Optimization of Laser Cladding Process Variables Based on Neural Network and Particle Swarm Optimization Algorithms

2011 ◽  
Vol 38 (2) ◽  
pp. 0203003
Author(s):  
倪立斌 Ni Libin ◽  
刘继常 Liu Jichang ◽  
伍耀庭 Wu Yaoting ◽  
鄢锉 Yan Cuo
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Laser Cladding ◽  
Optimization Algorithms ◽  
Particle Swarm ◽  
Process Variables ◽  
Swarm Optimization ◽  
Laser Cladding Process

scholarly journals Process Parameter Optimization When Preparing Ti(C, N) Ceramic Coatings Using Laser Cladding Based on a Neural Network and Quantum-Behaved Particle Swarm Optimization Algorithm

2020 ◽  
Vol 10 (18) ◽  
pp. 6331
Author(s):  
Zixin Deng ◽  
Tao Chen ◽  
Haojun Wang ◽  
Shengchen Li ◽  
Defu Liu
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Laser Cladding ◽  
Process Parameters ◽  
Ceramic Coating ◽  
Process Parameter ◽  
Micro Hardness ◽  
Swarm Optimization ◽  
Laser Cladding Process ◽  
And Control

The formation process of surface coatings fabricated with laser cladding is very complicated and coating quality is closely related to laser cladding process parameters. Generally, the optimization and control of process parameters play key roles when preparing high-quality ceramic coating. In this paper, three reasonable parameters were selected for each process parameter based on the preliminary experiment. The experiment of Ti(C, N) ceramic coating prepared with laser cladding was designed via the Taguchi method. The laser power, spot diameter, overlapping ratio, and scanning velocity were selected as the main process parameters, and their effects on coating micro-hardness were analyzed using the signal-to-noise (S/N) ratio and analysis of variance (ANOVA). Then, based on the back-propagation neural network (BPNN) and quantum-behaved particle swarm optimization (QPSO) algorithm, we created the prediction model of BPNN-QPSO neural network for laser cladding Ti(C, N) ceramic coating. The mapping of process parameters to the micro-hardness of the coating was obtained according to the model and we analyzed the influence of process parameters that interacted with the coating’s micro-hardness. The results showed that the interaction of laser cladding process parameters had a significant effect on the micro-hardness of the coating. The established BPNN-QPSO neural network model was able to map the relationship between laser cladding process parameters and coating micro-hardness. The process parameters optimized by this model had similar results with ANOVA. This research provides guidance for the selection and control of ceramic coating process parameters Ti(C, N) prepared via laser cladding.

Analysis of Feature Reduction Techniques for Online News Popularity Prediction

2018 ◽  
Vol 4 (10) ◽  
pp. 6
Author(s):  
Shivangi Bhargava ◽  
Dr. Shivnath Ghosh
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Online News ◽  
Feature Reduction ◽  
Support Vector ◽  
Swarm Optimization

News popularity is the maximum growth of attention given for particular news article. The popularity of online news depends on various factors such as the number of social media, the number of visitor comments, the number of Likes, etc. It is therefore necessary to build an automatic decision support system to predict the popularity of the news as it will help in business intelligence too. The work presented in this study aims to find the best model to predict the popularity of online news using machine learning methods. In this work, the result analysis is performed by applying Co-relation algorithm, particle swarm optimization and principal component analysis. For performance evaluation support vector machine, naïve bayes, k-nearest neighbor and neural network classifiers are used to classify the popular and unpopular data. From the experimental results, it is observed that support vector machine and naïve bayes outperforms better with co-relation algorithm as well as k-NN and neural network outperforms better with particle swarm optimization.

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