A Study of Cutting Tool Wear Prediction Utilizing Generalized Regression Neural Network with Improved Fruit Fly Optimization

2018 ◽  
Author(s):  
Ling Kang ◽  
Xin Xiong ◽  
Lili Yi ◽  
Yijun Guo
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Cutting Tool ◽  
Fruit Fly ◽  
Generalized Regression Neural Network ◽  
Wear Prediction ◽  
Fruit Fly Optimization ◽  
Cutting Tool Wear ◽  
Tool Wear Prediction ◽  
Generalized Regression

Prediction of microfibril angle in Dahurian larch wood using visible–near infrared spectroscopy and chemometric techniques

2019 ◽  
Vol 27 (4) ◽  
pp. 270-277
Author(s):  
Ying Li ◽  
Brian K Via ◽  
Qingzheng Cheng ◽  
Yaoxiang Li
Keyword(s):  
Neural Network ◽  
Stepwise Regression ◽  
Near Infrared ◽  
Microfibril Angle ◽  
Coupled Model ◽  
Fruit Fly ◽  
Generalized Regression Neural Network ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization ◽  
Generalized Regression

The microfibril angle of the S2 layer in the secondary cell wall of the tracheid is important for molecular and microscopic properties that influence collapse resistance, longitudinal modulus of elasticity and other lateral properties of conifers at the macroscopic level. This research aimed to investigate the feasibility of using a fruit fly optimization algorithm for visible and near infrared modeling optimization of Dahurian larch wood microfibril angle prediction. Originally, the linear relationship between microfibril angle and their raw spectra and visible and near infrared spectra pretreated by wavelet transform was established. Then, a nonlinear coupled model was built by combining the stepwise regression analysis and generalized regression neural network methods. As a final point, fruit fly optimization algorithm was used for optimizing stepwise regression analysis–generalized regression neural network coupled model. It was found that stepwise regression analysis–generalized regression neural network coupled model coupled model based on the optimization of fruit fly optimization algorithm simplify visible and near infrared spectral data and its prediction results ([Formula: see text] = 0.90, RMSEP = 0.75, mean average percentage error ([Formula: see text]) = 0.05) outperforms original partial least squares model ([Formula: see text] = 0.86, RMSEP = 0.88, [Formula: see text] = 0.06). This work demonstrated the feasibility of using improved chemometric techniques for improving the precision of visible and near infrared spectra in the prediction of microfibril angle.

An accurate cutting tool wear prediction method under different cutting conditions based on continual learning

2021 ◽  
pp. 095440542199369
Author(s):  
Jiaqi Hua ◽  
Yingguang Li ◽  
Wenping Mou ◽  
Changqing Liu
Keyword(s):  
Tool Wear ◽  
Cutting Tool ◽  
Prediction Method ◽  
Cutting Conditions ◽  
Wear Prediction ◽  
Cutting Tool Wear ◽  
Task Distribution ◽  
Tool Wear Prediction ◽  
Meta Learning ◽  
Continual Learning

Cutting tool wear prediction plays an important role in the machining of complex aerospace parts, and it is still a challenge under varying cutting conditions. To overcome the limitations of the existing methods in generalization ability when dealing with cutting conditions changing largely, this paper proposed a novel cutting tool wear prediction method based on continual learning. A meta-LSTM model is firstly trained for specific cutting conditions and can be easily fine-tuned with very small number of samples to adapt to new cutting conditions. Specifically, the meta-model could be continuously updated as machining data increase by using an orthogonal weights modification method. The experiment results show that the proposed method can realize accurate prediction of tool wear under different cutting conditions. Compared with existing methods including meta-learning methods, the range of adapted cutting conditions could be expanded as the task distribution of new cutting conditions is continuously learned by the prediction model.

CUTTING TOOL WEAR PREDICTION BY USING THE GROUP METHOD OF DATA HANDLING (GMDH)

Mechanika ◽  
2012 ◽  
Vol 18 (5) ◽  
Author(s):  
D. Kara Ali ◽  
M. E. A. Ghernaout ◽  
S. Galiz ◽  
A. Liazid
Keyword(s):  
Tool Wear ◽  
Cutting Tool ◽  
Group Method ◽  
Data Handling ◽  
Wear Prediction ◽  
Cutting Tool Wear ◽  
Tool Wear Prediction

scholarly journals A new optimization particle filtering navigation location method for aquatic plants cleaning workboat in crab farming

2018 ◽  
Vol 15 (4) ◽  
pp. 172988141878791 ◽  
Author(s):  
Chengzhi Ruan ◽  
Dean Zhao ◽  
Shihong Ding ◽  
Yueping Sun ◽  
Jinhui Rao ◽  
...  
Keyword(s):  
Neural Network ◽  
Particle Filter ◽  
Aquatic Plants ◽  
Optimization Algorithm ◽  
Fruit Fly ◽  
Generalized Regression Neural Network ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization ◽  
Network Methods ◽  
Generalized Regression

Chinese river crabs are important aquatic products in China, and the accurate operation of aquatic plants cleaning workboat is an urgent need for solving various problems in the aquaculture process. In order to achieve the accurate navigation positioning, this article introduces the visual-aided navigation system and combines the advantages of particle filter in nonlinear and non-Gaussian systems. Meanwhile, the generalized regression neural network is used to adjust the particle weights so that the samples are closer to the posterior density, thus avoiding the phenomenon of particle degradation and keeping the diversity of particles. In order to improve the network performance, the fruit fly optimization algorithm is introduced to adjust the smoothing factor of transfer function for the generalized regression neural network model layer. On this basis, the location filtering navigation method based on fruit fly optimization algorithm-generalized regression neural network-particle filter is proposed. According to the simulation results, the meanR of root-mean-square error of the proposed fruit fly optimization algorithm-generalized regression neural network- particle filter method decreases by 12.39% and 6.87%, respectively, compared with those of particle filter and generalized regression neural network methods, and the meanT of running time decreases by 16.04% and 9.14%, respectively. From the repeated experiments on the aquatic plants cleaning workboat in crab ponds, the latitude error of the proposed method decreases by 23.45% and 12.68%, respectively, and that the longitude error decreases by 29.11% and 17.65%, respectively, compared with those of particle filter and generalized regression neural network methods. It is proved that our proposed method can effectively improve the navigation positioning accuracy of aquatic plants cleaning workboat.

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