Multi response optimization of thrust force and delamination in carbon fiber reinforced polymer (CFRP) drilling using backpropagation neural network-particle swarm optimization (BPNN-PSO)

2018 ◽  
Author(s):  
R. Norcahyo ◽  
B. O. P. Soepangkat ◽  
Sutikno
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Carbon Fiber ◽  
Thrust Force ◽  
Fiber Reinforced Polymer ◽  
Carbon Fiber Reinforced Polymer ◽  
Backpropagation Neural Network ◽  
Swarm Optimization ◽  
Reinforced Polymer ◽  
Response Optimization

scholarly journals Multi response prediction of cutting force and delamination in carbon fiber reinforced polymer using backpropagation neural network-genetic algorithm

2019 ◽  
Author(s):  
Fathi Robbany ◽  
Bambang Pramujati ◽  
Suhardjono ◽  
Mohammad Khoirul Effendi ◽  
Bobby Oedy Pramoedyo Soepangkat ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Carbon Fiber ◽  
Cutting Force ◽  
Response Prediction ◽  
Fiber Reinforced Polymer ◽  
Carbon Fiber Reinforced Polymer ◽  
Fiber Reinforced ◽  
Backpropagation Neural Network ◽  
Reinforced Polymer

A particle swarm optimization–support vector machine hybrid system with acoustic emission on damage degree judgment of carbon fiber reinforced polymer cables

2020 ◽  
pp. 147592172092282
Author(s):  
Jie Xu ◽  
Xuan Liu ◽  
Qinghua Han ◽  
Weixin Wang
Keyword(s):  
Support Vector Machine ◽  
Acoustic Emission ◽  
Particle Swarm Optimization ◽  
Carbon Fiber ◽  
Fiber Reinforced Polymer ◽  
Carbon Fiber Reinforced Polymer ◽  
Support Vector ◽  
Swarm Optimization ◽  
Damage Degree ◽  
Reinforced Polymer

The feasibility of machine learning in damage degree judgment of carbon fiber reinforced polymer cables was first verified by the improved b-value method and wavelet packet spectrum analysis. Then, a hybrid system with support vector machine classification and particle swarm optimization algorithms was proposed to realize the prediction. The b-value calculated with all acoustic emission events has better performance when noise cannot be avoided. The 1/ b-value has almost the same trend with acoustic emission signal cumulative energy, which can meet the preliminarily needs of health monitoring. The particle swarm optimization clustering algorithm works by using nine characteristic parameters of acoustic emission signals. It demonstrates that the characteristic parameters of acoustic emission signals are closely related to the failure mode of the carbon fiber reinforced polymer cable. This indicates their correspondence to the cable’s damage degree and their ability to work as training data for machine learning. With particle swarm optimization, the trained support vector machine can reach at least 77% accuracy of a single acoustic emission signal when predicting the corresponding current damage degree. In addition, using the voting mechanism can promote the performance of support vector machine. This demonstrates the practicability of applying acoustic emission combined with machine learning as a damage degree judgment method for carbon fiber reinforced polymer cables.

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