A unified approach to multi-fixturing layout planning for thin-walled workpiece

2016 ◽  
Vol 231 (3) ◽  
pp. 454-469 ◽  
Author(s):  
Guohua Qin ◽  
Zikun Wang ◽  
Yiming Rong ◽  
Qiang Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Finite Element ◽  
Fitness Function ◽  
Machining Accuracy ◽  
Optimal Model ◽  
Prediction Ability ◽  
The Neural Network ◽  
Training Samples ◽  
Thin Walled

It is crucial to properly design the fixturing layout described by fixturing parameters, such as the fixturing sequence, the placement of clamping force, the locator position, and so on. This is because the clamping deformation of the thin-walled workpiece can influence extremely the machining accuracy and surface quality. Generally speaking, the finite element method can be used to easily obtain the deformation rule of the workpiece caused by one single fixturing parameter. But it is difficult to reveal the relationship between the multiple fixturing parameters and the clamping deformation of workpiece. Therefore, the workable finite element model of multi-fixturing layout is above all established for the thin-walled workpiece. Thus, clamping deformations can be calculated to be the training samples of the neural network. Next, according to the training samples, the prediction model is suggested for obtaining the clamping deformation from multiple fixturing parameters. When the prediction errors are defined as fitness function, the genetic algorithm is developed to search the optimal initial weights and thresholds for the neural network. The optimized neural network has better generalization and prediction ability than the non-optimized one. Ultimately, the embedded optimal model with the objective of minimizing the clamping deformation is presented for a multi-fixturing layout. When the individual fitness of each generation is constructed as a function of the clamping deformations, the genetic algorithm can be skillfully used to solve the embedded optimal model. Moreover, the experiment is conducted to validate the prediction method with good agreement between the predicted results and the experimental data. The above presented “analysis—prediction—control” method of clamping deformation not only improves the calculation efficiency of clamping deformation but also provides a basic theory of fixturing layout design for the thin-walled workpiece.

Using a Neural Network to Determine Fitness in Genetic Design

2001 ◽  
Author(s):  
Shane Farritor ◽  
Jun Zhang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Design Method ◽  
Fitness Function ◽  
Modular Robots ◽  
The Neural Network ◽  
Neural Network Structure ◽  
Algorithm Search ◽  
Human Designer

Abstract Many automated design approaches require an objective function to determine the quality of a given design. Often, this function depends on a complex relationship between many parameters. Some parameters may be subjective and the relationships difficult to quantify. This paper presents a method where a neural network is used to evaluate the quality of proposed designs during a genetic algorithm search. In general application of the approach, a human designer would propose candidate designs for a given problem. These candidate designs are used to train a neural network fitness function. Then the genetic algorithm evolves new designs that the human designer might not conceive. In this way, the proposed approach would aid in the brainstorming process. The method is applied to the genetic design of modular robots for planetary exploration. This application is briefly described and the genetic design method is summarized. Then the neural network structure is explained and the training method is detailed. Finally, the neural network is used with the genetic design method to create a robot for a specific task.

scholarly journals An unsupervised learning neural network for planning UAV full-area reconnaissance path

2021 ◽  
Vol 39 (1) ◽  
pp. 77-84
Author(s):  
Bo Li ◽  
Zhipeng Yang ◽  
Zhuoran Jia ◽  
Hao Ma
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Unsupervised Learning ◽  
Uncertain Information ◽  
Evaluation Indexes ◽  
Environment Model ◽  
Offline Learning ◽  
The Neural Network ◽  
Training Samples ◽  
Simulation Results

To plan a UAV's full-area reconnaissance path under uncertain information conditions, an unsupervised learning neural network based on the genetic algorithm is proposed. Firstly, the environment model, the UAV model and evaluation indexes are presented, and the neural network model for planning the UAV's full-area reconnaissance path is established. Because it is difficult to obtain the training samples for planning the UAV's full-area reconnaissance path, the genetic algorithm is used to optimize the unsupervised learning neural network parameters. Compared with the traditional methods, the evaluation indexes constructed in this paper do not need to specify UAV maneuver rules. The offline learning method proposed in the paper has excellent transfer performances. The simulation results show that the UAV based on the unsupervised learning neural network can plan effective full-area reconnaissance paths in the unknown environments and complete full-area reconnaissance missions.

Digital twin of equipment as a basis for the consumer in digital production

2020 ◽  
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Chip Formation ◽  
Network Models ◽  
Machining Accuracy ◽  
Neural Network Models ◽  
Digital Twin ◽  
The Neural Network ◽  
Digital Production ◽  
Cyberphysical System

The neural network models series used in the development of an aggregated digital twin of equipment as a cyber-physical system are presented. The twins of machining accuracy, chip formation and tool wear are examined in detail. On their basis, systems for stabilization of the chip formation process during cutting and diagnose of the cutting too wear are developed. Keywords cyberphysical system; neural network model of equipment; big data, digital twin of the chip formation; digital twin of the tool wear; digital twin of nanostructured coating choice

scholarly journals Optimizing the hardness of SLA printed objects by using the neural network and genetic algorithm

2019 ◽  
Vol 38 ◽  
pp. 117-124
Author(s):  
Guang Hu ◽  
Zhi Cao ◽  
Michael Hopkins ◽  
Conor Hayes ◽  
Mark Daly ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
The Neural Network

scholarly journals Safety Warning Model of Coal Face Based on FCM Fuzzy Clustering and GA-BP Neural Network

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1082
Author(s):  
Fanqiang Meng
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Early Warning ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Coal Face ◽  
Early Warning Model ◽  
Working Face ◽  
The Neural Network ◽  
Warning Model

Risk and security are two symmetric descriptions of the uncertainty of the same system. If the risk early warning is carried out in time, the security capability of the system can be improved. A safety early warning model based on fuzzy c-means clustering (FCM) and back-propagation neural network was established, and a genetic algorithm was introduced to optimize the connection weight and other properties of the neural network, so as to construct the safety early warning system of coal mining face. The system was applied in a coal face in Shandong, China, with 46 groups of data as samples. Firstly, the original data were clustered by FCM, the input space was fuzzy divided, and the samples were clustered into three categories. Then, the clustered data was used as the input of the neural network for training and prediction. The back-propagation neural network and genetic algorithm optimization neural network were trained and verified many times. The results show that the early warning model can realize the prediction and early warning of the safety condition of the working face, and the performance of the neural network model optimized by genetic algorithm is better than the traditional back-propagation artificial neural network model, with higher prediction accuracy and convergence speed. The established early warning model and method can provide reference and basis for the prediction, early warning and risk management of coal mine production safety, so as to discover the hidden danger of working face accident as soon as possible, eliminate the hidden danger in time and reduce the accident probability to the maximum extent.

The Research of Intrusion Detection Based on Intelligentization

2014 ◽  
Vol 490-491 ◽  
pp. 1588-1591
Author(s):  
Liang Zhang ◽  
Hao Yue Sun ◽  
Guo Lv ◽  
Xiao Lu Sun
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Intrusion Detection ◽  
Convergence Speed ◽  
Global Property ◽  
Detection Accuracy ◽  
Accuracy Rate ◽  
The Neural Network

In this paper, the intelligentized way is applied to detecting anomaly intrusion. Based on the global property of genetic algorithm and the locality of neural network, this method effectively improves the convergence speed of the network and the detection accuracy rate. It not only avoids the defect of the neural network, but also improves the precision.

Linear Static Response of Suspension Arm Based on Artificial Neural Network Technique

2011 ◽  
Vol 213 ◽  
pp. 419-426
Author(s):  
M.M. Rahman ◽  
Hemin M. Mohyaldeen ◽  
M.M. Noor ◽  
K. Kadirgama ◽  
Rosli A. Bakar
Keyword(s):  
Neural Network ◽  
Finite Element Analysis ◽  
Finite Element ◽  
Network Model ◽  
Neural Network Model ◽  
Mesh Size ◽  
Maximum Displacement ◽  
Element Analysis ◽  
The Neural Network ◽  
The Finite Element Analysis

Modeling and simulation are indispensable when dealing with complex engineering systems. This study deals with intelligent techniques modeling for linear response of suspension arm. The finite element analysis and Radial Basis Function Neural Network (RBFNN) technique is used to predict the response of suspension arm. The linear static analysis was performed utilizing the finite element analysis code. The neural network model has 3 inputs representing the load, mesh size and material while 4 output representing the maximum displacement, maximum Principal stress, von Mises and Tresca. Finally, regression analysis between finite element results and values predicted by the neural network model was made. It can be seen that the RBFNN proposed approach was found to be highly effective with least error in identification of stress-displacement of suspension arm. Simulated results show that RBF can be very successively used for reduction of the effort and time required to predict the stress-displacement response of suspension arm as FE methods usually deal with only a single problem for each run.

scholarly journals Forecast of Waterway Cargo Turnover Volume Based on Genetic Algorithm to Optimize Neural Network Parameters

2021 ◽  
Vol 2083 (3) ◽  
pp. 032010
Author(s):  
Rong Ma
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Forecast Model ◽  
Network Parameters ◽  
The Neural Network ◽  
Target Effect ◽  
The Empirical Analysis ◽  
Better Than

Abstract The traditional BP neural network is difficult to achieve the target effect in the prediction of waterway cargo turnover. In order to improve the accuracy of waterway cargo turnover forecast, a waterway cargo turnover forecast model was created based on genetic algorithm to optimize neural network parameters. The genetic algorithm overcomes the trap that the general iterative method easily falls into, that is, the “endless loop” phenomenon that occurs when the local minimum is small, and the calculation time is small, and the robustness is high. Using genetic algorithm optimized BP neural network to predict waterway cargo turnover, and the empirical analysis of the waterway cargo turnover forecast is carried out. The results obtained show that the neural network waterway optimized by genetic algorithm has a higher accuracy than the traditional BP neural network for predicting waterway cargo turnover, and the optimization model can long-term analysis of the characteristics of waterway cargo turnover changes shows that the prediction effect is far better than traditional neural networks.

scholarly journals PENENTUAN PARAMETER LEARNING RATE SELAMA PEMBELAJARAN JARINGAN SYARAF TIRUAN BACKPROPAGATION MENGGUNAKAN ALGORITMA GENETIKA

2020 ◽  
Vol 14 (2) ◽  
pp. 202-212
Author(s):  
Ade chandra Saputra
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Fitness Function ◽  
Learning Rate ◽  
Local Minima ◽  
Rate Parameter ◽  
Neural Network Learning ◽  
Value Of Learning ◽  
Backpropagation Learning

One of the weakness in backpropagation Artificial neural network(ANN) is being stuck in local minima. Learning rate parameter is an important parameter in order to determine how fast the ANN Learning. This research is conducted to determine a method of finding the value of learning rate parameter using a genetic algorithm when neural network learning stops and the error value is not reached the stopping criteria or has not reached the convergence. Genetic algorithm is used to determine the value of learning rate used is based on the calculation of the fitness function with the input of the ANN weights, gradient error, and bias. The calculation of the fitness function will produce an error value of each learning rate which represents each candidate solutions or individual genetic algorithms. Each individual is determined by sum of squared error value. One with the smallest SSE is the best individual. The value of learning rate has chosen will be used to continue learning so that it can lower the value of the error or speed up the learning towards convergence. The final result of this study is to provide a new solution to resolve the problem in the backpropagation learning that often have problems in determining the learning parameters. These results indicate that the method of genetic algorithms can provide a solution for backpropagation learning in order to decrease the value of SSE when learning of ANN has been static in large error conditions, or stuck in local minima

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