Bone Scaffold Forming Filament Width Prediction of LDM Based on the Improved BP Neural Network

LDM process is used for preparing three-dimensional scaffolds for tissue engineering rapid prototyping technologies. Because of its forming process is complex, which influenced by a variety of factors, so the processing environment is not stable, the forming of scaffold pore size can not be guaranteed, therefore the forming precision is poor. However, the scaffold pore size accuracy is mainly decided by the wire filament width. Neural network theory and development provides a powerful tool for the study of nonlinear systems. This article analyzed the influence factors for forming bone scaffold filament width of LDM process, based on improved BP neural network, using MATLAB software programming, then predicted the filament width. The results show that model prediction error was less than 8%, it has high forecasting precision, and it can be used to guide the LDM process parameter selection and forming precision of prediction.

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Research on Error Model of the Al5052 Sheet Stamping Forming Parts

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.667.149 ◽

2015 ◽

Vol 667 ◽

pp. 149-154

Author(s):

Li Zhi Gu ◽

Yun Ma ◽

Qian Deng

Keyword(s):

Neural Network ◽

Bp Neural Network ◽

Influence Factors ◽

Paper Analysis ◽

Alloy Sheet ◽

Forming Process ◽

Sheet Stamping ◽

Forming Precision ◽

Main Factors ◽

Main Influence

Abstract. How to improve the precision of the stamping forming has been one of the stamping researchers concern in the stamping technology.This paper analysis the material stamping performance of AL5052 aluminum alloy sheet,study the influencing factors of forming precision,and find the the mapping relationship between influence factors and stamping forming to effectively predict the forming error .The first part is to analyze the factors that affect the forming process, then get the main factors that influence the part of stamping forming. And then establish the main influence factors’ mathematical model of stamping forming error based on BP neural network, and through the training of BP neural network to prove the model’s practicability.

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BP Neural Network Model on Choice of Project Manager for Highway Slope Treatment

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.505-506.274 ◽

2014 ◽

Vol 505-506 ◽

pp. 274-277

Author(s):

Bin Wang ◽

Yong Tao Gao

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Neural Network Model ◽

Bp Neural Network ◽

Network Theory ◽

Influence Factors ◽

Project Manager ◽

Slope Treatment ◽

Neural Network Theory ◽

Bp Neural Network Model

To get the quantified indexes of comprehensive capacity about project manager, based on the modal on artificial neural network theory, different influence factors about choice of project manager for highway slope treatment were analyzed , identified, quantified and evaluated , then comprehensive capacity of the manager were analyzed. Such procedure provided a new method for choice of project manager for highway slope treatment.

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Hull Plate Bending Springback Prediction Based on Artificial Neural Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.988.309 ◽

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.

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Study on the Generalized Holo-Factors Mathematical Model of Dimension-Error and Shape-Error for Sheet Metal in Stamping Based on the Back Propagation (BP) Neural Network

Journal of Manufacturing Science and Engineering ◽

10.1115/1.4033156 ◽

2016 ◽

Vol 138 (6) ◽

Cited By ~ 4

Author(s):

Lizhi Gu ◽

Tianqing Zheng

Keyword(s):

Neural Network ◽

Mathematical Model ◽

Sheet Metal ◽

Bp Neural Network ◽

Back Propagation ◽

Shape Error ◽

Back Propagation Algorithm ◽

Minor Strain ◽

Major Strain ◽

Forming Precision

Precision improvement in sheet metal stamping has been the concern that the stamping researchers have engaged in. In order to improve the forming precision of sheet metal in stamping, this paper devoted to establish the generalized holo-factors mathematical model of dimension-error and shape-error for sheet metal in stamping based on BP neural network. Factors influencing the forming precision of stamping sheet metal were divided, altogether ten factors, and the generalized holo-factors mathematical model of dimension-error and shape-error for sheet metal in stamping was established using the back-propagation algorithm of error based on BP neural network. The undetermined coefficients of the model previously established were soluble according to the simulation data of sheet punching combined with the specific shape based on the BP neural network. With this mathematical model, the forecast data compared with the validate data could be obtained, so as to verify the fine practicability that the previously established mathematical model had, and then, it was shown that the generalized holo-factors mathematical model of size error and shape-error had fine practicality and versatility. Based on the generalized holo-factors mathematical model of error exemplified by the cylindrical parts, a group of process parameters could be selected, in which forming thickness was between 0.713 mm and 1.335 mm, major strain was between 0.085 and 0.519, and minor strain was between −0.596 and 0.319 from the generalized holo-factors mathematical model prediction, at the same time, the forming thickness, the major strain, and the minor strain were in good condition.

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Research on Applied-Information Technology with PM2.5 Generation and Evolution Model Based on BP Neural Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1003.226 ◽

2014 ◽

Vol 1003 ◽

pp. 226-229 ◽

Cited By ~ 1

Author(s):

Ying Hong Xie ◽

Xiao Wei Han ◽

Qi Li

Keyword(s):

Neural Network ◽

Information Technology ◽

Neural Network Model ◽

Bp Neural Network ◽

Influence Factors ◽

Evolution Model ◽

Good Reliability ◽

Bp Neural Network Model ◽

Near Future ◽

Xi’An City

In this paper, BP neural network model is used to establish the occurrence and evolution model of PM2.5 in an area in Xi'an city. In the model, wind, humidity, season, SO2,NO2,PM10, CO,O3 (in one hour ) and O3 (in eight hours ) and other influence factors are all considered. The model has good reliability, it can accurately forecast the value of PM2.5 and its variation in the near future, which can provide the basis for the PM2.5 control.

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Prediction Model of Iron Ore Pellet Ambient Strength and Sensitivity Analysis on the Influence Factors

Metals ◽

10.3390/met8080593 ◽

2018 ◽

Vol 8 (8) ◽

pp. 593 ◽

Cited By ~ 1

Author(s):

Qiangjian Gao ◽

Yingyi Zhang ◽

Xin Jiang ◽

Haiyan Zheng ◽

Fengman Shen

Keyword(s):

Neural Network ◽

Sensitivity Analysis ◽

Prediction Model ◽

Bp Neural Network ◽

Back Propagation ◽

Influence Factors ◽

Principal Component ◽

Bp Network ◽

Input Variables ◽

Artificial Neural Network Ann

The Ambient Compressive Strength (CS) of pellets, influenced by several factors, is regarded as a criterion to assess pellets during metallurgical processes. A prediction model based on Artificial Neural Network (ANN) was proposed in order to provide a reliable and economic control strategy for CS in pellet production and to forecast and control pellet CS. The dimensionality of 19 influence factors of CS was considered and reduced by Principal Component Analysis (PCA). The PCA variables were then used as the input variables for the Back Propagation (BP) neural network, which was upgraded by Genetic Algorithm (GA), with CS as the output variable. After training and testing with production data, the PCA-GA-BP neural network was established. Additionally, the sensitivity analysis of input variables was calculated to obtain a detailed influence on pellet CS. It has been found that prediction accuracy of the PCA-GA-BP network mentioned here is 96.4%, indicating that the ANN network is effective to predict CS in the pelletizing process.

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Transient Power Quality Recognition Based on BP Neural Network Theory

Energy Procedia ◽

10.1016/j.egypro.2012.01.220 ◽

2012 ◽

Vol 16 ◽

pp. 1386-1392 ◽

Cited By ~ 4

Author(s):

Xu Tongyu ◽

Zheng wei ◽

Sun Peng ◽

Zhang Qin

Keyword(s):

Neural Network ◽

Power Quality ◽

Bp Neural Network ◽

Network Theory ◽

Neural Network Theory ◽

Transient Power Quality

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Creep Behavior Prediction Research of T92 Steel Based on BP Neural Network

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.467.203 ◽

2013 ◽

Vol 467 ◽

pp. 203-207

Author(s):

Jian Liu

Keyword(s):

Neural Network ◽

Creep Behavior ◽

Bp Neural Network ◽

Network Theory ◽

Experimental Results ◽

Behavior Prediction ◽

Forecast Precision ◽

Network Method ◽

Simulation Results ◽

Neural Network Theory

Based on the BP neural network theory, the creep rate prediction model of T92 steel was established under multiple stress levels. Obtained the experimental results and using the model, the experimental results were trained. The results show that the simulation results match the measured results well with a high forecast precision. The BP neural network method can serve as research on T92 steel creep behavior.

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Design and Fabrication of Customised Scaffold for Femur Bone Using 3D Printing

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.845.920 ◽

2013 ◽

Vol 845 ◽

pp. 920-924

Author(s):

V. Iraimudi ◽

S. Rashia Begum ◽

G. Arumaikkannu ◽

R. Narayanan

Keyword(s):

3D Printing ◽

Pore Size ◽

Three Dimensional ◽

Unit Cell ◽

Bone Scaffold ◽

Software Patterns ◽

3D Cad ◽

Bone Scaffolds ◽

Femur Bone ◽

Scan Data

Additive Manufacturing is a promising field for making three dimensional scaffolds in which parts are fabricated directly from the 3D CAD model. This paper presents, the patients CT scan data of femur bone in DICOM format is exported into MIMICS software to stack 2D scan data into 3D model. Four layers of femur bone were selected for creation of customised femur bone scaffold. Unit cell designs such as double bend curve, S bend curve, U bend curve and steps were designed using SOLIDWORKS software. Basic primitives namely square, hexagon and octagon primitives of pore size 0.6mm, 0.7 mm and 0.8 mm diameter and inter distance 0.7mm, 0.8mm and 0.9 mm are used to design the scaffold structures. In 3matic software, patterns were developed by using the above four unit cells. Then, the four layers of bone and patterns were imported into 3matic to create customised bone scaffolds. The porosities of customised femur bone scaffold were determined using the MIMICS software. It was found that the customised femur bone scaffolds for the unit cell design of U bend curve with square primitives of pore size 0.8mm diameter and inter distance 0.7mm gives higher porosity of 56.58 % compared to other scaffolds. The models were then fabricated using 3D printing technique.

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