Prediction of Forming Load for the Helical Gear Precision Forging by BP Neural Network

2013 ◽  
Vol 823 ◽  
pp. 170-174
Author(s):  
Wei Feng ◽  
Ji Chang Cao ◽  
Shu Ting Wu ◽  
Yang Fan Li
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Fem Simulation ◽  
Helical Gear ◽  
The Finite Element Method ◽  
Forming Process ◽  
Forming Load ◽  
Precision Forging ◽  
Metal Forming Process ◽  
Backward Propagation

Precision forging of the helical gear is a complex metal forming process under coupled effects with multi-factors. The high forming load is required to fill the teeth corner, which significantly causes failure, plastic deformation and wear of dies. The maximum forming load during precision forging helical gear is calculated by the finite element method (FEM). Combining the FEM simulation results with the artificial neural networks (ANN), backward propagation (BP) neural network is trained using the data of FEM simulation as learning sample. The trained BP neural network is validated using test samples and used to predict the maximum forming load under the different deformation conditions. The results show that the predicted results agree well with the simulated ones, the differences of prediction results exhibit low value, the predicted precision satisfy the request of industry.

Prediction of Spring Back of the Two-Axle Rotary Shaping Based on Neural Network

2006 ◽  
Vol 532-533 ◽  
pp. 1044-1047
Author(s):  
Shi Hong Lu ◽  
Jing Wang
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Fem Simulation ◽  
Back Propagation Neural Network ◽  
Small Error ◽  
Forming Process ◽  
The Neural Network ◽  
Metal Forming Process ◽  
Set Up ◽  
Good Agreement

Two-axle rotary shaping is one of advanced sheet metal forming process that combined stamping ascendant used elastic medium with traditional rotary shaping principle. The prediction model of two-axle rotary shaping is set up to predict the springback for two-axle rotary shaping. It used the back propagation neural network because of the better nonlinear mapping ability. Some of data from the experiment and FEM simulation is applied to train the network; the other data is used to test the prediction result. The result showed that the value of prediction and experiment is in good agreement, and just small error is existed. It demonstrated that the neural network model might predict the springback of two-axle rotary shaping and reduce the number of simulation calculation and experiment operation. It can offer a powerful guidance for rapid choice of process parameters in production.

FEM Simulation Analysis of Cross Wedge Rolling Process

2011 ◽  
Vol 381 ◽  
pp. 72-75
Author(s):  
Bin Li
Keyword(s):  
Simulation Analysis ◽  
Three Dimensional ◽  
Fem Simulation ◽  
Rolling Process ◽  
Stress Distributions ◽  
Cross Wedge Rolling ◽  
Forming Process ◽  
Pressure Distributions ◽  
Metal Forming Process ◽  
Rolling Load

This paper investigates the interfacial slip between the forming tool and workpiece in a relatively new metal forming process, cross-wedge rolling. Based on the finite elements method, three-dimensional mechanical model of cross wedge rolling process has been developed. Examples of numerical simulation for strain, stress distributions and rolling load components have been included. The main advantages of the finite element method are: the capability of obtaining detailed solutions of the mechanics in a deforming body, namely, stresses, shapes, strains or contact pressure distributions; and the computer codes, can be used for a large variety of problems by simply changing the input data.

Experimental Analysis of Velocity Fields in Hot Extrusion of Aluminium Alloy 6351

2011 ◽  
Vol 491 ◽  
pp. 145-150 ◽  
Author(s):  
Marcelo Martins ◽  
Sérgio Tonini Button ◽  
José Divo Bressan
Keyword(s):  
Metal Forming ◽  
Internal Flow ◽  
Hot Extrusion ◽  
Experimental Tests ◽  
Velocity Fields ◽  
The Finite Element Method ◽  
Forming Process ◽  
Complex Shapes ◽  
Metal Forming Process ◽  
Volume Method

Hot extrusion is a metal forming process with a huge importance in the manufacturing of long metallic bars with complex shapes, and because of this, academics and industries are especially interested in better understanding how metal flows during the process. In order to have a reliable computational tool that can help to solve and to obtain material internal flow, experimental tests and numerical simulation with the finite element method were carried out to obtain results of the velocity fields generated in hot direct extrusion of aluminum billets (aluminum alloy 6351). The experimental results of the velocity field will be used to validate a computational code based on the finite volume method.

Finite Element Simulation of the Yoke Spline under Hot Forging Process

2020 ◽  
Vol 982 ◽  
pp. 106-111
Author(s):  
Surasak Suranuntchai
Keyword(s):  
Finite Element ◽  
Hot Forging ◽  
Fem Simulation ◽  
Die Design ◽  
Forming Process ◽  
Element Simulation ◽  
Metal Forming Process ◽  
Automotive Part ◽  
Forging Die ◽  
Hot Forging Process

Nowadays, finite element method (FEM) has been widely used to forecast metal forming process, to analysis problems of workpiece, to decrease production cost, and to save time of die design. This work studied the use of FEM as a tool to design a hot forging die for producing an automotive part named Yoke Spline. The part was made from carbon steel grade S45CVL0. There are three processes to produce Yoke Spline, including the buster, rougher, and finisher processes. The objective of the study was to increase efficiency of production by 5%. To achieve this objective, it was necessary to design a new die in the buster process by using FEM to analyze the die size and shape. The new die must produce the workpieces without any defects. The defects regularly found in the forging workpieces are the dimension out of specification, the under filling, and the crack. The sizes of the buster upper die cover are the width and depth. The die width of 44.5, 46.5 and 49.5 millimeters and the die depth of 25, 28 and 31 millimeters were used in the hot forging simulation. From FEM simulation results, it was found that the die width of 46.5 millimeters and the die depth of 28 millimeters were the best to form workpieces without any defects. In summary, the simulation and experimental results were compatible.

scholarly journals The analysis of the parameters for deep drawing of cylindrical parts

2018 ◽  
Vol 178 ◽  
pp. 02011
Author(s):  
Dan Chiorescu ◽  
Esmeralda Chiorescu ◽  
Sergiu Olaru
Keyword(s):  
Finite Element ◽  
Deep Drawing ◽  
The Finite Element Method ◽  
Ansys Software ◽  
Forming Process ◽  
Thin Steel ◽  
Metal Forming Process ◽  
Cylindrical Parts ◽  
Cylindrical Cup ◽  
High Degree

Deep drawing is a very important metal forming process. Thin steel sheet is important material for manufacture of numerous products with deep drawing and stamping. Cold working provides also the possibility of making parts of various shapes, from the simplest to those with a high degree of complexity whose execution through other methods is uneconomical, difficult and sometimes even impossible. In this paper it is analyzed both experimentally and with the help of the finite element, the behavior of the blank during the cylindrical cup deep drawing process, using the ANSYS software program and the finite element method. A comparison is realized between the experimental and the analytical results, elaborating a representative set of problems that analyze the variation of the die punch clearance, movement of the punch and with or without lubrication. The results of the research are useful in developing a sensible design of experiments.

Applying BP Neural Network Model to Forecast the Largest Coiling Torque of down Coilers

2014 ◽  
Vol 936 ◽  
pp. 1614-1619
Author(s):  
Ke Zhao ◽  
Zhi Gang Wang ◽  
Chang Ming Liu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
High Strength ◽  
Coiling Temperature ◽  
Strip Steel ◽  
Development And Utilization ◽  
Hot Rolling Mill ◽  
Bp Neural Network Model ◽  
Backward Propagation ◽  
Relationship Of

Down coiler is an important equipment of hot rolling mill. The coiling torque is changing constantly in the process of strip steel coiling, and the largest coiling torque depends on several factors, such as the material and specification of coiling strip, the coiling temperature and the process parameters and so on. Only when the largest coiling torque is less than the carrying capacity could the coiler work in security. A topology relationship of the largest coiling torque among the materials, the specification of the strip and the coiling temperature is established. Based on the BP(backward propagation of errors) artificial neural network, a predicted formula model of the largest coiling torque in coiling high strength strip is built, which provides a theoretical basis for the development and utilization of the largest working potential of the down coiler. Keywords: Down Coiler; BP Neural Network; Coiling Torque; Forecast

Hull Plate Bending Springback Prediction Based on Artificial Neural Network

2014 ◽  
Vol 988 ◽  
pp. 309-312
Author(s):  
Shao Juan Su ◽  
Yong Hu ◽  
Cheng Fang Wang ◽  
Bin Liu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Three Dimensional ◽  
Plate Bending ◽  
Forming Process ◽  
Cold Bending ◽  
Matlab Programming ◽  
Springback Prediction ◽  
Surface Plate

In the process of three-dimensional curved hull plate forming, springback caused serious influence on the forming accuracy, in order to ensure the forming quality of the asymmetric multiple pressure heads CNC bending machine of ship hull 3D surface plate, to achieve the automatic processing, it is necessary to solve the problem of springback in the hull plate forming process. It is rarely to see the research on the cold bending springback problem of middle-thickness hull plate now. To established nonlinear model of plate parameters and springback amount based on BP neural network, accurately analyzing the prediction of springback, and getting the sptringback prediction model based on the BP neural network in the Matlab programming.

Numerical Simulation of Glass Bottle Forming Process

2012 ◽  
Vol 503-504 ◽  
pp. 136-139
Author(s):  
Dao Cheng Zhang ◽  
Ke Jun Zhu ◽  
Shao Hui Yin ◽  
Yong Jian Zhu
Keyword(s):  
Fem Simulation ◽  
High Volume ◽  
Mechanical Analysis ◽  
The Finite Element Method ◽  
Glass Bottle ◽  
Forming Process ◽  
Glass Forming ◽  
Glass Containers ◽  
Forming Temperature ◽  
Process Factors

Glass forming process is a high-volume fabrication method for producing glass containers. In this paper, the mechanisms of glass forming process were analyzed. Combined the coupled thermo-mechanical analysis with the finite element method (FEM) simulation, it was carried out to analyze the key process factors such as forming temperature, forming pressure, friction coefficient. The results show that forming pressure has the greatest influence on the stress.

Position and the Size of Drawbeads for Sheet Metal Forming with the Finite Element Method

2014 ◽  
Vol 607 ◽  
pp. 112-117
Author(s):  
Khemajit Sena ◽  
Surasith Piyasin
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Sheet Metal Forming ◽  
Metal Forming ◽  
Drawing Process ◽  
The Finite Element Method ◽  
Forming Process ◽  
Deep Drawing Process ◽  
Metal Forming Process ◽  
Element Method

This study aims to find a solution to improve the formability in a deep drawing process. For this purpose drawbeads were used to avoid wrinkles and ruptures. The finite element method was applied to simulate the 3D metal forming process using a die and drawbead. The drawbead amount, position, size and form were studied for their affects on the formability. 3 drawbead patterns with 3 different heights were examined. The simulation was performed for each drawbead pattern and each drawbead geometrical parameter and the failure elements were counted. The best pattern chosen was the pattern that resulted in the least failure elements.

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