser Sintering Process

2017 ◽  
Vol 17 (2) ◽  
Author(s):  
Arash Gobal ◽  
Bahram Ravani
Keyword(s):  
Physical Modeling ◽  
Large Scale ◽  
Selective Laser Sintering ◽  
Powdered Material ◽  
Laser Sintering ◽  
Industrial Applications ◽  
Transient Temperature ◽  
Sintering Process ◽  
Powder Bed ◽  
Selective Heating

The process of selective laser sintering (SLS) involves selective heating and fusion of powdered material using a moving laser beam. Because of its complicated manufacturing process, physical modeling of the transformation from powder to final product in the SLS process is currently a challenge. Existing simulations of transient temperatures during this process are performed either using finite-element (FE) or discrete-element (DE) methods which are either inaccurate in representing the heat-affected zone (HAZ) or computationally expensive to be practical in large-scale industrial applications. In this work, a new computational model for physical modeling of the transient temperature of the powder bed during the SLS process is developed that combines the FE and the DE methods and accounts for the dynamic changes of particle contact areas in the HAZ. The results show significant improvements in computational efficiency over traditional DE simulations while maintaining the same level of accuracy.

scholarly journals Intelligent Prediction of Transmission Line Project Cost Based on Least Squares Support Vector Machine Optimized by Particle Swarm Optimization

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Tao Yi ◽  
Hao Zheng ◽  
Yu Tian ◽  
Jin-peng Liu
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Prediction Model ◽  
Transmission Line ◽  
Least Squares ◽  
Principal Component ◽  
Support Vector ◽  
Swarm Optimization ◽  
Empirical Parameter ◽  
Parameter Prediction

In order to meet the demand of power supply, the construction of transmission line projects is constantly advancing, and the level of cost control is constantly improving, which puts forward higher requirements for the accuracy of cost prediction. This paper proposes an intelligent cost prediction model based on least squares support vector machine (LSSVM) optimized by particle swarm optimization (PSO). Originally extracting natural, technological, and economic indexes from the perspective of cost composition, principal component analysis (PCA) is used to reduce the dimension of indexes. And PSO is innovatively introduced to optimize the parameters of LSSVM model to obtain the optimal parameters. The obtained principal component data are imported into empirical parameter LSSVM prediction model and the optimized parameter PSO-LSSVM prediction model, respectively, for modeling and prediction, and then comparing the prediction results to analyze the effect of model optimization. The results show that the absolute deviation of the optimized parameter prediction model is less than 9%. And the prediction accuracy of the optimized parameter prediction model is better than that of the empirical parameter model, which can provide a reliable basis for investment decision-making of transmission line projects.

scholarly journals Key Feature Recognition Algorithm of Network Intrusion Signal Based on Neural Network and Support Vector Machine

Symmetry ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 380 ◽  
Author(s):  
Kai Ye
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Feature Recognition ◽  
False Positive Rate ◽  
Principal Component ◽  
Recognition Algorithm ◽  
Support Vector ◽  
Data Set ◽  
Network Intrusion ◽  
Key Features

When identifying the key features of the network intrusion signal based on the GA-RBF algorithm (using the genetic algorithm to optimize the radial basis) to identify the key features of the network intrusion signal, the pre-processing process of the network intrusion signal data is neglected, resulting in an increase in network signal data noise, reducing the accuracy of key feature recognition. Therefore, a key feature recognition algorithm for network intrusion signals based on neural network and support vector machine is proposed. The principal component neural network (PCNN) is used to extract the characteristics of the network intrusion signal and the support vector machine multi-classifier is constructed. The feature extraction result is input into the support vector machine classifier. Combined with PCNN and SVM (Support Vector Machine) algorithms, the key features of network intrusion signals are identified. The experimental results show that the algorithm has the advantages of high precision, low false positive rate and the recognition time of key features of R2L (it is a common way of network intrusion attack) data set is only 3.18 ms.

Temperature field distribution in polymer particles during surface-selective laser sintering

Laser Physics ◽  
2020 ◽  
Vol 30 (5) ◽  
pp. 055601
Author(s):  
E N Antonov ◽  
A G Dunaev ◽  
A N Konovalov ◽  
S A Minaeva ◽  
V K Popov
Keyword(s):  
Temperature Field ◽  
Field Distribution ◽  
Selective Laser Sintering ◽  
Laser Sintering ◽  
Polymer Particles ◽  
Temperature Field Distribution
Sign in / Sign up

Export Citation Format

Share Document