scholarly journals Optimization of Open Die Ironing Process through Artificial Neural Network for Rapid Process Simulation

Metals ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. 1397
Author(s):  
Silvia Mancini ◽  
Luigi Langellotto ◽  
Giovanni Zangari ◽  
Riccardo Maccaglia ◽  
Andrea Di Schino
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Plastic Strain ◽  
Element Analysis ◽  
Design And Optimization ◽  
Forging Process ◽  
The Core ◽  
Die Forging ◽  
Artificial Neural ◽  
Open Die Forging

The open die forging sequence design and optimization are usually performed by simulating many different configurations corresponding to different forging strategies. Finite element analysis (FEM) is a tool able to simulate the open die forging process. However, FEM is relatively slow and therefore it is not suitable for the rapid design of online forging processes. A new approach is proposed in this work in order to describe the plastic strain at the core of the piece. FEM takes into account the plastic deformation at the core of the forged pieces. At the first stage, a thermomechanical FEM model was implemented in the MSC.Marc commercial code in order to simulate the open die forging process. Starting from the results obtained through FEM simulations, a set of equations describing the plastic strain at the core of the piece have been identified depending on forging parameters (such as length of the contact surface between tools and ingot, tool’s connection radius, and reduction of the piece height after the forging pass). An Artificial Neural Network (ANN) was trained and tested in order to correlate the equation coefficients with the forging to obtain the behavior of plastic strain at the core of the piece.

scholarly journals Open die forging process simulation: a simplified industrial approach based on artificial neural network

2021 ◽  
Vol 8 (5) ◽  
pp. 685-697
Author(s):  
Andrea Di Schino ◽  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Plastic Strain ◽  
Element Analysis ◽  
Simplified Approach ◽  
Forging Process ◽  
The Core ◽  
Die Forging ◽  
Artificial Neural ◽  
Open Die Forging

<abstract> <p>Simulations by Finite element analysis (FEM) of open die forging process related to different configurations are quite common in industry to optimize the process. This approach, anyway, is relatively slow to be performed: hence it is not suitable for online optimization of the forging processes. In this paper a simplified approach is proposed aimed to describe the plastic strain at the core of the forged component. The proposed approach takes into account the plastic deformation at the core of the forged component and consists on a thermo-mechanical FEM model implementation allowing to define a set of equations giving as output the plastic strain at the core of the piece as a function of the forging parameters. An Artificial Neural Network (ANN) is trained and tested aimed to relate the equation coefficients with the forging to obtain the behavior of plastic strain at the core of the piece.</p> </abstract>

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.

Design and Optimization of a Filter Based on Artificial Neural Network Applied to a Distillation Column

2008 ◽  
Vol 3 (1) ◽  
Author(s):  
Jose S Torrecilla ◽  
Adela Fernández ◽  
Julian Garcia ◽  
Francisco Rodríguez
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Distillation Column ◽  
Chemical Engineering ◽  
Engineering Process ◽  
Design And Optimization ◽  
Noisy Signals ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Design And Application

This paper discusses the design and application of a filter based on an Artificial Neural Network (ANN) in a chemical engineering process. The design of a filter consists of adapting the algorithms that make up the filter to the process to be filtered. Taking into account that the ANN is able to model almost every type of chemical process, the design and application of a filter based on ANN was studied. In this work, every ANN used was based on Multilayer Perceptron (MLP). Bearing in mind that ANN should reproduce the process as accurately as possible, an optimisation of the ANN (training function and parameters) was carried out. A mathematical model of a reflux in the upper part of a distillation column was used to test the ANN filter. The ANN is able to filter noisy signals with a mean prediction error less than 2.5•10-3 %.

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.

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