Dynamic Characteristics Analysis and Finite Element Simulation of Steel–BFPC Machine Tool Joint Surface

2019 ◽  
Vol 142 (1) ◽  
Author(s):  
Jiaxing Shen ◽  
Ping Xu ◽  
Yinghua Yu
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Machine Tool ◽  
Finite Element Simulation ◽  
Dynamic Characteristics ◽  
Dynamic Performance ◽  
Joint Surface ◽  
Element Simulation ◽  
Neural Network Prediction ◽  
Network Prediction

Abstract The dynamic performance of the steel–Basalt fiber polymer concrete (BFPC) machine tool joint surface (referred to as the joint surface) has a significant impact on the overall BFPC machine tool performance; however, its dynamic characteristics remain unclear. In order to solve this problem, the influence of roughness and surface pressure on the dynamic performance of joint surface was studied experimentally, and a neural network prediction model for the dynamic performance of the joint surface was established. A BFPC bed was designed and manufactured, and BFPC bed’s dynamic performance was tested experimentally. The finite element simulation model of BFPC bed was established with equivalent spring-damper element. The BFPC bed’s dynamic performance without considering the influence of the joint surface and considering the influence of the joint surface was studied separately. The results show that the maximum error of the natural frequency of the BFPC bed was 6.937% considering the influence of the joint surface, which was much lower than the error without considering the influence of the joint surface. The maximum amplitude error of the X-axis and Z-axis acceleration of the BFPC bed was 6.917% and 5.15%, which were much smaller than the error without considering the influence of the joint surface. It proves the accuracy of the neural network prediction model for dynamic performance of the joint surface and the validity of the finite element simulation method for the joint surface. It provides theoretical support for the design analysis of BFPC machine tool.

Application of Artificial Neural Network for Fatigue Life Prediction

2005 ◽  
Vol 297-300 ◽  
pp. 96-101
Author(s):  
Ishak Abdul Azid ◽  
Lee Kor Oon ◽  
Ong Kang Eu ◽  
K.N. Seetharamu ◽  
Ghulam Abdul Quadir
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Fatigue Life ◽  
Solder Joint ◽  
Finite Element Simulation ◽  
Life Prediction ◽  
Fatigue Life Prediction ◽  
Element Simulation ◽  
Parametric Studies ◽  
Solder Joint Fatigue

An extensively published and correlated solder joint fatigue life prediction methodology is incorporated by which finite element simulation results are translated into estimated cycles to failure. This study discusses the analysis methodologies as implemented in the ANSYSTM finite element simulation software tool. Finite element models are used to study the effect of temperature cycles on the solder joints of a flip chip ball grid array package. Through finite element simulation, the plastic work or the strain-energy density of the solder joints are determined. Using an established methodology, the plastic work obtained through simulation is translated into solder joint fatigue life [1]. The corresponding results for the solder joint fatigue life are used for parametric studies. Artificial Neural Network (ANN) has been used to consolidate the parametric studies.

scholarly journals STACKED DELTA DESIGN OF THREE-PHASE PERMANENT-MAGNET FAULT CURRENT LIMITERS

2021 ◽  
Vol 18 (1) ◽  
pp. 26-35
Author(s):  
Mohamed ELADAWY ◽  
Ibrahim Metwally
Keyword(s):  
Finite Element ◽  
Permanent Magnet ◽  
Finite Element Simulation ◽  
Voltage Drop ◽  
Dynamic Performance ◽  
Iron Core ◽  
Fault Current ◽  
Element Simulation ◽  
Three Phase ◽  
Realistic Representation

This paper proposes an improvement for the dynamic performance of presaturated stacked permanent magnet biased three-phase fault current limiter (PMFCL) through COMSOL finite element simulation. The nonlinear demagnetization behavior of the permanent magnet, especially in the upper part of the B-H curve with negative magnetic field intensity, has been modelled through the Jiles-Atherton method. This enables a realistic representation of the PMFCL dynamic behavior throughout its entire operations of pre-fault, fault and fault removal, respectively. The experimental measurements have been considered to validate the trends of the simulation outcomes during the entire operation of PMFCL. Extensive finite element simulation shows that the stacked design of PMFCL can increase the capability of fault current limiting with proper selection of the number and arrangement of the AC coils around the iron core (soft magnet). Results reveal that the division of AC coils into series differential connected subcoils, with an even number, can increase the limiting capability with increasing the AC coil number of turns, without exceeding the permissible tolerances of voltage drop and power losses. Moreover, this stacked design is subjected to parametric investigation for different fault types, either symmetrical or unsymmetrical, or even when changing the fault current peak value.

scholarly journals Inversion prediction of back propagation neural network in collision analysis of anti-climbing device

2020 ◽  
Vol 12 (5) ◽  
pp. 168781402092205
Author(s):  
Yu-Ru Li ◽  
Tao Zhu ◽  
Zhao Tang ◽  
Shou-Ne Xiao ◽  
Jun-Ke Xie ◽  
...  
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Finite Element Simulation ◽  
Evaluation Method ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Prediction Errors ◽  
Collision Model ◽  
Element Simulation ◽  
Simulation Results

Targeting to improve the calculation efficiency of the finite element simulation, we introduce the back propagation neural network–based machine learning method to carry out the inversion prediction framework. The inversion collision model is established based on the inversion prediction framework. Then, the prediction results are compared with the finite element simulation results of the anti-climbing device to verify the feasibility of the inversion collision model. The average prediction errors of velocity, displacement, interface force, and internal energy of the anti-climbing device are 3.7%, 4.31%, 3.4%, and 1%, respectively, and the cost time of the inversion collision model is less than 5 min. The results show that the inversion collision model constructed by back propagation neural network can significantly improve the calculation efficiency and greatly reduce the calculation time under the condition of ensuring accuracy. It will provide a new evaluation method and possibility for partially replacing the required experimental and simulation results for the crashworthiness and the safety of the anti-climbing device.

Optimization approach for processing design in the extrusion process of plastic profile with metal insert

e-Polymers ◽  
2012 ◽  
Vol 12 (1) ◽  
Author(s):  
Yue Mu ◽  
Guoqun Zhao ◽  
Xianghong Wu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Finite Element ◽  
Finite Element Simulation ◽  
Extrusion Process ◽  
Optimization Approach ◽  
Processing Conditions ◽  
Metal Insert ◽  
Element Simulation ◽  
Plastic Profile

AbstractThe extrusion process of plastic profile with metal insert is a kind of novel and advanced plastic processing method which requires rigorous control on processing conditions to ensure the quality and performance of final products. However, it is still difficult to achieve an optimal processing design by using traditional “trial and error” method. An optimization approach for the processing design in the extrusion process of plastic profile with metal insert is proposed based on finite element simulation, back propagation neural network and genetic algorithm in the study. The finite element simulation is conducted to predict polymer melts flow in the extrusion process. The simulated results are extracted for the establishment of neural network. The genetic algorithm is performed for the search of globally optimal design variable with its objective function evaluated by using the established neural network model. The proposed approach is adopted for the optimization of processing conditions in the extrusion process of plastic profile with metal insert. According to the flow balance principle, the uniformity of outlet flow distribution is taken as the optimization objective with a constraint condition on the maximum shear stress. The optimization of two processing parameters including the volume flow rate and the metal insert moving velocity is conducted in the extrusion process of plastic profile with metal insert and the optimization objective is successfully achieved.

Dynamic Characteristics Analysis of Abnormal Vibration for Power Assembly of Diesel Engine of SUV

2011 ◽  
Vol 308-310 ◽  
pp. 1941-1945 ◽  
Author(s):  
Hao Dong Meng ◽  
Shun Ming Li ◽  
Ying Bai
Keyword(s):  
Finite Element ◽  
Diesel Engine ◽  
Simulation Model ◽  
Finite Element Simulation ◽  
Dynamic Characteristics ◽  
Simulation Analysis ◽  
Excitation Frequency ◽  
Modal Testing ◽  
Test Bed ◽  
Element Simulation

With regard to the problem of abnormal vibration for power assembly of diesel engine of the Sport Utility Vehicle in working rotation speed conditions, the method of test mode combined finite element simulation analysis was adopted, the dynamic characteristics of the power assembly were studied in all conditions. First vibration modal testing of the power assembly was carried out in whole vehicle condition and in test bed, the cause of abnormal vibration of the power assembly was found out, the bending stiffness was low, then a kind of actual finite element simulation model of the power assembly was set up, the constraint mode was calculated. The rationality of finite element simulation model of the power assembly was validated. Integrated modal testing and simulation analysis showed that the low stiffness of flywheel shell led to the low first modal frequency of power assembly which was easily in the excitation frequency range of diesel engine working rotational speed, and resulted in system resonance of the power assembly

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