Emulsifier fault diagnosis based on back propagation neural network optimized by particle swarm optimization

Wang, Yue-Sheng; Qian, Hao; Zhen, Dawei

doi:10.1109/icsai.2014.7009314

Cited by 1 publication

(1 citation statement)

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“…To overcome these shortcomings, the particle swarm optimization algorithm and the genetic algorithm (GA) have been first selected to determine the structure parameters of the BP neural network, and the BP neural network trained with the aid of PSO algorithm has shown higher prediction accuracy. Thus, the particle swarm optimization algorithm which has the advantages of fast convergence, strong robustness and global search ability is finally selected in this paper to help training the BP neural network [25,26].…”

Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Prediction of Tool Point Frequency Response Functions within Machine Tool Work Volume Considering the Position and Feed Direction Dependence

et al. 2020

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A chatter vibration in milling process results in poor surface finish and machining efficiency. To avoid the chatter vibration, the stability lobe diagram (SLD) which is the function of tool point frequency response functions (FRFs) is adopted to predict the chatter-free machining parameters. However, the tool point FRF varies with the changes of machining positions and feed directions within machine tool work volume. Considering this situation, this paper presents a method to predict the position and feed direction-dependent tool point FRF. First, modal parameters of the tool point FRFs obtained at some typical positions and feed directions are identified by the modal theory and matrix transformation method. With the sample information, a back propagation (BP) neural network whose inputs are the position coordinates and feed angle and outputs are the modal parameters can be trained with the aid of the particle swarm optimization (PSO) algorithm. Then, modal parameters corresponding to any position and feed direction can be predicted by the trained BP neural network and used to reorganize the tool point FRFs with the modal fitting technique. A case study was performed on a real vertical machining center to demonstrate the accurate prediction of position and feed direction-dependent tool point FRFs. Furthermore, the position and feed direction-dependent milling stability was researched and origin-symmetric distributions of the limiting axial cutting depths at each machining position were observed.

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Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%