scholarly journals Bending Angle Prediction Model Based on BPNN-Spline in Air Bending Springback Process

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Zhefeng Guo ◽  
Wencheng Tang
Keyword(s):  
Prediction Model ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Numerical Control ◽  
Experimental Sample ◽  
Bending Angle ◽  
Air Bending ◽  
Error Back Propagation ◽  
Element Simulation ◽  
Bending Process

In order to rapidly and accurately predict the springback bending angle in V-die air bending process, a springback bending angle prediction model on the combination of error back propagation neural network and spline function (BPNN-Spline) is presented in this study. An orthogonal experimental sample set for training BPNN-Spline is obtained by finite element simulation. Through the analysis of network structure, the BPNN-Spline black box function of bending angle prediction is established, and the advantage of BPNN-Spline is discussed in comparison with traditional BPNN. The results show a close agreement with simulated and experimental results by application examples, which means that the BPNN-Spline model in this study has higher prediction accuracy and better applicable ability. Therefore, it could be adopted in a numerical control bending machine system.

Weight Loss Prediction Model for Pig Carcass Based on a Genetic Algorithm Back- Propagation Neural Network

2020 ◽  
Vol 63 (4) ◽  
pp. 1071-1077
Author(s):  
Chenyang Sun ◽  
Lusheng Chen ◽  
Yinian Li ◽  
Hao Yao ◽  
Nan Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Weight Loss ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Time Interval ◽  
Spraying Parameters ◽  
Pig Carcasses

HighlightsWe propose five spraying parameters according to the characteristics of pig carcasses in the spray-chilling process.A prediction model for pig carcass weight loss, based on a genetic algorithm back-propagation neural network, is proposed to reveal the relationship between weight loss and spraying parameters.To study the effects of various spraying parameters on weight loss, an automatic spray-chilling device was designed, which can modify up to five spraying parameters.Abstract. Because the weight loss of a pig carcass in the spray-chilling process is easily affected by the spraying frequency and duration, a prediction model for weight loss based on a genetic algorithm (GA) back-propagation (BP) neural network is proposed in this article. With three-way crossbred pig carcasses selected as the test materials, the duration and time interval of high-frequency spraying, the duration and time interval of low-frequency spraying, and the duration of a single spray were selected as inputs to the network model. The weight and threshold of the network were then optimized by the GA. The prediction model for pig carcass weight loss established by the GA BP neural network yielded a correlation coefficient of R = 0.99747 between the network output value of the test samples and the target value. Weight loss prediction by the model is feasible and allows better expression of the nonlinear relationship between weight loss and the main controlling factors. The results can be a reference for chilled meat production. Keywords: BP neural network, Genetic algorithm, Pig carcass, Predictive model, Weight loss

Free Air Ball Forming Process Parameter Analysis for Wire Bonder

Manufacturing ◽  
2002 ◽  
Author(s):  
Jau-Liang Chen ◽  
Hsu-Yang Chang
Keyword(s):  
Optimal Parameter ◽  
Back Propagation ◽  
Tail Length ◽  
Back Propagation Neural Network ◽  
Parameter Analysis ◽  
Forming Process ◽  
Current Time ◽  
Ball Size ◽  
Error Back Propagation ◽  
Free Air

In this paper, we are focusing on the FAB forming technology for fine pitch wire bonding. The parameters that affect the FAB formation include: 1) tail length; 2) spark gap; 3) Electric flame-off (EFO) voltage, current, time; 4) relative position between electrode plate and tail; 5) wire material; and 6) type of capillary. Except the last two items, all the other parameters can be quantified for analysis. By using Taguchi method it was found that EFO time and EFO current are the most important parameters that affect the formation free-air ball. The error-back-propagation neural network was then used to predict suitable EFO time and current setting. The main objective in this research is to find a suitable rule for parameters setting in order to control the FAB ball size as required. The result can be used in the future for optimal parameter setting and prediction of FAB formation.

scholarly journals Method of Predicting Ore Dilution Based on a Neural Network and Its Application

2020 ◽  
Vol 12 (4) ◽  
pp. 1550 ◽  
Author(s):  
Xingdong Zhao ◽  
Jia’an Niu
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Relative Error ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Average Deviation ◽  
Graph Method ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
The Difference

A back-propagation neural network prediction model with three layers and six neurons in the hidden layer is established to overcome the limitation of the equivalent linear overbreak slough (ELOS) empirical graph method in estimating unplanned ore dilution. The modified stability number, hydraulic radius, average deviation of the borehole, and powder factor are taken as input variables and the ELOS of quantified unplanned ore dilution as the output variable. The training and testing of the model are performed using 120 sets of data. The average fitting degree r2 of the prediction model is 0.9761, the average mean square error is 0.0001, and the relative error of the prediction is approximately 6.2%. A method of calculating the unplanned ore dilution is proposed and applied to a test stope of the Sandaoqiao lead–zinc mine. The calculated unplanned ore dilution is 0.717 m, and the relative error (i.e., the difference between calculation and measurement of 0.70 m) is 2.4%, which is better than the relative errors for the empirical graph method and numerical simulation (giving dilution values of 0.8 and 0.55 m, respectively). The back-propagation neural network prediction model is confirmed to predict the unplanned ore dilution in real applications.

On the Identification of a Loading Scheme in Large Radius Air Bending

2015 ◽  
Vol 639 ◽  
pp. 155-162 ◽  
Author(s):  
Vitalii Vorkov ◽  
Richard Aerens ◽  
Dirk Vandepitte ◽  
Joost R. Duflou
Keyword(s):  
High Strength ◽  
Material Behavior ◽  
Large Radius ◽  
Element Analysis ◽  
Camera System ◽  
Bending Angle ◽  
Air Bending ◽  
Contact Points ◽  
Bending Process ◽  
Loading Diagram

Large radius air bending has a different loading diagram than conventional bending, which affects the material behavior during the bending process. In order to establish a correct loading diagram, the position of the contact points between the plate and the punch is determinant. The position of the contact points is depending on the evolution of the bending process and the influence of the material is unknown. In this work, the determination of the position of the contact points in large radius air bending has been studied by means of both an experimental campaign and finite element analysis. Experiments were performed on a press-brake with a capacity of 50 metric tons. High-strength steel Weldox 1300 and aluminum alloy AlMg3, and punches of radii 30, 35 and 40 mm have been used. During the bending process, the punch movement has been monitored and the bending angle has been measured by means of images recorded by a camera system. Based on the obtained results, the relation between the bending angle and the position of the contact points is discussed.

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