Dynamic Design for a Novel Parallel Kinematic Machine with Passive Linkage

2007 ◽  
Vol 364-366 ◽  
pp. 1037-1042
Author(s):  
Ying Hu ◽  
Bing Li ◽  
Dai Zhong Su ◽  
Hong Hu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Parallel Mechanism ◽  
Parallel Kinematic Machine ◽  
Element Analysis ◽  
Dynamic Design ◽  
Modeling Accuracy ◽  
Axis Parallel ◽  
Parallel Kinematic ◽  
Artificial Neural

Based on the proposed 4PUS-1RPU parallel mechanism a 5-axis Parallel Kinematic Machine (PKM) scheme has been developed and the dynamic characteristics of the PKM have been investigated in detail. To avoid the intensive computation caused by finite element analysis in the research, two typical metamodeling techniques of Response Surface Methodology (RSM) and its artificial neural network enhanced technique were employed as the robust design approaches. Comparing the results obtained from the direct RSM the modeling accuracy by the artificial neural network enhanced RSM can be improved.

Notch (stress concentration) factor estimation of a cylinder under internal pressure using different approaches

2021 ◽  
Vol 63 (5) ◽  
pp. 430-435
Author(s):  
Osman Atalay ◽  
Ihsan Toktas
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stress Concentration ◽  
Mechanical Design ◽  
Computer Software ◽  
Element Analysis ◽  
Notch Stress ◽  
Artificial Neural ◽  
Effect Parameter ◽  
Stress Concentration Factors

Abstract Today, fluid transportation via pipes can be found in many sectors. Therefore, safe fluid transportation possesses critical importance. While working, transportation pipes are exposed to unwanted loads that culminate in stresses which cause deformation on the part geometry especially in sharp corners, holes or sudden cross-section change areas considered as notched. The notch effect parameter is considered in the mechanical design formulas. This study is interested in the notch factor that is estimated for a cylinder which undergoes an inner pressure. Some users can use false numerical values due to misreading or lack of attention. Because of this reason, graphs were converted to the numerical value by using computer software. In this study, Peterson’s chart was accepted as scientifically valid. Stress concentration factors were obtained by using four other approaches. These are regression, analytical, artificial neural network and finite element analysis. Among these models, high accuracy values were given by the artificial neural network model.

Vibration–Based Damage Detection Of Slab Structure Using Artificial Neural Network

2012 ◽  
Author(s):  
Norhisham Bakhary
Keyword(s):  
Neural Network ◽  
Finite Element Analysis ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Damage Detection ◽  
Ann Model ◽  
Element Analysis ◽  
Severity Prediction ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Kertas kerja ini memaparkan kajian berkenaan keberkesanan Artificial Neural Network (ANN) dalam mengenal pasti kerosakan di dalam struktur. Data dari getaran seperti frekuensi semula jadi dan mod bentuk digunakan sebagai data masukan bagi ANN untuk meramalkan lokasi dan tahap kerosakan bagi struktur lantai. Analisis unsur terhingga (Finite Element Analysis) telah digunakan untuk memperoleh ciri–ciri dinamik bagi struktur–struktur rosak dan tidak rosak untuk ‘melatih’ model ‘neural network’. Senario kerosakan yang berbeza disimulasikan dengan mengurangkan kekukuhan elemen pada lokasi yang berbeza sepanjang struktur tersebut. Berbagai kombinasi data masukan bagi mod yang berbeza telah digunakan untuk memperolehi model ANN yang terbaik. Hasil kajian ini menunjukkan ANN mampu memberikan keputusan yang baik dalam meramal kerosakan pada struktur lantai tersebut. Kata kunci: Ramalan kerosakan struktur, Artificial Neural Network This paper investigates the effectiveness of artificial neural network (ANN) in identifying damages in structures. Global (natural frequencies) and local (mode shapes) vibration–based data has been used as the input to ANN for location and severity prediction of damages in a slab–like structure. A finite element analysis has been used to obtain the dynamic characteristics of intact and damaged structure to train the neural network model. Different damage scenarios have been introduced by reducing the local stiffness of the selected elements at different locations along the structure. Several combinations of input variables in different modes have been used in order to obtain a reliable ANN model. The trained ANN model is validated using laboratory test data. The results show that ANN is capable of providing acceptable result on damage prediction of tested slab structure. Key words: Structural damage detection, artificial neural network

scholarly journals An Artificial Neural Network Approach to Plastic Collapse of Oval Boiler Tubes

2008 ◽  
Vol 41-42 ◽  
pp. 421-426 ◽  
Author(s):  
K. Zarrabi ◽  
A. Basu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Plastic Collapse ◽  
Element Analysis ◽  
Neural Network Approach ◽  
Collapse Pressure ◽  
Thin Tube ◽  
Processing Plants ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Boilers in power, refinery and chemical processing plants contain extensive range of tube bends. Tube bends are manufactured by bending a straight-section tube. As a result, the crosssection of a tube bend becomes oval. Using the finite element analysis (FEA) and artificial neural network (ANN), the paper presents the relationships between the plastic collapse pressures and tube bend dimensions with various degrees of ovality. It is found that as ovality increases the plastic collapse pressure decreases. Also, the reduction of plastic collapse pressure with ovality is small for a thick tube bend when compared with that for a thin tube bend.

The Reinforcement Rib Performance Prediction Based on the BP Algorithm and the Finite Element Analysis

2011 ◽  
Vol 101-102 ◽  
pp. 212-215
Author(s):  
Liang Yao Su ◽  
Xiang Sheng Li ◽  
Xiong Fei Yin ◽  
Xiao Yan Feng ◽  
Shang Wen Ruan
Keyword(s):  
Neural Network ◽  
Finite Element Analysis ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Performance Prediction ◽  
Bp Algorithm ◽  
Prediction System ◽  
Element Analysis ◽  
The Finite Element Analysis ◽  
Artificial Neural

The reinforcement rib design is one of the key parts in entire bottle design. This paper presents the rib performance prediction system based on the BP algorithm and the finite element analysis, which adopts the finite element analysis results as its learning samples, sets up the rib performance prediction system with BP artificial neural network. The results show that the artificial neural network plays an important role in rib performance prediction; meanwhile it can guide the bottle design in practical terms.

scholarly journals Vibration Reliability Analysis of Drum Brake Using the Artificial Neural Network and Important Sampling Method

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Zhou Yang ◽  
Unsong Pak ◽  
Cholu Kwon
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Reliability Analysis ◽  
Sampling Method ◽  
Back Propagation ◽  
Simulation Method ◽  
Back Propagation Neural Network ◽  
Element Analysis ◽  
Analysis Methods ◽  
Artificial Neural

This research aims to evaluate the calculation accuracy and efficiency of the artificial neural network-based important sampling method (ANN-IS) on reliability of structures such as drum brakes. The finite element analysis (FEA) result is used to establish the ANN sample in ANN-based reliability analysis methods. Because the process of FEA is time-consuming, the ANN sample size has a very important influence on the calculation efficiency. Two types of ANNs used in this study are the radial basis function neural network (RBF) and back propagation neural network (BP). RBF-IS and BP-IS methods are used to conduct reliability analysis on training samples of three different sizes, and the results are compared with several reliability analysis methods based on ANNs. The results show that the probability of failure of the RBF-IS method is closer to that of the Monte-Carlo simulation method (MCS) than those of other methods (including BP-IS). In addition, the RBF-IS method has better calculation efficiency than the other methods considered in this study. This research demonstrates that the RBF-IS method is well suited to structure reliability problems.

Biomechanics of the Healthy and Keratoconic Corneas: A Combination of the Clinical Data, Finite Element Analysis, and Artificial Neural Network

2019 ◽  
Vol 24 (37) ◽  
pp. 4474-4483 ◽  
Author(s):  
Alireza Karimi ◽  
Najme Meimani ◽  
Reza Razaghi ◽  
Seyed Mohammadali Rahmati ◽  
Khosrow Jadidi ◽  
...  
Keyword(s):  
Neural Network ◽  
Finite Element Analysis ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Clinical Data ◽  
Element Analysis ◽  
Artificial Neural

scholarly journals Shear Resistance Prediction of Post-fire Reinforced Concrete Beams Using Artificial Neural Network

2019 ◽  
Vol 13 (1) ◽  
Author(s):  
Bin Cai ◽  
Long-Fei Xu ◽  
Feng Fu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Reinforced Concrete ◽  
Concrete Strength ◽  
Shear Resistance ◽  
Reinforced Concrete Beams ◽  
Rc Beams ◽  
Residual Shear Strength ◽  
Element Analysis ◽  
Artificial Neural

Abstract In this paper, a prediction method based on artificial neural network was developed to rapidly determine the residual shear resistance of reinforced concrete (RC) beams after fire. Firstly, the temperature distribution along the beam section was determined through finite element analysis using software ABAQUS. A residual shear strength calculation model was developed and validated using the test data. Using this model, 384 data entries were derived for training and testing. The input layer of neural network involved parameters of beam height, beam width, fire exposure time, cross-sectional area of stirrup, stirrup spacing, concrete strength, and concrete cover thickness. The output was the shear resistance of RC beams. It was found that use of BP neural network could precisely predict the post-fire shear resistance of RC beams. The predicted data were highly consistent with the target data. Thus, this is a novel method for computing post-fire shear resistance of RC beams. Using this new method, further investigation was also made on the effects of different parameters on the shear resistance of the beams.

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